TOWARD A UNIFIED HUMAN BEHAVIOUR MODELLING ... - CiteSeerX

TOWARD A UNIFIED HUMAN BEHAVIOUR MODELLING APPROACH KRISTINA YORDANOVA

Informatik Preprint CS-02-11 University of Rostock, Institute of Computer Science May 2011 Abstract. Modelling human behaviour could play an essential role in systems dealing with user monitoring or assistance. At present, there are variety of human behaviour models describing behaviour from different activity domains. However, there is no unified modelling approach that can be applied across multiple domains. To solve this problem, we derive the requirements that such modelling approach should satisfy in order to capture the dynamics of behaviours from different domains. Additionally, we discuss the aspects according to which a model can be categorized. Furthermore, we perform a case study with 19 human behaviour models where we attempt to identify the requirements they satisfy. Finally, we discuss the model that is the most probable candidate to be used in a unified human behaviour modelling approach.

1. Introduction The understanding of human behaviour plays an important role in simulations, games, and human-computer interaction systems. To create an adequate agent player in a computer game, it is significant to be able to construct a realistic human-like behaviour, otherwise the agent would look too artificial to the opponent human player. The same applies for the different military and civil simulators where people are trained to react adequately in stressful real world situations, to make surgical operations, to navigate a plane. The understanding of human behaviour is also extremely important in ubiquitous systems where intelligent appliances strive to infer human actions and intentions in order to offer better interaction and assistance. In this case, incorrect action recognition could lead to unwanted system performance. To solve these problems, different human behaviour models are developed and applied to various domains. 1.1. Reasons for describing human behaviour. Before discussing the different ways of modelling human behaviour, we have to answer one important question: Why do we need to describe human behaviour at all? Objectively speaking, it is possible to create human-computer interaction without using explicit human behaviour models. There is an increasing number of works in the field of activity recognition that are based only on the observed sensor data, and which try to 1

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recognize human activities with the help of different statistical methods. Many of these works give promising results that encourage the development of this field of research. However, there are several reasons for describing human behaviour, that make its modelling important. From psychological point of view, human behaviour modelling is essential for the better understanding of human actions. Questions such as: ”what does an action consist of?”; ”what does this action implies?”; ”what are the reasons and consequences of an action?” arise and their answers could be found exactly in a human behaviour models that describe the smallest actions as well as their relations within the context of complex activities and composition of activities. Another reason for modelling human behaviour is to detect a specific behaviour based on given activity. This aspect of HBM is important in systems that deal with human monitoring and rise questions such as: ”if an activity is recognized, what kind of behaviour does it imply?”; ”is the behaviour normal or abnormal?”; ”should the activity be reported as deviation from the expected?”. These systems could be in the sphere of health care where the condition of patients is monitored, or in the sphere of security where abnormal behaviour could imply intrusion. Yet another reason for describing human behaviour is to provide assistance. In this case HBM is essential for systems that try to assist their users in accomplishing a goal. Here when an action is detected and recognised, it is important to discover not only what the action implies, but also why it is executed, what is the end goal of the user. In that way the system will be able to assist the user in reaching her goal. All the above situations require human behaviour models in order to cope with their objectives which makes human behaviour modelling important aspect of systems regarding human psychology, health care, security or smart appliances. 1.2. Purpose of human behaviour models. Although all of them model human behaviour, human behaviour models can serve different purposes. Many of them are used just for simulation of human behaviour where a particular behaviour path is constructed independently of its occurrence probability. With the rapid development of more and more realistic human-centered games, simulation of human behaviour is thoroughly investigated and different models striving to improve the simulation realism are developed. Another reason for deriving human behaviour models is to detect a specific behaviour. This is called prediction and the idea behind it is to find the most probable behaviour from a set of behaviours. In difference with simulation, models dealing with prediction usually assign a probability function to all possible behaviours, instead of just giving one solution. If we go even further beyond simulation, models are used for inference. If the purpose is inference, the model not only tries to predict the most probable human behaviour, but also to infer the reasons behind this behaviour, and if possible, to discover the long term human behaviour. Having this in mind, we distinguish three main purposes for human behaviour modelling. : Simulation Simulation is the process of imitating a real world situation or behaviour. Simulation does not take into account how probable the execution sequence is, it just gives

TOWARD A UNIFIED HUMAN BEHAVIOUR MODELLING APPROACH

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Figure 1. Purposes of human behaviour models X: Y: Simulation

Prediction

Filtering

Smoothing

t=0

t

t+s

T

a sample of a future trajectory. In simulation it is not possible to judge which is the best course of action, because the different samples are equally probable. Fig. 1 graphically shows the difference between the different behaviour purposes, where simulation is presented simply by a state sequence X1:t+s which is not affected by the probability distribution of the observed states. : Prediction In difference with simulation, prediction defines a probability distribution across an action space. Here we not only have a sample of a future trajectory, but also the probability of this action sequence happening. Prediction is extremely helpful for making decision about which action to support and which to discard. In Fig. 1 prediction is described as the probability of having the state Xt+s , given the observations Y1:t , or shortly P (Xt+s |Yt ). : Inference Beyond prediction there is inference where we go one step further and try not only to predict future behaviour, but also to find the reasons behind this behaviour. When talking about inference, we distinguish 4 different approaches of interest. • smoothing Often the sensor datasets contain not only useful information but also a lot of noise. To avoid the redundant data, smoothing is employed. Smoothing is the process where an approximation of the original data is obtained, that tries to catch the information patterns but to leave out the noise or other fine-scale structures. Fig. 1 shows smoothing as the process where the state Xt at time t is estimated, taking into account all observations YT up to time T, or in other words P (Xt |YT ).

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• filtering Like in smoothing, filtering also performs a transformation of the original data to approximated copy of it that is reduced of noise. However, while in smoothing the process is performed over the whole dataset, in filtering an online learning is performed and the smoothing is done over only parts of observations. In Fig. 1 filtering is described as the process of estimating the state Xt at time t, taking into account only the observations Yt up to time t, namely P (Xt |Yt ). • parameter estimation To completely specify a model, it is necessary to define the parameters that explain and capture the behaviour of the observed data. For that the parameter space has to be defined, that includes all parameters needed to fully describe the model. It is possible that some of the parameters are hidden and have to be estimated. For that purpose the sensor data is used. It is also possible that the model can be correctly described with more than one set of parameters. • structure learning Often the structure of the model is defined by a system expert. However, in the case where this task is too complex for humans, a structure learning is used. In the latter case the structure and the parameters of the model are learned from the sensor data. 1.3. Unified human behaviour modelling approach. In the recent years there is a growing interest in the field of human behaviour modelling and a great part of it is based on the interest in activity recognition where more and more systems able to recognize human activities are emerging. Their usage can vary from daily living activities, elderly care, surveillance and outdoor activities, to smart rooms able to assist participants in a meeting. Such systems can be invaluable assets to a vast number of social programs aiming to make the smart appliances part of the environment and the everyday human interaction with them easier and unnoticeable. If we go even further, a system able to detect the user’s behaviour and infer her intention, would easily assist her in her interaction with any number of electronic devices without any effort from the human part. Or in another scenario, it could help the monitoring of elderly or ill people and make the work of a social care specialist much easier. However, the different use cases imply different sensor data and different models that cannot be used in scenarios varying from the one, they were build for. A unified human behaviour model that can take into account the prior knowledge and predict activities from different domains could solve this problem and enable the usage of one general model for various activity fields. Unfortunately, there is not much research on this particular topic. Thus in this work different existing human behaviour models are investigated and the requirements for a high level human behaviour model are defined. This is the first step in building a unified approach that will later be continued by selecting the most suitable models and by investigating a way to combine them in one model. In that way we hope to achieve a modelling


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approach with better performance. When doing that, the different modelling aspects have to be considered. Some models are presented in a process-based view (see Subsection 2.1) while other – in a causal view (see Subsection 2.2). Both modelling aspects have their advantages, thus it is important to find a way to combine them, so that the best from both views can be used. One possible way of doing that is shown in Fig. 2. Here we would like to map a high level process based human models into low level causal models, which then can be mapped into probabilistic dynamic models. If we are able to achieve that, we would like to investigate if it is possible to reverse the process – namely, to map these probabilistic dynamic models back to the high-level process models.

Figure 2. Modelling aspects

Process-based view

Causal view

Probabilistic dynamic models

1.4. Outline. The rest of this work is structured as follows. Section 2 describes the different modelling aspects that we consider, namely process-based and state-based modelling views. Section 3 deals with the different kinds of semantics in the field of modelling. These are denotational, operational and axiomatic semantics. Section 4 provides short descriptions of the different programming paradigms with which the human behaviour models can be characterised, namely imperative and declarative programing. We also discuss the need of prior knowledge for improving the model performance (Section 5) and the requirements that a unified human behaviour model should satisfy (Section 6). Section 7 shows a case study of 19 HBM , their usage and characteristics; it also lists the requirements they meet and the type of prior knowledge and purpose they have. Finally, in Section 8 we discuss the results from the case study in Section 7 and the most probable candidate for a unified human behaviour modelling approach.

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2. Modelling Aspects All human behaviour models strive to catch the dynamics of human behaviour, yet this does not mean they have the same structure or approach. On the contrary, some of them differer in everything but their purpose to model human behaviour. One of the aspects in which they differ is the modelling view. Some of them follow a step by step description of the behaviour process, where it is explicitly defined which path to follow in order to achieve a goal. Other models have clearly defined action preconditions and effects but do not explicitly specify the way in which these effects should be achieved. Bellow we discuss these two modelling aspects and try to explain their differences. Additionally, some models are expressed by a set of actions and relations between them, while other consist of a set of states with transitions, explaining their relations. 2.1. Process-based models. When we think of a process, we usually understand the act of executing a set of routine procedures in order to achieve a goal. Beaten [9] describes a process as ”behaviour of a system. A system is anything showing behaviour, in particular the execution of a software system, the actions of a machine or even the actions of a human being. Behaviour is the total of events or actions that a system can perform, the order in which they can be executed and maybe other aspects of this execution such as timing or probabilities. Always, we describe certain aspects of behaviour, disregarding other aspects, so we are considering an abstraction or idealization of the real behaviour. Rather, we can say that we have an observation of behaviour, and an action is the chosen unit of observation”. Thus, we can consider a process as the set of actions with transitions between them that describe the change of behaviour. In this context, a process-based model is a model that describes activities through set of actions with transitions that lead from one action to another in order to achieve a goal. When talking about human behaviour, a process-based model represents it as a composite structure of activities with transitions defining the next activity. Thus, when describing how to reach a goal, the process-based model will go through several activities until reaching the desired state. Considering process-based models from another perspective, we can think of them as models answering the question what is a user doing. There are two different model approaches concerning the process-based modelling view. These are grammar-based models, where the human behaviour is described in the form of grammar and rules; and process calculi which represents a diverse family of related approaches for modelling of concurrent systems. 2.1.1. Grammar-based models. A grammar-based model constructs a grammar that describes the human behaviour. In their book ”Artificial Intelligence A Modern Approach” [43], Russell and Norvig define a grammar as ”a finite set of rules that specifies a language. Formal languages always have an official grammar, specified in manuals or books. Natural languages have no official grammar, but linguists strive to discover properties of the language by a process of scientific inquiry and then to codify their discoveries in a grammar”. Bernard Meyer [36] gives another definition by explaining that a grammar ”defines the


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syntax of a language as a set of productions. Each production specifies one construct by describing the structure of specimens of the construct.” In the context of human behaviour modelling, a grammar-based model describes behaviour in the sense of constructions of rules that define the dynamics of the activities constituting a behaviour. 2.1.2. Process calculi. Process calculi are various approaches for modelling concurrent systems. They provide a tool for describing high-level interactions, communication and synchronization between different agents or processes. It is also possible to use them for comparing and analysing independent processes. Although there are different types of process calculus, all of them share the same features. They represent interaction between independent processes as communication; they use a set of primitives and operators combining these primitives to describe the processes; they define algebraic laws for the process operators; they use equation reasoning to manipulate process expressions. Beaten describes process algebra as ”the study of the behaviour of parallel or distributed systems by algebraic means. It offers means to describe or specify such systems, and thus it has means to talk about parallel composition. Besides this, it can usually also talk about alternative composition (choice) and sequential composition (sequencing). Moreover, we can reason about such systems using algebra, i.e. equational reasoning. By means of this equational reasoning, we can do verification, i.e. we can establish that a system satisfies a certain property” [9]. 2.2. Causal (state-based) models. Causality can be described as the relationship between two events – the first being the cause, and the second – the effect that resulted from the first. In his book ”Natural Philosophy of Cause and Chance” Born [12] explains that ”Causality postulates that there are laws by which the occurrence of an entity B of a certain class depends on the occurrence of an entity A of another class, where the word ’entity’ means any physical object, phenomenon, situation, or event. A is called the cause, B the effect.” Causality is the basic idea behind causal models. In difference with process-based models, causal models do not specify a set of actions with which a goal can be achieved, but rather define the precondition for reaching it, the effects after the goal has been reached and a set of states through which one should go in order to reach the goal state. Pearl gives the following formal definition of a causal model. ”A causal model is a pair M =< D, ΘD > consisting of a causal structure D and a set of parameters ΘD compatible with D. The parameters ΘD assign a function xi = fi (pai , ui ) to each Xi ∈ V and a probability measure P (ui ) to each ui , where P Ai are the parents of Xi in D and where each Ui is a random disturbance distributed according to P (ui ), independently of all other u” [39]. In difference with process-based models which answer the question ”what”, causal models deal with the problem of why a user is doing something, thus investigating the cause and effects of a given action sequence. Here, when talking about causal models, we consider two different types: forward rulebased models and backward rule-based models. Russell and Norvig [43] explain that

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”Rule-based systems emerged from early work on practical and intuitive systems for logical inference” and that they possess three useful properties: locality, detachment and truth-functionality. Bellow we explain the differences between the forward and backward rule-based systems and describe their properties. 2.2.1. Forward rule-based models. The basic idea behind the forward rule-based models is that they deal with rules and facts. First rules and facts are defined and if specific facts are true, they can make a certain rule applicable. When a rule becomes applicable, it is asserted. In difference with the process-based models, where an explicit process describes the system behaviour, the rule-based models continuously apply a collection of rules to a collection of facts. Rules can modify the collection of facts. If we take for example a production system, which is a forward rule-based system, it provides a mechanism necessary to execute productions in order to achieve some goal of the system. It basically consists of two steps: the first is the prediction step or IF statement; and the second is the action step or THEN statement. This means that if the production’s prediction matches the current state of the world, the production is triggered and a production’s action is executed. 2.2.2. Backward rule-based models. In difference with the forward rule-based systems, where an action is triggered only if a fact is true, the backward rule-based systems use approach called backtracking. As described in [21] backtracking ”systematically searches for a solution to a problem among all available options. It does so by assuming that the solutions are represented by vectors (v1 , ..., vm ) of values and by traversing, in a depth first manner, the domains of the vectors until the solutions are found.” In the context of backward rule-based models, this means that given a problem, the algorithm goes through the present states and their relations and tries to find a solution. If any goal fails in the course of executing the algorithm, all state bindings that were made since the most recent choice-point are undone and the execution continues with the next alternative of the choice point. 3. Formal Semantics Every language, formal or natural, consists of syntax and semantics. While the syntax deals with the properties of a language, the semantics ”attempts to give the language an interpretation, and to supply a meaning, or value, to the expressions, programs etc. in the language” [46]. Although the syntax provides the rules to make a system grammatically correct, it does not give a meaning to this system. Especially when dealing with a real time system, we have to give it semantics in order to make it consistent. In the context of human behaviour modelling, the different kinds of semantics divide the models into two main groups – the first having a clear execution structure; while the second consists of models that have a clear goal but not exact execution sequence. There are three main methods for defining semantic description of a language. These are operational, denotational and axiomatic approach. Bellow we shortly describe these


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three methods and concentrate on the first two as the axiomatic approach is not relevant to our purposes. 3.1. Operational semantics. The idea behind the operational approach to semantics description is to define an abstract machine which has a state with several components and a set of primitive instructions. The machine is defined by specifying the way in which the components of the state are changed by each of the instructions. Then the semantics of the particular language are defined in terms of these changes. ”The semantic description of the programming language specifies a translation into this code. Once this code is understood, one merely has to trace through the translated program step by step to determine its exact effect” [46]. The operational semantics can be thought of as interpreter that express the semantics of a language by the means of a mechanism that makes it possible to determine the effects of a program in the language. It gives a concrete, intuitive description of the used language. However, as Meyer explains, ”The very qualities of operational method,..., also speak of its limitations. Striving to be executable, operational descriptions lose one of the essential qualities of specifications: independence from the implementation. What an operational description specifies is one particular way to execute programs” [36]. The operational semantics specify a concrete sequence of states through which a process must go, thus running into the problem of being over-constraining. 3.2. Denotational semantics. The denotational approach expresses the semantics of a language in terms of translation schema where a meaning (denotation) is associated with each program in a language. Stoy describes denotational semantics in the following way: ”We give ”semantic valuation functions”, which map syntactic constructs in the program to the abstract values (numbers, truth values, functions etc.) which they denote. These valuation functions are usually recursively defined: the value denoted by a construct is specified in terms of the values denoted by its syntactic subcomponents, and it is this emphasis on the values denoted by all these constructs that gives the approach its name.There may or may not be an obvious way of working out the results if these functions in any particular case: that is, the defining equations may or may not suggest a way of implementing the language” [46]. The denotational method reaches a level of abstraction which cannot be obtained in the operational approach, no matter how abstract the chosen automata is. This is due to the fact that denotational semantics have program elements as their arguments independent of any data. This indicated that a state will be less visible and more abstract than the state in the operational semantics. 3.3. Axiomatic semantics. The axiomatic approach regards semantics of a language as a theory of the programs written in that language. It is a mathematical theory for the language that expresses statements about programs written in this language and proves or disproves such statements. As Stoy explains, ”We associate an ”axiom” with each kind of statement in the programming language, which states what we may assert after execution of that statement in terms of what was true beforehand” [46].

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The axiomatic approach is not in the scope of our interest, as we are not interested in proving the correctness of the model statements, but rather finding a way in which to structure the existing models. Thus the axiomatic approach will not be regarded further in this work. 4. Programming Paradigm Programming paradigms are fundamental styles of computer programming that differ in the concepts and abstraction representing a program, as well as in the steps that compose a computation. There are several different programming paradigms, however in the scope of this work we will consider only two of them, namely the imperative and declarative programming paradigms as opposing to each other programming styles. Like in the previous Section 3 we consider these two programming paradigms in order to find a structure that can be used for ordering the HBM. The imperative and declarative programming paradigms allow us to structure HBM in a way similar to the one from formal semantics. Thus, bellow we discuss the differences between the two programming paradigms. 4.1. Imperative programming. Imperative programming is the oldest of the high-level programming paradigms and represents computation in terms of statements that change a program style. It imperatively describes the steps that have to be taken in order a desired state to be achieved. Imperative programming is the dominant programing paradigm not only because it has existed longer, but also because programs describe real-world processes with real-world objects with varying states. The imperative programming provides an intuitive model of these processes by providing variables that model such objects and programs that model such processes. In the context of modelling, the idea behind imperative programming is to express behaviour as a set of states that follow a set of instructions leading to the goal state. 4.2. Declarative programming. Declarative programming, in difference with imperative programming, expresses the logic of a computation without explicitly describing its control flow. The idea of declarative programming is to minimize side effects by describing what the program is supposed to accomplish without explaining how exactly to accomplish it. It often considers programs as theories of formal logic, and computations as deductions in that logic. In the context of modelling, declarative programming is similar to denotational semantics where we may or may not have a specific way of reaching desired state. 5. Prior Knowledge Let us consider a system that tries to infer human intentions by employing sensor data. There are two popular approaches to achieving this. The first is to search for patterns in the sensor data with the help of statistical methods without any additional knowledge of the nature of these patterns. The second approach is to use an already created model of the human activities, build on the basis of the knowledge of these activities, and try to


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infer the intentions. Both methods could be successful in their task, so the question of whether we really need to make use of any prior knowledge arises.

5.1. What is prior knowledge and do we need it? Prior knowledge is the knowledge we have prior to the present moment. In the context of intention recognition, this will be the knowledge of human activities that we have prior to receiving the sensor data from which the intentions have to be inferred. Additionally, it contains the information about the domain and the current state of the world. When creating a human behaviour model, this knowledge will be incorporated into the model in order to improve the process of activity recognition. However, in the recent years there is increasing interest in using statistical methods for activity recognition. Thus the question of prior knowledge’s importance arises. Do we really need it or can we rely only on the sensor data? If we consider situation where the sensor data describes human behaviour in a particular domain and is collected with the same type of sensors, then a pattern extraction methods could be sufficient for learning the system to recognise future human activities. Especially, having in mind that humans are creatures of habit and exhibit certain behaviour patterns. However, even changing the sensors type could be a problem for recognising the activity patterns. Even worse, a change in the domain would make activity recognition more difficult if not hardly possible. The reason for this is that by using only sensor data, a learned model is highly dependent on the observed data, so it will be difficult to use it in a different from the observed situation. On the other hand, a human behaviour model making use of the prior knowledge could be more abstract and flexible, so that it can be used in different domains by utilising different types of domain knowledge. It is possible to build an abstract model representing generic behaviours that are valid for different activity domains and when applying the model for a specific situation make use of the domain knowledge to infer human intentions. Another problem that the use of prior knowledge solves, is arriving at a local maxima. Geisler gives the following example with a first person shooter in Quake: when relying only on observation data to learn, it is possible that there is shooting only in 5% of the observations. Relying only on these observations the shooter learns not to shoot, thus arriving at a undesirable local maxima [6]. This drawback can be solved exactly by applying prior knowledge from which the agent knows that this is not exactly what he is supposed to learn. In the recent years the sensor data to be analysed is increasing until we have come to the point where we have huge amount of observations and the process of analysing it becomes tedious and slow. A way to avoid this problem could be to employ prior knowledge which will reduce the amount of sensor data to be processed only to that which is important for the specific situation. Prior knowledge can be extremely important for creating a uniform human behaviour model, because if the model is abstracted from the various domains, the prior knowledge will be the key for applying this model to different situations. In difference with a model

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built from a sensor data, this one will not be so dependent on observations, thus it will be more successful when applied to more than one domains. This means that prior knowledge can be avoided in specific situations and only the sensor data be used for model learning. However, for applying such model on a broader spectrum of activity situations, as well as avoiding arriving at a local maxima, a prior knowledge will inevitably be necessary to solve the arising problems. 5.2. Types of prior knowledge. There are several types of prior knowledge that we consider in the context of human behaviour modelling. 5.2.1. Prior knowledge based on cognitive psychology. Cognitive psychology is the study that explores internal mental processes. Thus prior knowledge based on cognitive psychology will consist of all the internal human states such as stress, emotions, perceptions etc. Such type of knowledge is important because cognition greatly affects human behaviour, and it is important to understand and take into account its influence of a human behaviour model. 5.2.2. Environmental knowledge. Environment is everything that surrounds a system and that exchanges different properties with it. In the context of human behaviour modelling, prior knowledge based on the environment will be knowledge about the state of the world outside the system. Such knowledge is important, because it can be essential for determining different domains that may affect the way an activity is executed but still refer to the same activity. 5.2.3. Prior knowledge based on ergonomics. Ergonomics is the study that concerns the understanding of the interaction between human and other elements of a system and that strives to optimize human well-being and overall performance. Such type of prior knowledge is important, because it may contain important behavioral patterns that will make the recognition of a human activity easier. 6. Requirements for Human Behaviour Models Let us consider human behaviour as a complex system consisting of a set of elements with relations between them having different properties. If we want to model such system, there are certain requirements that the modelling language has to meet. The behaviour being the entire system, we would like to decompose it into smaller elements until we reach its building blocks. Thus the modelling language should posses the property hierarchy where the complex behaviour will be decomposed into sub-behaviours until the atomic action constants are reached. Additionally, a behaviour can be regarded as a complex system composed of several smaller pieces that are executed either sequentially, in parallel, or are interleaving. Thus we need to be able to express the property composition. The first possible property of the elements composing a behaviour, is the ability to sequentially execute actions that lead to the goal state. Thus, to model this, the language should be able to express sequences. However, sometimes it happens that a person is doing two things simultaneously, like for example watching TV and talking on the phone. To express such


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situation our modelling language should possess the property parallelism. Finally, if the behaviour is composed of interleaving activities that overlap, we should be able to express this in our model. Sometimes it happens that one executes one action, or succession of actions over and over again, which means that the language should be able to express loops. Imagine that the system does have loops and it models a repeating action, but it has no way of knowing when to terminate it. In that case we will have infinite loops. If we want our loop to terminate at some point, we need an additional feature, namely finite loops. Another requirement for our human behaviour modelling language are constraints. Constraints would be extremely useful when making sure that an action is executed only if a specific precondition is met or when making a choice. Choice is something that humans do every moment of their life, so it is important the modelling language to be able to express it, as well as the reasons for taking one rather than the other option. Constraints will do the work, but we should not forget that there is a cause for a constraint and this cause is based on the prior knowledge we possess. Thus it is important for the model to have the ability of expressing prior knowledge and its impact on the system. To fully describe the impact on the system, the model requires another property, namely preconditions and effects. With their help the model will be able to express the connection between the conditions that have to be met before executing an action and the effects this action will have on the system. To properly model behaviour, synchronization is also required. It allows the coordination between two processes. Another aspect of human behaviour are interleaving activities. Imagine someone cooks and the phone rings. She will stop cooking to answer the phone and after the conversation is over, will continue with cooking. For describing such type of activities we need temporal operators like enabling, disabling, suspend and resume. Additionally, priority is an important operator when describing two processes that have to be executed but parallel execution is not allowed. For example the phone is ringing but at the same time the food is burning and one person cannot be at two places at the same time, so she has to decide which action to execute first. Priority can also be considered in the sense of probabilistic parameterization where we want to assign a priori distribution to the possible set of execution sequences, so that we can mark which execution sequence is more probable. Furthermore, an observation model is necessary in order to map the observations to the human behaviour model and to show on what kind of observations the model depends. Finally, in real systems it is always possible that the human behaviour does not correspond to the modelled world. In such cases, it is desirable that the model be able to denote such unknown behaviour as being inconsistent with the model. To identify maximally full set of HBM requirements several different datasets from various activity domains (see Table 1) have been analysed. The datasets include activities from the daily living domain, elderly care domain, behaviour of people with cognitive restrictions domain, smart meeting rooms domain and outdoor activities domain. In Table 1 the name of the datasets can be seen, the duration of the recorded data, the size of the

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Dataset Duration Size Sensors used Mobility 13x28d 557MB GPS Daily living (breakfast) 4h 550MB Ubisense, RFID, Acc. 3-Person Meeting 20x10m 3MB Ubisense 10-Person Meeting 2h 10GB Ubisense 4-Person Meeting 10x8m 1.6GB Ubisense Behaviour of people with cognitive restrictions 7x15h 250MB Acc., Gyro. Behaviour of people with cognitive restrictions II 5x5d 2GB Acc., Gyro. Elderly care 26m 606MB RFID, Acc., Gyro. Elderly care II 32m 977MB RFID, Acc., Gyro. Table 1. Activity datasets

dataset and the used sensors. In the sensors used column Acc. stands for Accelerometer, and Gyro. stands for Gyroscope. From the analysed datasets we have identified five groups of requirements for a human behaviour modelling language. These are requirements for procedural modelling, for parallel execution modelling, for probabilistic modelling, for causal modelling, and the modelling purpose. • Requirements for procedural modelling: composition, hierarchy, sequences, loops, interleaving activities, choice, constraints, enabling, disabling, priority, independence, suspend, resume; • Requirements for parallel execution modelling: parallelism, synchronisation; • Requirements for probabilistic modelling: observation models, probabilities for action sequences, probable durations of activities; • Requirements for causal modelling: preconditions, effects, relation to prior knowledge; • Requirements for modelling purpose: simulation, prediction, causal inference, state estimation, parameter estimation, detecting errors, unknown actions detection; 7. Human Behaviour Models: A Case Study Having in mind the requirements from Section 6, in this section we investigate 19 different human behaviour models and attempt to identify which of our requirements they satisfy; and what kind of paradigm, semantics, prior knowledge and purpose they have. The goal of this analysis is to find the most suitable candidates for using in a unified human behaviour modelling approach. 7.1. PECS Reference Model. [10, 11] PECS is an agent-based method suitable for the construction of simulation models in which human behaviour is important. The internal structure of a PECS agent consists of


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three layers: an input layer which has Sensor and Perception components; an internal layer describing the state of the agent and including the components Physic, Emotion, Cognition and Social Status; and an output layer consisting of the components Behaviour and Actor. In their work [11] Bernd Schmidt and Bernhard Schneider investigate the model by applying it to the simulation model Adam. Adam is an agent that is located on a 12 by 12 grid on which there are also several cells with food. Adam is hungry and his aim is to find a way to the food. However, there are also some danger points on the grid which Adam has to avoid but he has no prior knowledge of their position. The Adam model is used in order to show the interplay between the various PECS components and the way in which emotions (in this case fear) are modelled by PECS. When a danger point is encountered, the information of its location is recorded in Adam’s environment model, making Adam extend his knowledge of the world. The component Cognition then evaluates the new facts and increases the fear level in the component Emotion. The motive Fear now has the highest value and thus will become the action guiding. Adam then uses the component Sensor for self observation and hands the information on to the Perception model in order to monitor the fear level. When the level is above the threshold value, the perceived fear is transferred to the Cognition component therefore making Adam aware of his own fear. This causes the component Reflection to control the fear the higher the fear level, the more Adam will try to control it. The strength of the motive Fear Control is then transferred to the component Behaviour, where the various motives will be compared and the one with the highest value will be selected, thus becoming the motive guiding the agent actions. After managing to control his emotions, Adam will now be able to create a new plan that will lead him to the food source. The above example shows how the PECS Reference model is able to supervise and organise the complex interaction between cognitive and emotional processes, but in general it is also able to construct a wide range of models for agents which use dynamics based on physical, emotional, cognitive and social factors and the interactions between them. The system is based on hierarchy and supports sequences, loops and choice. Constraints are expressed by a set of condition-action rules on the basis of which an execution order is issued. The model also has a knowledge base that expresses the relation to prior knowledge. The type of prior knowledge that the model uses is environmental knowledge and knowledge based on cognitive psychology. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: forward rule-based • programming paradigm: imperative • formal semantics: operational • purpose: simulation 7.2. Behaviour Design Patterns. [20] Behaviour design patterns (BDP) originate from software design patterns and are adjusted for representing human behaviour in high-fidelity human behaviour simulations. The idea is to create different BDP that solve various problems and then integrate them in

ZD\SRLQWV IRU IRUPDWLRQV RQH FKDVHV D VLQJOH PRYLQJ WDUJHW WKH OHDG LQ HQJDJLQJ DQ HQHP\ RQH DFKLHYHV VRPH UDQJH IURP WKH WDUJHW LQ RUGHU WR HPSOR\ ZHDSRQV )XUWKHUPRUH ERWK DLUFUDIW DQG WDQNV SHUIRUP WKHVH DFWLYLWLHVDOEHLWFRQVWUDLQHGDQGLQÀXHQFHGE\GHWDLOVDERXW WKHGLIIHUHQWSK\VLFDOSODWIRUPVDQGWKHLUFDSDELOLWLHVDQG )LJXUH 16 LOOXVWUDWHV D VXEVHW RI 7DF$LU6RDU¶VKRISTINA EHKDYLRUYORDANOVA WKHRWKHUDJHQWVDQGREMHFWVWKH\VHQVHLQWKHHQYLURQPHQW WD[RQRP\ 7KH WZR ODUJH ER[HV UHSUHVHQW VRPH RI WKH 7KLV%'3HQFDSVXODWHVHDFKRIWKHVHEHKDYLRUFRPSRQHQWV existing models like for example SOAR or ACT-R. At the moment they are represented by PDMRUFDWHJRULHVRIEHKDYLRUV0RYHPHQWDQG6LWXDWLRQDO GHVFULEHKRZDQGZKHQWKH\DUHXVHIXODQGVSHFL¿ HVKRZ diagrams that unfortunately are not ableWKH\ to catch their full functionality. However, $ZDUHQHVVUML 7KH VPDOO VKDGHG ER[HV UHSUHVHQW EHKDYLRUV FDQ ¿W WRJHWKHU ZLWK RWKHU PRGXOHV WR GH¿QH PRUH the notation can be easily substituted by process calculus that will be more appropriate. WDNHQGLUHFWO\IURP7DF$LU6RDUWKHZKLWHER[HVUHSUHVHQW FRPSOH[DJHQWEHKDYLRU In their work [20], Glenn Taylor and Robert E. Wray use Tac-Air Soar [41] to extract WKH WD[RQRPLF VWUXFWXUH LQWURGXFHG LQ DQDO\VLV7KH VROLG BDPs. Tac-Air Soar is a model for aircraft pilots that is RI ableWKH toSURSRVHG execute VROXWLRQ most of LV theDOVR JLYHQ LQ 7KH VWUXFWXUH OLQHVUHÀHFWWKHLVDUHODWLRQVKLSVEHWZHHQHOHPHQWVDYRLG missions performed by a fixed wing aircraft (see WKH Subsection 7.13). analysing the Tac-Air GLDJUDP +HUHBy ZH RIIHU D VLPSOH KLHUDUFKLFDO JRDO VWDWLFWDUJHWLVDVSHFLDOL]DWLRQRIDYRLGWDUJHW 7KHGRWWHG Soar ER[HV model,LOOXVWUDWH Taylor and eight general behaviour categories: communications, GHFRPSRVLWLRQRIZKDWLWPHDQVWRPRYHWRZDUGDWDUJHW OLQHV EHWZHHQ WKHWray GDWDderived GHSHQGHQFLHV missions, control, coordination, flying, navigation, situational awareness, and action. Fig. 3ZKLFK LWVHOI RU FKDQJLQJ RULHQWDWLRQ EHWZHHQ EHKDYLRUV DQG KLJKOLJKW WKH FURVVFXWWLQJ QDWXUH FKDQJLQJ VSHHG shows an example of such BDP that deals with regularities a certainSLWFK class of movements. PD\ LQFOXGHinFKDQJLQJ UROO RU KHDGLQJ ,PSRUWDQW RILQWKLVH[DPSOHVLWXDWLRQDODZDUHQHVV Here there are three typical movement behaviours: chasing an enemy, a WKDW route, and RI YDULDWLRQ WR WKH GH¿QLWLRQ RI WKHfollowing SDWWHUQ LV SRLQWV maintaining in a IURP group. The latter two behaviours are composed of even simpler DUHLGHQWL¿ HGDQGLVRODWHG)RUH[DPSOHPRYHPHQWLQWKH $ IDLUO\ VLPSOH H[DPSOHformation %'3 GHULYHG WKLV 7DF$LU building blocks and on another level they are allJURXQG kinds GRPDLQLV of target chasing. Additionally, GLIIHUHQWWKDQ LQ WKH DLUthe GRPDLQGXH WR 6RDUDQDO\VLVLVVKRZQLQ)LJXUH7KH0RYHWR7DUJHW aircrafts perform these activities influenced by WKH the GHJUHHV physicalRI platform IUHHGRPcapabilities, DOORZHG LQ and HDFKthe SLWFK DQG UROO %'3FDSWXUHVUHJXODULWLHVLQDFHUWDLQFODVVRIPRYHPHQW other agents and objects that sense in the environment. The BDP encapsulates all the RULHQWDWLRQVDUHQRWW\SLFDOO\VSHFL¿ HGLQDWDQN%\LVRODWLQJ EHKDYLRUV)RUH[DPSOHWKUHHEHKDYLRUVW\SLFDOLQPLOLWDU\ behaviour components, describes specifies the way in which they can fit WKHVHGLIIHUHQFHVDQGJLYLQJWKHPDSODFHLQWKHSDWWHUQ GRPDLQV DUH FKDVLQJ DQ HQHP\ IROORZLQJ D their URXWHusage, DQG and together with other modules in order to define more complex behaviour. PDLQWDLQLQJ IRUPDWLRQ LQ D JURXS %DVLF URXWH IROORZLQJ WKHVLPLODULWLHVFDQEHH[SORLWHGWRFRYHUDZLGHDUUD\RI FRQVLVWVRIPRYLQJIURPRQHSRLQWWRDQRWKHU0DLQWDLQLQJ EHKDYLRUV ,QFOXGHG LQ WKH GH¿QLWLRQ RI WKH SDWWHUQ DUH D IRUPDWLRQ FRQVLVWV RI NHHSLQJ D SRVLWLRQ UHODWLYH WR D GHVFULSWLRQRIWKHSUREOHPLWVROYHVDQGWKHFRQVHTXHQFHV SRVLWLYHmove-to-target DQG QHJDWLYH RI PRYLQJOHDGYHKLFOH%RWKRIWKHVHEHKDYLRUVDUHFRPSRVHG Figure 3. Behaviour design pattern: [20]XVLQJ WKH SDWWHUQ 7KH SDWWHUQ RI HYHQ VLPSOHU EXLOGLQJ EORFNV VXFK DV FKDQJLQJ DOVR LGHQWL¿HV H[DPSOHV RI XVH DQG RWKHU SDWWHUQV WKDW FURVVFXWWLQJ WKDQ FRPPXQLFDWLRQV DVSHFWV RI ZKLFK FDQ EH HQFDSVXODWHG VWUDLJKWIRUZDUGO\ VXFK DV FRRUGLQDWLRQ WHDPZRUNDQGHUURUKDQGOLQJ&URVVFXWWLQJLVVXHVDUHWKH VXEMHFW RI FXUUHQW UHVHDUFK LQ VRIWZDUH HQJLQHHULQJ >HJ @DQGPD\SURYLGHVRPHSRWHQWLDOVROXWLRQV

)LJXUH7KH0RYHWR7DUJHW%HKDYLRU'HVLJQ3DWWHUQ &RS\ULJKW 6LPXODWLRQ,QWHURSHUDELOLW\6WDQGDUGV2UJDQL]DWLRQ


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BDP support hierarchy, sequences, parallelism, loops, choice, constrains and have connection to prior knowledge which makes them extremely fitting for the description of action constants. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: process calculi • programming paradigm: declarative • formal semantics: denotational • purpose: simulation 7.3. BDI. [18] The Belief Desire Intention (BDI) model is used for implementing beliefs, desires and intentions in order an agent to be able to execute a given plan. Beliefs represent the information state of the agents, namely what he knows about the world including environment, itself and other agents. Desires are the motivational state of the agent and represent objectives that the agent would like to accomplish. Intentions show the deliberative state of the agent, in other words what the agent has chosen to do. Emma Norling investigates the ability of BDI to model human behaviour using a Quake agent [18]. She uses Quake 2 as a simulation environment as it is commercially available, but is on the other hand complex enough to explore a range of human behaviours. Norling argues that BDI is suited for modelling human behaviour because of its folk psychological basis and tries to demonstrate that by building models of expert Quake 2 players. The approach she uses is to take advantage of a knowledge elicitation methodology that maps closely to the philosophical basics of BDI, and then to encode this knowledge using JACK Intelligent Agents [38]. Although the BDI architecture is good at capturing expert knowledge, it is too abstracted representation of human reasoning and has problems with situations where human behaviour is influenced by cognition. This is the reason why Norling tries to incorporate a naturalistic decision-making strategy into the agents and investigates whether this improves their human-like behaviour. Another research employs the BDI architecture for modelling human decision making for evacuation scenarios [45]. Using submodules based on Bayesian Belief Networks, DecisionField-Theory and Probabilistic Depth-First Search the proposed modelling framework is able to represent not only the decision-making but also the decision-making in one framework. The framework is then demonstrated for a human evacuation scenario in response to a terrorist bomb attack. The authors concluded that the proposed simulation framework is able to mimic human behaviour even in complex situations. Through the learning process, it can also represent the change of behaviour of novice agent to becoming closer to the behaviour of commuter agent. The model supports sequences, choice, constraints and relation to prior knowledge expressed by beliefs. The prior knowledge is environmental but also based on cognitive psychology where the folk psychological background is considered. However the BDI agents lack any specific mechanism with which to learn from past behaviour and adjust to new situations.

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If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: backward rule-based • programming paradigm: declarative • formal semantics: operational • purpose: simulation 7.4. GOMS. [22] GOMS stands for Goals, Operators, Methods, and Selection rules and aims at modelling human computer interaction observation. GOMS reduces the interaction to basic actions that can be physical, cognitive or perceptual. The model consists of goals – what the user intends to do; operators –actions, performed to achieve a goal; methods – sequences of operators that accomplish a goal; selection rules – describe when a user will prefer a given method over the other. In his work [22], Henrik Tonn-Eichstadt investigates a website usability for blind users by building a GOMS model. GOMS allows the calculation of execution times for performing tasks in user interfaces that later can be used to compare design alternatives and determine the most suitable solution. Thus Tonn-Eichstadt creates GOMS templates that can be arranged according to concrete task. Some of these templates include microscopic navigation, choices, acquiring content, activating elements, homing, text entry. After the templates are build, a concrete analysis can be done. GOMS supports hierarchy, sequences, choice, constraints and relation to prior knowledge based on ergonomics. However there are also some limitations: the analysis is suitable only for expert users; it can be applied only to linear tasks; assumes error-free execution; assumes only closed tasks. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: grammar based, forward rule-based • programming paradigm: imperative • formal semantics: operational • purpose: simulation, prediction 7.5. CPM-GOMS. [44] CPM-GOMS is a variation of the GOMS model where CPM stands for Cognitive Perceptual Model. In difference with GOMS, CPM-GOMS is able to express not only sequential processes, but also parallel as it assumes that experienced user can do multitasking. It also combines a hierarchical task decomposition with resource architecture, where the task analysis terminates in low level Cognitive, Procedural, and Motor operators. In their work [44], Seung Man Lee et al. combine CPM-GOMS with the Apex computational architecture, which is used to provide the underlying simulation environment. They test it using ACES which is a distributed agent-based event-driven airspace simulation system. A simple Apex human agent model for air traffic controller is developed to perform


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the functions of an individual controller of a traffic sector in a simulation. The hierarchical goal structure of GOMS is represented in Apex using its Procedure Description Language, where steps are decomposed hierarchically into procedures of simpler steps until they reach a decomposition of primitive actions that occupy human resources. By interacting with ACES, the Apex agent provides the opportunity to analyse the impact of changes in human behaviour on the overall system performance. The model supports hierarchy, sequences, parallelism, choice, constraints and relation to prior knowledge that is based on ergonomics. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: calculi process-based, grammar-based • programming paradigm: imperative • formal semantics: operational • purpose: simulation 7.6. ACT-R. [37, 25] ACT-R is a computational theory of human cognition that incorporates declarative and procedural knowledge into a production system where procedural rules act on declarative chunks. Facts and rules have specific attributes, where facts have an activation attribute that is responsible for influencing the retrieval probability, while rules have a reliability attribute which influences their probability of being used. ACT-R consists of two types of modules: perceptual-motor modules which are responsible for the interface with the real world; and memory modules which are divided into declarative memory and procedural memory. In his work [26], Juergen Kiefer models individual human behaviour in human multitasking by using ACT-R. Most of the cognitive models try to explain the overall performance rather than the differences in individual performances although there is much evidence for such differences. Kiefer strives to model the performance from another perspective: that individual differences are results of cognitive abilities, and that different strategies determine different task performance. Thus he investigates individual cognitive strategies in dynamic multitasking environments and the resulting theoretical consequences for modelling. He achieves that by using a car driving simulator where the test participants executed a compound continuous task. The test results showed that under multitasking cognitive strategies are used to optimally adapt to a given situation, thus the strategies were successfully transferred into ACT-R and their usage was able to explain individual differences in dynamic task environment. Another work based on ACT-R investigates the modelling of the progression of Alzheimer’s disease with application in smart homes [5]. The authors present a way of modelling and simulating the progression of dementia and also evaluate the performance of executing an activity of daily living. In difference with other works form this area of research, the paper focuses on modelling and simulating erroneous behaviour and its progression parallel to the disease progression, rather than the modelling of normal behaviour. The simulated behaviour of 100 people suffering from Alzheimer’s disease was compared with the results of 106 patients performing an occupational assessment. The comparison showed that the

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KRISTINA YORDANOVA

modelled behaviour closely resembles the behaviour of real patients and the authors concluded that the model is able to capture not only the erroneous behaviour but also its progression in the different phases of the disease. ACT-R supports hierarchy, sequences, parallelism, choice and relation to prior knowledge which in this case is based on cognitive psychology. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: forward rule-based • programming paradigm: imperative, declarative • formal semantics: operational • purpose: simulation, prediction, inference when extended 7.7. PMFserv. [8, 17] PMFserv is a testbed for implementing and studying how performance moderator functions (PMFs) contribute to a unified human performance architecture. There are PMFs on psychology and stress, personality, cultural and emotive processes, perception, social processes and cognition. There are already about 500 PMFs abstracted from the literature on human performance under stress and the architecture unifying them has been build on the following principles: inter-relations between the parts; subsystems are systems as well; study best of breed PMFs; agent archetypes and individual differences; and find the synergy. PMFserv consists of the modules Perception; Biology/Stress; Personality, Culture, Emotion; Social Module; Decision Making; and Expression. Silverman et al. [7] investigate the applicability of PMFserv for simulation of human agents by recreating portions of crowd and militia behaviours observed in the Ranger operation in Mogadishu, described in the book ”Black Hawk Down: A Story of Modern War” by Mark Bowden. Silverman et al. created four archetypes from the culture described in the book and used them to populate the simulated Bakarra Market. They did not have a specific success criteria but relied on the judgment of the project sponsor who indicated that the results are excellent, while the technical representative has provided positive reports. Another research investigates the dimensions of comparisons of different behaviour models using PMFserv [14]. There the bad and good modelling behaviours of company leaders are compared and the discovered dimensions are divided into those related to modelling leader context, and those related to leader profiling. The PMFserv allowed the modelling of the related context, the decision making behaviour and the world behaviour with which they specified the dimensions of comparison in leader-in-context models. The authors concluded that the models variability is likely to reduce when the models are designed by experienced specialists. However, the dimensions of comparison are not quantified and do not provide metrics for assessment of socio-cognitive leader models. PMFserv supports composition, hierarchy, sequences, loops, choice and relation to prior knowledge that is based on cognitive psychology. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: forward rule-based


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• programming paradigm: imperative • formal semantics: operational • purpose: simulation 7.8. Operation Models. [49] Operation models (OM) are sets of operation rules which represent how users operate their agents in simulations. The operation rules are condition-action rules and users operate their agents by following the action part of the rule whose conditions are satisfied. When developing an operation model, the consistency of the model has to be preserved after the combination of several operation rules. Also to ensure realism, personalities are integrated. This is done by extracting personal operational models from the users’ operation history and the logs of the agents that participated in the simulations. Yohei Murakami et al. [49] validate the method by modelling a subject operating an evacuee agent in an evacuation drill. They used their method to create evacuee agents that carried out virtual training for developing evacuation methods using as a simulation environment Free Walk [23], a 3D virtual space platform. They applied the modelling method with a domain knowledge of 22 operation rules obtained trough interview with 6 human subjects which resulted in 4 types of operation models. Operational models support composition, hierarchy, sequences, choice, constraints and relation to prior knowledge based on the environment and cognitive psychology. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • • • •

modelling view: forward rule-based programming paradigm: imperative formal semantics: operational purpose: simulation

7.9. Discrete Event System Specification (DEVS). [34] DEVS stands for Discrete EVent Simulation and is a modular formalism that is used for deterministic and causal systems’ modelling. A DEVS model consists of time base, inputs, states, functions connecting states, and outputs. It is possible to build larger models from atomic models by connecting them together in a hierarchical manner. Input and output ports serve as media for interactions. DEVS are used in various systems but our interest is in human behaviour modelling that was investigate in [34]. Mamadou Seck et al. [34] validate their approach by modelling a sniper team with three members on a tactical mission, with every member having an individual role. The DEVS model thus consists of four entities: the SEO, the sniper, the observer, and an aggregate observer and sniper model, which together create a coupled model of four entities with various interactions, and aggregation and disaggregation messages mediated through input and output ports. Additionally, every one of the entities has its own model consisting of an appraisal model, a model of the evaluation of stress and a behavioural model with different interactions. To complete the appraisal model, a certain personality is assumed based on

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KRISTINA YORDANOVA

the susceptibility to negative emotions. Finally, a hierarchy of the events regarding stressor intensity is created. The DEVS human behaviour model is rather simple to construct from the rules of engagement, thus making the understanding of correspondence of relationships from the real world rather trivial. This simplicity permits the building of more realistic models for discrete event human behaviour simulation. DEVS support composition, hierarchy, sequences, loops, choice and relation to prior knowledge which is based on the environment and cognitive psychology. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • • • •

modelling view: calculi process-based, forward rule-based programming paradigm: declarative, imperative formal semantics: denotational, operational purpose: simulation

7.10. Task Models with CTT. [35] CTT which stands for ConcurTaskTree is a notation used for expressing hierarchical task models. With it a compound activity is represented as a task tree, where each tree node represents a task which allows composite tasks to be decomposed into subtasks. Various temporal notations are used for constraining the task’s sibling nodes. Usually hierarchical task models are used for specifying the interaction of users with a software system, because they are able to describe basic temporal structures of composite activities. In their work [35], Giersich et al. use CTT to model tasks from the viewpoint of mobile and ubiquitous computing. With the help of CTT they manage to derive the dialog structure of a mobile human computer interface and then use probabilistic behaviour models to assign probability distribution over the activities space in order to infer the activity of a user. Fig. 4 shows an example of a task model, where a simple meeting consisting of three tasks (A, B, C) and a discussion (D) is represented in a CTT notation. The tasks can be executed in any order, which is specified by the temporal relation order independency (”| = |”). Additionally, the discussion can be performed only after all the tasks are executed, which is specified by the relation enable (”>>”). CTT supports composition, hierarchy, sequences, parallelism, loops, choice, constraints and relation to prior knowledge that is based on ergonomics. It also is able to express activity enabling and disabling as well as various other temporal operators. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • • • •

modelling view: calculi process-based programming paradigm: declarative formal semantics: operational purpose: simulation, prediction, inference when extended

7.11. CTML. [33, 32]


1112

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Figure 4. CTT M. Giersich et for al. a meeting with three tasks (A, B, C) and discussion (D) [35]

A

|=|

B

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C

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D

Fig. 1. Task model specifying the schedule of a meeting The Collaborative Language or CTML of is used as a specification framecompound activities. Task For Modelling inferring the activity a user from sensor data, we work for collaborative applications. It satisfies the following requirements for such collabneed additional information: a specification of how probable a certain execution orative applications: it has task driven methodology; it is able to model cooperation; it is sequence is.model Next, will and look at aformal current to this able to the we domain; it has syntaxapproach and semantics. Theseproblem. features make CTML a good solution for activities modelling especially when team cooperation is considered. CTML is a tuple consisting of a set of actors, a set of roles, a set of collaborative task expressions and a set of domain objects defined by a domain model. A collaborative task expression is modelled as a CTT-like tree that has an identifier, precondition and effect. In their work [33], Wurdel et al. not only present the CTML syntax, but they also give examples its usage in a collaborative They use a simplefrom meeting situation As outlined of above, computing the environment. user’s current activity sensor data reconsisting of a chairman, presenter and an audience. The chairman announces the talk quirestopic, a task model that allows toit,make statements theinformation plausibility of and while the presenter presents the audience can accessabout additional the presentation on theirApersonal devices. Subsequent talks are giventhe in user’s sensorconcerning data given a specifictopic activity. system can then try to identify the same manner, until at the end the chairman encourages an open discussion, sums the current task by selecting that task, whose action sequence is most plausible with session up and closes it. Using the CTML editor they specify this scenario and show the respect to theusability. observed sensor data. language’s Additionally, Wuerdel al showed that CTML,current or task models be Bayesian Filtering foret.identifying a user’s task respectively has been can successfully used in the field of activity recognition [31]. The specified models are used to define the used in several projects that aim at supporting user activities in classrooms, probabilities of the next possible action during activity execution. A probabilistic inference meeting rooms,having and office environments [4,5,6]. Here,usedynamic networks mechanism, recognized the current state, then makes of the taskBayesian model in order to adjust the probabilitiesincreasingly of the next state. approach aalso reduces the state space (DBNs) are investigated forThis modeling user’s activities [7,8]. growth as it removes all states that are unreachable form the current state. In our own work, we look at using DBNs for inferring the current task and CTML supports hierarchy, sequences, parallelism, loops, choice, constraints, relation to actions ofknowledge a team based of users. Given (noisy intermittent) sensor readings prior on ergonimics, enabling,and disabling, priority, independence, suspend, of the team resume. members’ positions in a meeting room, we are interested in inferring the

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Inferring Intentions

team’s current objective – such as having a presentation delivered by a specific team member, a moderated brainstorming, a round table discussion, a break, or the end of the meeting. The basic structure of the DBN we propose for modeling the activities of such a team is given in Fig. 2. In general, a DBN consists of a sequence of time slices, where each time slice describes the possible state of a system at a given time t. A time slice consists of a set of nodes that represent the system’s state variables at that time. State variables may be connected through directed causal links. A

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KRISTINA YORDANOVA

If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: calculi process-based • programming paradigm: declarative • formal semantics: operational • purpose: simulation, prediction, inference when extended 7.12. High Level Symbolic Representation for Behavior Modeling. [24, 40] High Level Symbolic Representation for behaviour modelling or HLSR aims to simplify the development and reuse of agents exhibiting relatively high degree of autonomy. HLSR provides language constructs that explain to the agents what to do and how to do it, how to understand the knowledge they have, and how to identify knowledge components useful for the development of agent extensions. HLSR includes a language for specifying high level agent behaviour, a mapping between the language and its underlying components and a methodology for agents’ development. HLSR can be compiled to generate code that for the moment is compatible with Soar and ACT-R, but its developers intend to extend it to other architectures. HLSR supports sequences, choice, constraints and relation to prior knowledge based on cognitive psychology. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: forward rule-based • programming paradigm: declarative • formal semantics: operational • purpose: simulation 7.13. SOAR and TacAir-Soar. [41, 16] TacAir-Soar is a rule-based system that generates human behaviours for military simulations. The system is capable of executing most of the airborne US military missions and accomplishes that by integrating real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated agents, maintenance of situational awareness, and the ability to respond to new orders during mission. The system is based on the SOAR architecture and has more than 5200 rules. SOAR uses production rules for representing long-term knowledge. These rules propose, select and apply operators, which correspond to the actions and goals that a human performs during mission. Jones et al. [41] evaluate the TacAir-Soar by embedding it within real-time large scale simulations of a battlefield. Time in the simulation corresponded to the one in real world, so both human and computer forces had to react to real world time. Each instance of TacAirSoar controls a single virtual entity and the entities’ missions included scheduled flights of aircrafts, defensive counter-attacks, close-air support, strategic attack, escorts, and airborn warnings. Each mission lasted from 90 minutes to 8 hours with 30 to 100 planes. The results showed that the behaviour of the synthetic pilots was consistent with the accepted


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tactics and the planes were able to flew their missions without human intervention. The planes were also able to correctly respond to weather condition and to use appropriate tactics for intercepting enemy aircrafts. Another research uses the SOAR architecture for knowledge representation and execution of basic behaviour requirements for synthetic adversaries for urban combat training [42]. Utilizing SOAR, the long-term knowledge is encoded as production rules, while the current situation is represented as declarative structures. The rules check for matchings against the current state and propose operations that are applicable with respect to the state. The authors concluded that creating fully autonomous intelligent agents, that exhibit various behaviours, will require much additional research. However the SOAR cognitive architecture helped significantly for nearing this goal. SOAR and respectively TacAir-Soar supports sequences, loops, choice, constraints and relation to prior knowledge based on cognitive psychology. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: forward rule-based • programming paradigm: imperative • formal semantics: operational • purpose: simulation 7.14. Rasmussen’s Human Behaviour Model. [30] Rasmussen’s Human Behaviour Model (RHBM) is a framework designed to define the training objectives, needs and means of minimally invasive surgery. RHBM distinguishes three different levels of human behaviour: skill-based, rule-based and knowledge-based behaviours. Skill-based behaviour is represented by actions that take place without a conscious control and the sensory information during such kind of behaviour is in the form of continuous signal. The second type of behaviour is rule-based and during it task execution is controlled by stored rules and procedures. During this type of behaviour the sensory data is perceived as discrete signals. The last type of behaviour is knowledge-based and it occurs when there are no rules form previous encounters. Here different plans are developed, analysed and the best plan is then selected. At this level the information is represented by symbols, that refer to chunks of conceptual information. In their work [30], Wentink et. al introduce RHBM as a practical framework for defining the needs, training objectives and means in minimally invasive surgery training. The training needs of a novice a represented with the skill-, rule- and knowledge-based RHBM behaviours. In this study, the authors concluded that the full understanding and implementation of these three types of behaviour can lead to a successful implementation of a full-scale laparoscopy simulator. RHBM supports sequences, choice and relation to prior knowledge based on cognitive psychology and the environment. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: forward rule-based

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KRISTINA YORDANOVA

• programming paradigm: imperative • formal semantics: operational, denotational • purpose: simulation 7.15. Markovian Task Model. [47, 48] Markov model is a stochastic model that assumes the Markov property. In our case, Markovian Task Model (MTM) is based on this assumptions and tries to model the relationship and dependencies between several separate actions that build a complex activity. MTM is represented as a directed acyclic graph where each vertex is a set of subtasks that have been already done, and each edge represents a transition between two vertices one of which has exactly one more subtask done. The task execution then is just a navigation through series of states that lead to the end state. In their work [48], Yi and Ballard evaluate their approach for human task recognition by performing peanut butter and jelly sandwich experiment with four objects involved: bread, peanut butter, jelly, and hand. In this experiment, Yi and Ballard track the eye movement in order to discover the object on which the eye is fixated and from that to infer the performed subtask. Both the hidden ”gaze object” and the observed ”recognized object” nodes have 5 states, one for each object and another for ”nothing”. The results from this experiment showed that the system is able to recognize human behaviour even if the sensor data is noisy. MTM supports composition, sequences and relation to prior knowledge based on the environment. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • • • •

modelling view: forward rule-based programming paradigm: imperative formal semantics: operational purpose: prediction, inference

7.16. Natural Language. [4] In their work Kojima et al. [4] try to translate behavioural expression into natural language sentence. Behavioural expressions are transformed into words and then a higher level behavioural expressions are derived by applying production rules to consecutive behavioural expressions. At the end, natural language texts can be produced by applying word dictionaries, case structure pattern of verbs and syntax rules into behavioural expressions. Kojima et al. test their method by using camera images for pose and position estimation of humans. Later conceptual behaviour features are extracted from the pose and the position and a feature value in the interval [0, 1] are assigned. As Kojima et al. explains ”to evaluate the feature value of move near, calculate d , the change of the distance of the agent to an object between at the beginning and at the end of the segment. Then the feature value is f(d), where the sigmoid function f(x) is defined by the following equation of which the range is [0, 1].


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f (x) = 1+Ae1−Bx where A and B are empirically selected constants. ” Then the most suitable verb for the given segment is selected, based on its conceptual features. Finally, with the help of production rules applied to several consecutive behaviour expressions, a higher level behaviour is inferred. This approach supports composition, hierarchy, sequences, choice, constraints and relation to prior knowledge based on the environment. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: forward rule-based, grammar-based • programming paradigm: imperative • formal semantics: operational • purpose: inference 7.17. Petri Nets. [19] Petri Nets are used for different purposes such as system modelling, verification and implementation, thus there are various ways for introducing them. However, here we consider them as means for modelling actions. Petri Nets model actions by expressing the way this actions change the local environment, where environment is the set of conditions and restrictions on which the actions depend. Petri Nets are widely used, because they have several advantages to other modelling methods such as mathematically and graphically founded formalism, mechanisms for abstraction and refinement and variety of available graphical tools [19]. In their work [28], Fix et al. investigate the relation between emotion and social norms in human societies. They consider the implementation of these dependencies in a multi-agent system in order to establish dynamic and flexible structures and also introduce an approach based on Petri Nets for modelling the emergence of social norms in multi-agent systems. Their approach allows the modelling of these norms on different levels of abstraction and shows that the Petri Nets formalism is suitable for behaviour modelling. Additionally, Fix et al. [27] introduce E-MULAN, which are emotional multi agent Petri Nets. E-MULAN is not only a reference architecture for modelling emotions in multi-agent systems but also the tool for realising it. It is build on several layers, namely social system view, social structure view, agent view and emotion view. The system allows a smooth integration of emotion models into agent systems and allows the conceptual separation of emotion and cognition. Thus emotions can be investigated in terms of their cause and their influence on the agent’s behaviour. This approach supports composition, hierarchy, sequences, choice, parallelism, loops, constraints, enabling disabling, priority independence, suspend, resume and relation to prior knowledge based on ergonomics and cognitive psychology. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: forward rule-based, process calculi • programming paradigm: imperative

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KRISTINA YORDANOVA

• formal semantics: operational • purpose: simulation 7.18. PDDL. [1] PDDL stands for Planning Domain Definition Language and is mainly used in planning problems. It is intended to express the elements and dynamics of a domain, namely what kind of predicates are there, what set of actions are possible, what is the structure of compound actions, and what are the effects of these actions. The language supports the basic STRIPS-like actions, and in addition it has conditional effects, universal quantification over dynamic universes, domain axioms over stratified theories, specification of safety constraints, specification of hierarchical actions composed of subactions and subgoals, and management of multiple problems in multiple domains [1]. Although PDDL is designed for planning problems, human behaviour can also be regarded as a plan, a person is following in order to achieve a goal. Burghardt et al. [15, 13] uses PDDL for synthesizing human behaviour, where the actions are represented by preconditions and effects which allows the generation of different possible behaviours without explicitly specifying every one of them. The model is then used as part of an inference mechanism for adjusting the probabilities for the next possible action. It is also used for reducing the search space growth, as only states that are part of valid plans are considered. This modelling approach supports composition, sequences, parallelism, choice, constraints, prior knowledge, based on the environment, enabling, disabling, priority and independence. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • • • •

modelling view: backward rule-based programming paradigm: declarative formal semantics: operational purpose: simulation, prediction, inference when extended

7.19. HTN with POP. [43] The concept of activities as hierarchical structures that can be further decomposed and refined is well known [43]. Hierarchical Task Networks (HTN) is a planning approach that regards activities exactly as plans that can be decomposed until an atomic level is reached where the actions cannot be further refined. Russell and Norvig [43] propose the combination of HTN with partial order planning (POP). Then the initial plan is composed of coarse grained activities that are refined into a partially ordered set of lower level actions. Thus, action decompositions contain information of how to implement a more complex action. The most fine grained decomposition layer is usually considered to contain actions that the agent can execute automatically. Using HTN with POP problems such as plan complexity can be resolved. Additionally, some irresolvable conflicts in plans with highlevel actions can be resolved when the plan is decomposed to a more fine-grained plans where the activities can have interleaving actions.


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Castillo et al. [29] use this approach to build an HTN planner and to enhance it with partial order metric structure, deadlines, temporal landmarking and synchronisation capabilities. The planner is tested by generating plans from the electronic tourism domain, where it planned a visit according to the personal tourist preferences. The results were compared with the performance of another HTN planner and showed superior performance. Another work uses HTN with POP for planning teamwork project management, namely the allocation of human resources and web services for the cooperative development of on-line courses in an e-learning center [3]. There the authors use HTN combined with a PDDL domain, that supplies consistency and completeness of the process. Although the HTN-POP approach is mainly used for planning problems, it can also be successfully applied in the human behaviour modelling, because the behaviour can be considered as a sequence of actions from a plan that lead to the achieving of a specific goal. This modelling approach supports composition, hierarchy, sequences, parallelism, choice, constraints, prior knowledge, based on the environment, enabling, disabling, priority and independence. If we consider the aspects modelling view, programming paradigm, formal semantics, and purpose, the method could be defined in the following way. • modelling view: backward rule-based • programming paradigm: declarative • formal semantics: operational • purpose: simulation, prediction 7.20. Features of human behaviour models. In this subsection we summarise the different features of the human behaviour models described above. The features we consider are modelling aspects (process-based and causal models), programming paradigms (imperative and declarative paradigms), formal semantics (operational and denotational semantics), type of prior knowledge (coming from the field of environment, ergonomics and cognitive psychology), model purpose (simulation, inference and prediction), and modelling features (composition, hierarchy, sequences, parallelism, loops, choice, constraints, enabling, disabling, priority, independence, suspend and resume). All these features are shown in Table 2 and Table 3 where the first column represents the different features and ”+” denotes that they exist in a given method. Additionally, ”?” implies that the information about the feature in question is not available, thus we are not sure if the model supports it or not. The models are ordered in the succession they appear in Section 7 and only their abbreviations are used. 8. Discussion Above we discussed the need for human behaviour models and for a unified human behaviour modelling approach that can be applied across various activity domains. Additionally, the different aspects of behaviour models were investigated, namely the modelling view, the semantics, the paradigm, the type of prior knowledge they utilise and the purposes the models serve. Furthermore, we attempted to identify the requirements a human behaviour model should satisfy in order to be successfully used in a unified approach. For

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KRISTINA YORDANOVA

Features PECS BDP composition + + hierarchy + + sequences + + parallelism + interleaving ? ? infinite loops ? ? finite loops + + choice + + constraints ? + enabling disabling priority independence suspend resume synchronization precondition / effect ? duration probab. param. observation model error detection prior knowledge ergonomics cog. psych. + environment + + model view grammar-based process calculi forward state-based + backward state-based paradigm imperative + declarative + semantics operational + denotational + model purpose simulation + + prediction inference Table 2. Human

BDI

GOMS

CPM-GOMS

ACT-R

PMFserv

OM

DEVS

CTT

+ + ? ? + + ? ? ? -

+ + + ? ? + + ? ? ? -

+ + + + ? ? ? + + ? ? ? -

+ + + ? ? ? + + ? ? + +

+ + + ? ? + + + ? ? ? ? ? ? ? ? -

+ + + ? ? + + ? ? ? + ? -

+ + + ? + + ? ? ? ? -

+ + + + + + + + + + + + + + + + -

+ +

+ + -

+ + -

+ +

+ +

+ +

+ +

+ +

+

+ + -

+ + -

+ -

+ -

+ -

+ + -

+ -

+

+ -

+ -

+ +

+ -

+ -

+ +

+

+ -

+ -

+ -

+ -

+ -

+ -

+ +

+ -

+ + + + + + + + + behaviour models and their features

+ -

+ -

+ + -


Features CTML HLSR SOAR RHBM MTM composition + + + hierarchy + sequences + + + + + parallelism + interleaving + infinite loops + ? ? ? finite loops + ? + ? choice + + + + + constraints + + + ? ? enabling + disabling + priority + ? independence + suspend + resume + synchronization + precondition / effect + + duration ? probab. param. observation model + error detection prior knowledge ergonomics + cog. psych. + + + environment + + + + model view grammar-based process calculi + forward state-based + + + + backward state-based paradigm imperative + + + + declarative + semantics operational + + + + + denotational model purpose simulation + + + + + prediction + + inference + Table 3. Human behaviour models and

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NL

Petri Nets

PDDL

HTN-POP

+ + + ? + + + + + -

+ + + + + + + + + + + ? + + + -

+ + + + + + + + + + + + + -

+ + + + + + + + + + + + + + + + -

+

+ + -

+ +

+ +

+ + -

+ + -

+ +

+ +

+ -

+ -

+

+

+ -

+ -

+ -

+ -

+ + -

+ + -

+ their features

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KRISTINA YORDANOVA

that reason we analysed several activity datasets from different domains and derived the requirements that are needed to model all of these activities. Finally, we discussed 19 different human behaviour modelling approaches, their usage, and the requirements they satisfy. The results from this analysis were presented in Table 2 and Table 3. From both tables it can be seen that the information about some of the models was insufficient for deciding if a model satisfies certain requirements. However, it can be concluded that the models satisfying the most requirements are CTML, PDDL and HTN-POP. CTML has the advantage of satisfying the requirements for procedural modelling and for parallel execution. Unfortunately, it does not support action durations, as well as probabilistic modelling. However, the latter problem was resolved in [31] where the model was combined with a probabilistic inference mechanism and used to reduce the search space and to adjust the execution probabilities for the next state. Such combination of a high level behaviour model and a lower level probabilistic mechanism makes CTML a powerful tool for intention recognition based on human behaviour modelling. One problem with using CTML for activity and intention recognition could be that every variation of the behaviour has to be manually modelled. This could be not only time consuming but also inflexible in terms of coping with variations of human behaviour that still lead to the desired goal. This drawback could be resolved by applying partial order planning techniques for the model generation. As proposed in [2], describing human behaviour in a probabilistic manner can be done by defining a catalogue of basic actions and their dependencies. These actions can be described in terms of preconditions and effects as planning operators. Later, when a concrete setting is present, the actions are parameterised with specific values. When these operators are fed into a planner, it produces a possible execution sequence of the behaviour. Thus, the variations in human behaviour do not need to be explicitly defined. It is enough that the causal links are valid and different variations of one behaviour can be produced. PDDL is a good example of such language for POP. From Table 3 it can be seen that PDDL, like CTML, satisfies a great number of requirements. One problem is that it does not support loops as the resulting plan is a directed acyclic graph, but on the other hand most activities can be expressed without using iterations, or by introducing action duration. Another feature that PDDL does not support is interleaving activities, which can be solved by introducing a hierarchical structure where more complex activities can have interleaving subactivities or atomic actions. This is basically the idea behind combining HTN with partial order planning approach. As shown in [29, 3] hierarchical task networks can be combined with partial order planning in order to reduce the plan complexity. Additionally, they show that PDDL can be extended with temporal operators which provides the desired requirement for activity duration. Another feature that PDDL and HTN-POP do not support are the requirements for probabilistic modelling. However, as shown in [15, 13] this problem can be resolved in a similar manner as with CTML. Namely, the PDDL model is converted into a probabilistic model and a filtering mechanism is applied for estimating the current state. Thus, it seems that HTN-POP approach is a good choice for a unified human behaviour modelling approach. Combining PDDL with HTN can provide enough functionality for describing


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activities form different domains. Additionally, using PDDL operators for describing actions provides action abstraction and reusability, namely the same action template can be parameterised with different values and used in different scenarios. References [1] Adele Howe, Craig Knoblock, Drew McDermott, Ashwin Ram, Manuela Veloso, Daniel Weld, and David Wilkins. Pddl the planning domain definition language. Yale Center for Computational Vision and Control, 1998. [2] Albert Hein, Christoph Burghardt, Martin Giersich, and Thomas Kirste. Model-based inference techniques for detecting high-level team intentions. behaviour monitoring and interpretation. Behaviour Monitoring and Interpretation - BMI, page 284, 2009. [3] Arturo Gonzalez-Ferrer, Juan Fdez-Olivares, Luis Castillo, and Lluvia Morales. Towards the use of xpdl as planning and scheduling modeling tool: The workflow patterns approach. In Proceedings of the 11th Ibero-American Conference on AI: Advances in Artificial Intelligence IBERAMIA ’08, 2008. [4] Atsuhiro Kojima, Masao Izumi, Takeshi Tamura and Kunio Fukunaga. Generating natural language description of human behavior from video images. Proceedings of the International Conference on Pattern Recognition, 2000. [5] Audrey Serna, Helene Pigot, and Vincent Rialle. Modeling the progression of alzheimers disease for cognitive assistance in smart homes. User Model User-Adap Inter, page 415, 2007. [6] B. Geisler. An empirical study of machine learning algorithms applied to modeling player behavior in a ”first person shooter” video game. M.S. thesis, 2002. [7] Barry G. Silverman, Gnana K. Bharathy, Jason Cornwell and Kevin OBrien. Human behavior models for agents in simulators and games: Part ii gamebot engineering with pmfserv. Presence: Teleoperators and Virtual Environments, 2006. [8] Barry G. Silverman, Michael Johns, Jason Cornwell and Kevin OBrien. Human behavior models for agents in simulators and games: Part i enabling science with pmfserv. Presence: Teleoperators and Virtual Environments, 2006. [9] J.C.M. Beaten. A brief history of process algebra. Theoretical Computer Science, 2005. [10] Bernd Schmidt. Modelling of human behaviour the pecs reference model. Proceedings 14th European Simulation Symposium, 2002. [11] Bernd Schmidt and Bernhard Schneider. Agent-based modelling of human acting, deciding and behaviour – the reference model pecs. Proceedings 18th European Simulation Multiconference, 2004. [12] Max Born. Natural Philosophy of Cause and Chance. Oxford University Press, 1949. [13] Christoph Burghardt and Thomas Kirste. Synthesizing probabilistic models for team-assistance in smart meetings rooms. CSCW, 2008. [14] Ceyhun Eksin. Dimensions of leader-in-context models. In Proceedings of the 10th International Conference on Cognitive Modeling, 2010. [15] Christoph Burghardt, Martin Giersich and Thomas Kirste. Synthesizing probabilistic models for team activities using partial order planning. KI, 2007. [16] Jacob Crossman. Aspects and soar: A behavior development model. 27th Soar Workshop, May 2007. [17] Daniel N. Cassenti. Performance moderated functions servers (pmfserv) military utility: A model and discussion. Army Research Laboratory, 2006. [18] Emma Norling. Capturing the quake player: Using a bdi agent to model human behaviour. Proceedings of 2nd Joint Conference on Autonomous Agents and Multiagent Systems, 2003. [19] Claude Girault and Rudiger Valk. Petri Nets for System Engineering: A Guide to Modeling, Verification and Applications. Springer, 2003. [20] Glenn Taylor and Robert E. Wray. Behavior design patterns: Engineering human behavior models. Behavior Representation in Modeling and Simulation, 2004. [21] Eitan Gurari. Data Structures. 1999.

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[44] Seung Man Lee, Roger Remington, Ujwala Ravinder and Michael Matessa. Developing human performance models using apex / cpm-goms for agent-based modeling and simulation. Proceedings of the 37th Conference on Winter Simulation, 2005. [45] Seungho Lee, Young-Jun Son, and Judy Jin. An integrated human decision making model for evacuation scenarios under a bdi framework. ACM Transactions on Modeling and Computer Simulation, 2010. [46] Joseph E. Stoy. Denotational Semantics: The Scott-Strachey Approach to Programming Language Theory. MIT Press, 1977. [47] Weilie Yi and Dana H. Ballard. Routine based models of anticipation in natural behaviors. American Association for Artificial Intelligence, 2005. [48] Weilie Yi and Dana H. Ballard. Behavior recognition in human object interactions with a task model. Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, 2006. [49] Yohei Murakami, Yuki Sugimoto and Toru Ishida. Modeling human behavior for virtual training systems. American Association for Artificial Intelligence, 2005.