An Agent-Based Testbed for Simulating Large Scale Accident Rescue

Journal of Computer Science (Special Issue): 21-26, 2005 ISSN 1549-3636 © 2005 Science Publications

An Agent-Based Testbed for Simulating Large Scale Accident Rescue Heuristics 1

Narjès Bellamine-Ben Saoud, 2Bernard Pavard, 2, 3Julie Dugdale 1 Tarek Ben Mena and 1Mohamed Ben Ahmed 1 RIADI-GDL Laboratory ENSI, Campus Universitaire 2010 la Manouba, Tunisia 2 GRIC-IRIT UPS-CNRS, U P S, 118 Route de Narbonne, 31062 Toulouse 3 IIHM CLIPS-IMAG. 385 Rue de la Bibliothèque, BP 53, 38041 Grenoble, France Abstract: The scope of this study is to present our study of the complex social problem of large scale accident rescue by applying an agent-based approach. Our field of study concerns situations involving a large number of victims over a wide area (which may or not be hostile) and where rescuers have to act rapidly to rescue the greatest number of victims in the shortest time by optimizing both their human and material resources. Based on real life observations and rescue plans on one side and designing new rescuing strategies on the other side we have built a generic and interactive user-friendly simulator. Modeling and simulation provide us with a virtual environment where we can easily develop and test a large number of “what-if” heuristic scenarios of different rescue organizations. These organizations may be compared and assessed in order to find efficient configurations and strategies for organizing a rescue. Key words: Non-deterministic, social, complex, NP problem INTRODUCTION

“a large number of parts that interact in a non-simple way” and that in complex systems “the whole is more than the sum of the parts”. In complex systems we do not have pure superposition of phenomena and processes[2]. Rescue and evacuation is a collaborative social complex process since various agents (victims, doctors, fire-fighters, police-officers) each with heterogeneous, complementary and interwoven competences and having various roles, organize themselves dynamically in groups and teams, adapt their behavior, process continuously all of the received information and react in an unpredictable way to their environment. Their main objectives are rescuing victims, performing better evacuation results and mainly reducing delays and minimizing the number of dead victims. The decision processes involved in the evolution of a crisis situation are complex and only partially known. Several mechanisms and issues have been identified to explain the dynamics of such groups: (1) the multicasting and overhearing mechanism imply the nondeterministic propagation of information among actors[3]; (2) the role of artefacts (objects of the environment) in the explicit and implicit communication[4]; (3) the role of verbal and non verbal interactions[5]; (4) the role of organizational awareness[6]. Such mechanisms are very often in mutual interaction. Also the environment plays a great role since it influences the scope of communication, the walking strategies of the rescuers and victims. We are then faced with a system composed of a tightly coupled

In large real-life hazardous situations, decision makers are increasingly faced with diverse problems, such as coordinating the various kinds of actors who belong to different structures and organizations, where they have partial knowledge about the real situation and the available resources and where they have to optimize their action plans usually within a very short time period and in stressful situations. In the frame of large scale accidents, performing close-to-reality and large scale live field simulations is becoming increasingly difficult and more expensive. Such simulations are also of limited value since they allow only a few scenarios to be played. Nonetheless, live-simulations are fundamental for understanding the characteristics of the situation, the interactions and the cooperative activities of the actors involved in this shared environment. Our ultimate goal in this research field is to provide the most appropriate and supportive plans for responding to crisis situations. Given the complexity of this issue and the related field specificities, our methodology is based on developing and assessing heuristics in order to identify the most efficient ones which describe the most appropriate solution by means of agent-based modeling and simulation. Organization in large scale accident rescue process is a socio-technical, collaborative, complex, open and Nondeterministic Polynomially acceptable problem (NP). In fact, very early in complex systems research, Simon[1] stated that complex systems are composed of

Corresponding Author: Dr. Narjès Bellamine-Ben Saoud, 3, rue du Romarin 2045 Laouina Tunisia, Phone: 216 98 964 565

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J. Comp. Sci., (Special Issue): 21-26, 2005 number of rescued victims in the “best” status health; (2) reducing the global rescue and evacuation time. Also, given the representation of the process as a graph, the problem of defining an efficient rescue plan becomes that of searching/defining “feasible and acceptable” scenarios and then finding efficient paths in this graph; where a path is defined by its sequencing of nodes, transitions as well as their associated enactment couples and related heuristics leading from the initial state (start of the rescue) to the final state. More concretely, the aims of our work are to develop a multiagent simulator able to model the dynamics of crisis situation characterizing this social problem and to offer a virtual environment where we can ask, run and evaluate what-if questions. Our approach is based on applying various appropriate heuristic algorithms to each agent and component of the agent-based social simulator. The simulator should enable flexible tests of various combinations of heuristics both real ones which represent the actual existing rescue as well as new and hypothetical ones enabling us to test alternatives and to help designing new solutions. We then aim to get a realistic testbed of scenarios and heuristics in reasonable delays.

set among all its functional levels ranging from interpersonal communication to a structured organization, codified in advance in frame of Rescue Organization plans. In[7] four specific properties of complex systems are discussed, which fit with our case study of rescue: (1) Non-determinism and non tractability: it is difficult either to trace the movements of each actor on the incident site or to know the exact steps of exploration or to estimate the time of the evacuation or the consequences of discovering new victims. (2) Limited functional decomposability: since our system is dynamic, it is therefore difficult to study its properties by decomposing it into functional stable parts. (3) Distributed nature of information and representation: the activities are naturally distributed between sub teams and managed by their representatives. Information is usually heterogeneous and is very difficult to classify and organize. (4) Emergence and self-organization: it is often unpredictable in real-time collaboration as to how a team should divide itself to solve an emerging problem such as reacting to sudden electric sparks causing new fires and producing more victims. Thus, teams are capable of self-organization. Furthermore, an open system is one in which the structure of the system itself is capable of dynamically changing, the characteristics of such a system are that its components are not known in advance and can change over time[8]. A large scale accident site can be considered as an open system. The rescue organization problem is NP since, as shown before, it is a non-deterministic, distributed, open and collaborative social process. In order to better emphasize the NP property, we could formalize this process as a graph. Consequently, our problem can be stated as follows: Given a catastrophic situation defined by: (1) the area/town/city (size, contents including routes, buildings, dangerous zones, obstacles, green spaces, key centers like hospitals or fire-fighting centers) defining the dynamic environment where it is occurring; (2) the victims (number, health status, gravity, abilities, localization, …) whose health is continuously changing; (3) a set of rescuers (doctors, nurses, fire-fighters, police-officers, decision makers, authority representatives) who need to act efficiently and rapidly; (4) a set of resources (ambulances, on site rescue materials) which should be used efficiently; (5) communication and coordination artifacts (paper forms, radio transmitters, electronic devices) which should facilitate coordination and support group cooperation; Is there a rescue plan characterized by the adequate values of the previous variables added to (1) doctors’ behavior (exploration and/or treatment and/or evacuation) ; (2) group organization strategy (centralized or distributed) ; (3) communication artifact (paper or electronic device) Which provides efficiency, by satisfying mainly the criteria of (1) maximizing the

MATERIALS AND METHODS Our methodology consists in applying iteratively four main steps: activity analysis, iterative agents modeling, simulation and validation. Activity analysis, Orly Red plan live simulation and main field results: Activity analysis consists of using an ethno-methodological approach in a real situation and focuses on the effective strategies followed by the actors to achieve their goals and to avoid problems. In our work, activity analysis aims to model the behavioral rules, of the autonomous and heterogeneous agents, corresponding to the concrete activities and not the foreseen ones and written in procedures. Consequently, our analysis has been conducted within a multidisciplinary team. We have also based our study on two official French plans: the White Plan[9] which concerns the intervention of the medical resources on the site of the accident; the Red Plan[10] which concerns fire-fighters interventions. It has, also, been based on video recording as well as a collection of hard and multimedia artifacts and documents produced during the live Orly Red Plan simulation. It corresponds to a live simulation of an aerial disaster organized on February 2004, in France. It involved a large number of victims: 33 alive and 40 dead (these roles are played by the young fire-fighters for simulation). It involved also a Rescue Commander, 122 fire-fighters, 16 doctors, 12 nurses, 8 ambulance teams and several observers. In total, 207 people participated in this exercise. The main problems discovered within the Orly Red plan live simulation concerned: (1) the use and coordination of 22

J. Comp. Sci., (Special Issue): 21-26, 2005 activities by means of the medical forms; (2) evacuation management; and (3) coordination problems among the different services. Iterative modeling: Agent-Based Social simulation approach: It is recognized that computer simulation, especially agent-based simulation, is valuable for studying complex systems. Computer simulation in general and agent-based simulation in particular, is a primary research tool of complexity theory[11]. Several agent-based simulation applications have been developed in many domains[12-14] as well as for the design of complex organizational systems in a crisis situation[3,15-18]. Given all the listed properties of our sociotechnical system, the multiagent simulation provides tools to model the emergent, non linear and distributed processes and produces full tractability of the process dynamics. Based on the emergency analysis results, an agentbased model and simulator was constructed. The study of the real world activity identifies 19 different intervening key roles and 2 supervision and coordination posts. After applying an iterative unification approach[19], we retained 7 heterogeneous key agents. We defined also the main interaction rules among agents taking into consideration the context where an agent evolves. Some rules are directly accessible via the simulator interface where others are directly implemented in the model. The main components of our multiagent model are presented hereafter according to the vowels model bricks[20,21]: 1.

2.

3.

4.

5.

The Environment refers to the domain dependent elements for structuring external interactions between entities. It represents the whole city or the area where an accident occurs including the routes and the hospitals. The autonomous Agents define the internal architecture of the processing entities. Victims and rescuers are the main agents of our model: * victims are modeled as reactive agents with a continuously evolving degree of health gravity, which is a function of the environmental properties of the victims’ location (normal zone, or dangerous zone) and of the rescuers intervention and treatment. According to medical sources[9], a five step scale of gravity has been considered. To model the dynamics of this health evolution, we adopted a Markov Chain, per type of zone and per type of intervention, defining the probabilities of transition from one state to another over time. Markov chains have been initialized in the model and than calibrated with experts and compared to real field data. Such a probabilistic view enables us to express intuitive facts and to implement flexible

heuristics related to victims modeling. In total 8 heuristic rules have been stated such as “In a normal/safe zone and without others action, a victim has a greatest chance to remain in its state, a very low chance to get better autonomously and a low chance to get worse”. * Rescuers, are supervisors, doctors, nurses, firefighters, collaborating to rescue victims. They have perceptive and cognitive intelligence which enables them to understand their environment according to a structured representation. In order to model the rescuers behaviors, more or less elementary (about 20) heuristic algorithms have been implemented to each rescue process step and adequately to roles. For example for on site treatment, one of the heuristics defines the perception guided victims’ finding: “while walking on the site, a doctor looks for the nearest or the first victim within her/his visibility zone whether (s)he is just entering the site or not.” The Interactions are of three types: (a) between actors and the environment; (b) among rescuers who are exploring collectively, evacuating by pairs, communicating directly or via artifacts; and (c) between the user and the simulator. An ontology has been defined to insure agent’s communication. The Organization is defined both as centralized strategy which is adequate to real field and as distributed strategy where we modeled new collaboration forms. The User interfaces allow (a) the initial configuration of the simulations by stating new ones or by loading them from scenario database, drawing or loading existing city maps, placing key points, (b) continuous visual control of the process of rescuing, dynamic changes of parameters of an on-going simulation (e.g. adding new victims, adding new rescuers, or adding dangerous zones or new obstacles on sites where we can cope with realistic scenarios where new victims are discovered after the first exploration) and (c) stepby-step simulation.

Added to heuristics modeling dynamics of the system, simulation parameters (total number is 139) are used to express model components’ properties such as agent’s numbers, visibility and speed, treatment delays, etc. Overview of the simulator: The simulator has been developed in Java language, using the agent-based platform JADE complying with the FIPA specifications. Our simulator interface provides Input, Output and Control widgets and displays as shown in Fig. 1. The simulator is generic, interactive and provides full tractability both on-line and off-line by 23

J. Comp. Sci., (Special Issue): 21-26, 2005 tracing each component along the corresponding files and/or database fields.

simulator. Each use case identifies a new rescue organization which should be instantiated by a set of values associated to configuration variables and parameters to generate the effective experiment cases to run and compare. Each set of experiments related to a case may explore research questions such as: “impact of the organization strategy on the rescue” or “impact of the communication artifact on rescue”. After validating the simulator, we run virtual experiments in order to assess scenarios. According to the experiment aim, we have qualitative and quantitative results. Efficiency criteria may vary and may even be multi-variable. Since, we stated that the aim of emergency rescue plans is to rescue the more victims in shortest delay, we consider on a first stage the following efficiency criteria:

VIRTUAL EXPERIMENTS AND RESULTS Given that we have developed a virtual environment encompassing heuristics similar to concrete field as well as hypothetical ones, the use of this simulator and its experimentation covers two families of scenarios: real and hypothetical. We design experiments by combining choices of the main strategies and related heuristics: (1) The environment may essentially be limited to the incident field or extended to a whole city, including one or many incidents, routes and hospitals; (2) The rescue organization strategy may be centralized or distributed; (3) the incident field may be considered as one area or subdivided in sub-zones; (4) the rescuers operation may include exploration or may prioritize evacuation similarly to the American “keep and run” approach; (5) each doctor may organize her/his actions on victims according to the distance or according to the gravity. These choices can generate 64 main use cases of the

GET=the Global Evacuation Time of all victims ETFV=the Evacuation Time of the First Victim RR=the Rescue Rate RAE=Rescue Rate of Absolute Emergency RRE=Rescue Rate of Relative Emergency RDCD =Rate of dead victims.

Simulator

* * * * * *

Fig. 1: Overview of the simulator: input, output and control interfaces Table 1: Exp 1 Exp 2 Exp 3 Exp 4 Exp 5 Exp 6 Exp 7 Exp 8

Summary of the simulation results for each of the eight experiments GET ETFV Centralized/1 Zone/Paper 325 122 Distributed/1 Zone/Paper 340 90 Centralized/4 Zones/Paper 424 204 Distributed/4 Zones/Paper 263 86 Centralized/1 Zone/electronic 282 80 Distributed/1 Zone/electronic 273 55 Centralized/4 Zones/electronic 284 95 Distributed/4 Zones/electronic 346 54

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RR 0,882 0,941 0,912 1,000 0,941 0,941 0,853 0,941

RAE 0,7 0,7 0,9 1,2 0,7 1,2 0,7 1,2

RRE 0,958 1,042 0,917 0,917 1,042 0,833 0,917 0,833

RDCD 1,103 1,051 1,077 1,000 1,051 1,051 1,128 1,051

J. Comp. Sci., (Special Issue): 21-26, 2005 what we could think and the results are sensitive with initial configuration values. According to the simulation objectives we may consider some of them and ignore the others by assigning priorities to efficiency criteria. Also new criteria may be found with rescue experts’ help.

Table 1 shows samples of experiments and their simulation results: Exp 1 represents the scenario similar to the Orly Red Plan live simulation. The seven others are examples of new rescue heuristics that may be applied. Configuration values are coherent with the Orly Red Plan simulation: 73 victims, 15 doctors, 60 nurses, 8 ambulance teams, 1 hospital with high hosting capacity. All other parameters have default values. By sorting results according to ascending GET and plotting these values (Fig. 2) we notice that: GET and ETFV are not always correlated and don’t evolve necessarily on the same way; by reference to Exp 1, which is the practiced one, four scenarios give better results. So we can use our simulator to find better scenarios. Also, by considering Rescue Rates (Fig. 3) we notice that: RR decreases as the RDCD increases; Exp 1 is not the one maximizing the rescue rate; experiments 2, 5, 6 and 8 give almost same rates. By combining results of Fig. 2 and Fig. 3, Exp. 4 seems the most efficient among this set of scenarios for this initial configuration values.

CONCLUSIONS AND PERSPECTIVES In this study, we introduce the large scale rescue case study, which is a complex socio-technical collaborative open system, non-deterministic, distributed and NP problem. We applied an iterative agent-based appraoch, guided by field works, to understand the process and to design new efficient rescue organization including new collaborative technologies by means of the generic simulator which provides an environment to test alternatives of heuristic scenarios. We have therefore developed an agent-based simulator, where eleven kinds of autonomous agents behave according to their own information with respect to their role and interdependencies with others, taking into account the evolving characteristics of the environment. Having implemented the contextual micro-behaviors, we observed the emergence of the entire team reorganization in rescue teams. Sample of results illustrating a way of use of this tool has been given. By this modeling/simulation activity we show that we can create a virtual environment with cooperating agents interacting in a dynamic environment. Within modeling activities we can begin to understand in depth the cooperation processes and we can find out the parameters, variables and strategies hidden in this work. By implementing the possible heuristics relative to each strategy we provide a generic and interactive tool, with full tractability, which is easily configurable and user friendly and which enables testing of different scenarios: a testbed for real and hypothetical heuristics and rescue organization scenarios. Although we have voluntarily implemented a “simplified”, “bounded” and realistic model, as a first stage in our research in this field, we got a complicated simulator with more than one hundred parameters. In future work, we intend to focus on dynamic environment properties such as fire evolution, route blocking or traffic jams. Hence, new heuristics need to be implemented and assessed. Consequently, to be more realistic and in order to be able to simulate really large scale accidents (hundreds of victims) within reasonable execution times we are developing a distributed version of this simulator.

Fig. 2: Evacuation time per experiment

Fig. 3: Rescue rates per experiment

ACKNOWLEDGMENTS

Contrarily to what may be intuitively “expected”, electronic mode is not always better than paper mode and distributed strategy is not always better than centralized one. This process is more complex than

Thanks to the COSI Project for the opportunities given to focus on this study within summer schools. Sincere thanks to S. Darcy, M. Gadri, N. Ben Touati for 25

J. Comp. Sci., (Special Issue): 21-26, 2005 their contributions and to Prof. Henda BEN GHEZALA for her recommendation.

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