Biologically Inspired Architecture for Creating Cognitive Situated Software Agents Bruno A. Santos1, Henrique E. Borges1 1
Centro Federal de Educação Tecnológica de Minas Gerais - CEFET-MG, Intelligent Systems Laboratory Av. Amazonas, 7675 - Nova Gameleira. CEP. 38510-000. Belo Horizonte –MG, Brazil {bruno, henrique}@lsi.cefetmg.br http://www.lsi.cefetmg.br
Abstract. This paper aims to contribute to the construction of adaptive agents with biological inspiration and based on the contemporary approaches to cognitive phenomenon. Thus, it is proposed a generic and flexible architecture to create these agents, here denoted, Cognitive Situated Software Agents (CSSA). The CSSA created using such architecture will have an internal organization similar to living organisms. The operation of their cognitive system will be similar to the operation organism's nervous systems. Additionally, the CSSA will presents cognition and perception in the same way as they are considered by the situated cognition approaches
Keywords: Situated Cognition; Software Agents; Biologically Inspired System; Biology of Cognition.
1 Introduction The construction of adaptive hardware and software mechanisms is based on the approaches of the cognitive phenomenon. Two traditional approaches are: cognitivism [1] and connectionism [2]. Recently it is outstanding another approach that deals with the cognition as situated. This approach is referred in the literature of the cognitive science and artificial intelligence in different ways, some of them are: Biology of Cognition [3, 4, 5, 6]; Situated Cognition [7]; Enaction [8]; Ecology of the Mind [9]. In this paper these approaches will be referred as situated cognition. The hardware mechanisms developed according to these approaches are relatively advanced [10, 11]. On the other hand, the works involving the software mechanisms are little developed and very specific [12, 13]. Thus, this paper intends to contribute to adaptive software mechanisms construction based on the situated cognition approaches. In the section 2 it will be discussed about the biological inspiration used to design the architecture to create Cognitive Situated Software Agents (CSSA), which is the
adaptive software mechanisms intended for construction. In the section 3 such architecture will be presented and discussed.
2 Biological Inspiration The objects that will be used as biological inspiration to design the architecture are the living organisms with nervous system. These organisms were chosen because their nervous system vastly enlarges their cognitive capacity [3]. When it is said that a living organism presents cognition it means that it is suffering, through structural coupling, continuous structural changes in its nervous system in such a way that it conserves its adaptation in the course of his history of interactions with its environment. The situated character of the cognition means that the structure of the organism develops in congruence with the structure of the environment. It can be observed that is not possible to explain cognition if the organism exists in a biological vacuum [18]. The organism should exist as one of the elements that compose a unique and indivisible totality (natural world). The existence of the organism in its environment, is not just a choice, it is in fact the epistemological principle of the situated cognition approaches [3, 7, 8, 12, 16, 17]. For this reason, studying isolated parts it is not enough, they should be seen in a whole, in other words, the organisms should be studied in its environment. It is out off scope of this paper a discussion about the cognitive phenomenon of these organisms; the inseparability organism/environment; the parts that compose the world; and the interaction process among them. The present paper specifically discuss on the organisms’ architecture that composes the world. In the section 2.1 it will be presented a classification of the organisms components. In the section 2.2 it be will discussed the nervous system operation and organization. 2.1 Components Classification The classification of the organism’s components can be made in either cognitive or non-cognitive. The non-cognitive components (lung, heart, etc.) do not contribute to the adaptive organisms functions. These components just participate on the organism operation as a whole. The cognitive components compose the nervous system. They are classified according to their functions in the nervous system. Some components are: cortex; hippocampus; basal ganglia; cerebellum; etc. [22, 23, 24, 25]. It is important to point out that those components are considered as specialized functional areas and not as isolated compartments [22]. The cognitive components interact with the non-cognitive components and with the environment. This communication happens through the sensors and effectors neurons that are at the nervous system borders. The sensors and effectors neurons that interact with the non-cognitive components compose the visceral nervous system, and the ones that interact with the environment compose the somatic nervous system [22]. The neurons that are neither sensors nor effectors can be treated as internal ones.
In this paper, the set of cognitive components will be denominated as cognitive system and the set of non-cognitive components as non-cognitive system. The Fig. 1 presents an outline of an organism with its cognitive system and its non-cognitive system.
Fig. 1 - Cognitive and Non-cognitive systems The organism shown in Fig. 1 has a cognitive and a non-cognitive system. The non-cognitive system is composed of non-cognitive components that interact with the cognitive system through the sensors and effectors of the visceral nervous system. The cognitive system is composed of sensors, effectors and functional components. The sensors and effectors interact with the environment and with the non-cognitive components. The next section discusses in more details the cognitive system operation and organization. 2.2 Cognitive System The study of the cognitive system should be made considering the theories that are in conformity with the situated cognition approaches. One of these theories is the Theory of Neuronal Group Selection (TNGS) proposed by Edelman [19, 20, 21]. According to this theory, the brain can be divided into two areas: non-mapped and mapped. The first are composed of hippocampus; basal ganglia; cerebellum, etc. These elements contribute to linkage the organism actions; they participate in the emotional process; they control the organism attention; they accomplish the sensorial integration, etc. The mapped areas compose the cortex and accomplish the correlations between sensors and effectors. The cortex can be divided into three functional areas: primary, secondary and tertiary. The primary area is responsible for the organisms’ sensations and actions. The secondary area is responsible for accomplishing the sensations recognition, the actions planning, etc. The tertiary area is responsible for: concentration; emotional behavior; social behavior, etc. [22]. All these areas are composed of neuronal groups (NG), which are defined as a group of neurons that is located on a certain specific area and discharge synchronal, i.e., with the same frequency [19, 20, 21]. The neurons that compose a NG has reentrants connections, in other words, the neurons of the same NG possess synapses to each other in both directions [21]. Thus,
two neurons A and B that have synapses in both directions become co-dependent on their changing states. This generates a non-linear behavior in nervous system as a whole, characteristic of the self-organized systems. A set of NG interconnected form an abstract structure denominated local map (LM), which represents a very specific function, for instance, vision of the object’s color, vision of the object’s shape, etc. [21]. The NG that composes a LM also has reentrants connections. This reentrancy causes a co-dependence among the NG states, in other words, the changing state of a NG as a whole depends on the state of each NG on which has connections and vice-versa. A set of LM with reentrants connections composes an abstract structure denominated global map (GM) [21]. A GM is composed of a circuit that connects several LM and it is responsible for emerging an action with a meaning in a context, what is known as an experience with qualia [9]. “A global mapping is a dynamic structure containing multiple reentrant local maps (both motor and sensory) that are able to interact with non-mapped parts of the brain.” [21]. Consider, for instance, that an organism has sensitive and motors cortical areas related with the touch and the hands movements. Each LM of these areas gives to the organism the capacity to feel objects and to accomplish primitive actions like to hold and to loosen objects. The set of these LM connected by reentrants links and distributed in the whole brain compose the GM. These GM give to the organism the capacity to recognize an object by touching; to interpret the object in a social and emotional context; and to accomplish some action with the object in the context. An important characteristic of the nervous system operation is that all NG are continually changing their local state and eventually, their global state. Another characteristic is that there is not individual instant of time for changing states in the NG located on: the sensitive primary area; the secondary and tertiary areas; and the motors primary area. All changes happen continually in all NG, independently of the located area. This continuous changing state emerge the sensation, the perception and the action at the same moment. This is one of the most important characteristics of the situated cognition. In this section was presented some important aspects about the internal organization of the living organisms. It was also pointed out the nervous system organization and operation. In the next section it will be proposed an architecture to create CSSA. Such architecture is in conformity with biological aspects discussed here.
3 Proposed Architecture Since it does not make sense to talk about cognition of an organism isolated from its environment, it also does not make sense to talk about an cognitive software dissociated of its artificial environment. Thereby, for principle reasons, to build a CSSA it is indispensable to project it as part of an artificial world. The artificial world, in narrow analogy to the natural world, will be composed of CSSA - similar to the alive organism with nervous system - and software components - similar to the inanimate things. The Fig. 2 presents the class diagram in UML that models this situation
Sof twareC om ponent Artif icial W orld CSSA
Fig. 2 - UML diagram of the artificial world and its elements In the section 3.1, based on what was presented in the section 2.1, it will be presented a class diagram that models the CSSA components classification in a cognitive and non-cognitive. In the section 3.2, based on what was presented in the section 2.2, it will be presented some class diagrams that model the CSSA cognitive system. 3.1 Components Classification In analogy to the living organisms’ component classification, the CSSA components can also be classified in cognitive and non-cognitive, each one composing the cognitive and non-cognitive system, respectively (Figure 2). CSSA 1..1
1..1 CognitiveSystem 1.. 1 n CognitiveComponent
1..1
1..1 NonCognitiveSystem 1.. 1 n NonCognitiveComponent
Fig. 3 - CSSA and its cognitive and non-cognitive systems The CSSA possesses a cognitive system and a no-cognitive system and each one can possess several components. Some examples of non-cognitive components have the following functions in the CSSA: destruction, creation, persistence, changes of activity state, etc. [26]. These components can be represented as sub-classes of the class NonCognitiveComponent (Figure 4).
NonCognitiveSystem 1..1 interacts-with
1.. n
n NonCognitiveComponent
1..n
1..n Effector
1..n Sensor
- - - - - - - - - - - - {incomplete}- - - - - - - - - - - Creation
Dest ruction
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Fig. 4 - Non-cognitive system The non-cognitive components contribute to the cognitive phenomenon indirectly. They discharge stimuli to the cognitive components and are stimulated by these through the sensors and effectors components. In this paper there is no intention to discuss the project of the non-cognitive components. This subject is being researched in LSI [14] and it will be published in future paper. The cognitive system components are responsible for the CSSA adaptive capacity. The details of this system are discussed in the next section. 3.2 Cognitive System The cognitive system of CSSA (Figure 5) is composed of several cognitive components. The sensors and effectors components have the same function of the sensors and effectors neurons that are at the nervous system borders. They accomplish the interface between the cognitive system and the non-cognitive components and also between the cognitive system and the environment. The other cognitive system components can be classified in agreement with the function that they perform on the cognitive phenomenon. Three functional components represented are: Correlation-Sensor/Effector; Time/Sequence; and Emotion/Attention. The component Time/Sequence will create structures that give to CSSA the notion of time and sequence of its actions. The component Emotion/Attention will create structures that will give value to the CSSA experiences and it also will select the stimuli that will be given attention by CSSA. Oliveira, Campos and Borges [28] present a discussion about such component construction foundations. The component Correlation-Emotion/Attention has a similar function as the cortex. It accomplishes the correlations between the sensors and effectors components. All the functional components are composed by one or several structures. The structures that belong to the component Correlation-Sensor/Effector are mapped and the responsible for managing the connections among those structures are the local maps. In other words, the local maps will be responsible for correlating the structures
that have similar functionalities and compose the component CorrelationSensor/Effector. < organiz ed-in
G lobalM ap
CognitiveS y s tem
n 1..1 n CognitiveCom ponent
n < < abs trac t> > F unc tionalCom ponent
Loc alM ap
S ens or
E ffec tor
0..1 - - - - - - - - - - - - - -{inc om plete}- - - - - - - - - - - - - Correlation-S ens or /E fec ttor
Tim e/S equenc e
1..1
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1..n n
1..n
E m otion/A ttention
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Fig. 5 - Cognitive system The global maps will have the responsibility of managing the correlations between the local maps and the other functional components structures. These correlations managed by the global maps generate the behavior of CSSA in a context. As each local map represents a primitive behavior, the global map can correlate local maps in a hierarchy in layers as in Brooks’ subsumption architecture [10, 11, 27]. However other possibilities to do that correlation in a self-organized way are being investigated by the authors. The functional components structures can be of several types (Fig. 6). Two of them are: “Non-DeterministFiniteAutomata” and “ArtificialNeuronalGroups” (ANG). The last ones are similar to NG of TNGS. Observe that the artificial neuronal cells (sensor, effector and internal) compose the ANG. Besides that, each artificial neuronal cell has a specific state and it can make synapses with several other cells. The sensors and effectors artificial neuronal cells should not be confused with the sensors and effectors components. The first ones are in the ANG border and the second ones are in the cognitive system border. There is a communication between both of them when an ANG is stimulated or it stimulates some sensors or effectors components. These ANG are similar to the living organisms NG that are in the sensitive and motors primary cortical areas.
Structure
ANCEffector
ANCIntern
ANCSensor
- - - - - - -{incomplete}- - - - - - - - - - - - - {incomplete}- - - - - NonDeterministic FiniteAutomata
ArtificialNeuronal Group
ArtificialNeuronal Cell 1.. 1
1..n
1..n 1..1
State
1..1
1..n
Synapse
Fig. 6 - Structures The synapses among the artificial neuronal cells in the same ANG are managed by ANG itself. However the synapses between different ANG are managed by the local or global maps. It is important to point out that the synapses among the artificial neuronal cells in the same ANG or in different ANG, are reentrants. This generates the cognitive system non-linear behavior. The dynamic and reentrant linkages between ANG can be implement, for instance, through the GBSB (Generalized-Brain-State-into-Box) nets [29, 15], although there are other possibilities. In the living organisms nervous system all NG are changing their state continuously. To maintain the conformity with the biological inspiration, all ANG should also be changing continuously their states. Only through these continuous changes the CSSA will be able to have sensation, perception and action at the same moment, like in the living organisms. The architecture as a whole can be seen in the Figure 7. Using nowadays technology it is not possible to construct a CSSA that presents sensation, perception and action at the same moment like the living organisms. However, there are other ways to simulate that situation in software. One way is the use of threads. In this case, each structure would be one thread and would have one slice of time between the interval (T1–T0) to execute and to change its state. Alternatives to solve this problem are being studied by the authors.
S o ftwa re Co m p o n e n t A rti fi ci a l Wo rl d CS S A 1 ..1
1 ..1 Co g n i ti ve S yste m 1 ..1
GlobalM ap
1 ..1 No n Co g n i ti ve S yste m
n 1 ..1
1 ..1 n Co g n i ti ve Co m p o n e n t
n 0.. n No n Co g n i ti ve Co m p o n e n t 0. .n
- - - - - - - - - - {i n co m p l e te }- - - - - - - - -
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- - - - -- - - - - - - - - - - - - -{i n co m p l e te }- - - - - - - - - - - - - - - - - - Co rre l a ti o n -S e n so r/ E fe ctto r 1 ..1 n L o ca l M a p
0 ..1
n
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1 ..n 1 ..n S tru ctu re 1 ..n
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A NCIn te rn
A NCS e n so r
- - - - - - - - - - -{i nc om p le te } - - - - - - - - - - - - - - - - {i n co m p l e te }- - - - - No n De te rm i n i sti c Fi n i te A u to m a ta
1 ..1
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A rti fi ci a l Ne u ro n a l G ro u p
1.. n A rti fi ci a l Ne u ro n a l Ce l l 1 ..1
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Fig. 7 – Architecture for creating CSSA
4 Conclusion This paper contributed to the construction of adaptive software mechanisms based on the situated cognition approaches. It was discussed about the living organisms, their internal organization and their cognitive system. Based on this discussion a generic and flexible architecture was proposed to create CSSA with some similar aspects to the living organisms.
The CSSA created using the architecture will presents the cognition as the same way they are considered in the situated cognition approaches. However, as it was said, using nowadays technology it is not possible to implement the cognition identically as it happens in the living organisms. This impossibility occurs due to the continuity changes in the NG state. Without the continuity it is impossible to have sensation, perception, and action at the same time. Thus, to simulate the cognitive phenomenon in software using nowadays technologies it was suggested the use of threads. Using threads, ANG changing states would occurs as if they were continuous and the CSSA would behave as if it was having sensation, perception and action at the same time. The architecture proposed is generic. It can be used to create CSSA that act in different application domains. Another characteristic of the architecture is its high level flexibility. It leaves possibilities to easily add new functionalities. The project of the non-cognitive components; of the sensors and effectors components; of the functional components and of their structures; and of the local and global maps; should be based on researches about the organization and the operation of each one of these components in the living organisms, without losing the conformity with the situated cognition. The development of the adaptative software mechanisms in conformity with the situated cognition has been accomplished at the Laboratory of System Intelligent at CEFET/MG [14]. The present paper demonstrates part of this development.
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