Evolutionary Approach to Investigations of Cognitive Systems Vladimir Red ’ ko a), Anton Koval ’ b) a) Scientific Research Institute for System Analysis, Russian Academy of Science, Moscow b) National Nuclear Research University “ MEPhI ”, Moscow

Epistemological problem Epistemological problem: why human logical thinking is applicable to cognition of nature? To emphasize the problem, let us consider physics. The power of physics is due to effective use of mathematics. However, a mathematician makes logical inferences, proves theorems, basing on his mind, independently from physical world. Why are his results applicable to real nature, to real physical world? To investigate problem, it is reasonable to analyze cognitive evolution (evolution of animal cognitive abilities), evolutionary origin of human logical thinking. So, it is reasonable to model cognitive evolution

Sketch program: steps of modeling cognitive evolution (from simple animal cognitive abilities to mathematical deductions): 1)Modeling of adaptive behavior of autonomous agents that have natural needs: food, safety, reproduction 2)Investigation of the transition from the physical level of information processing in nervous system of animals to the level of the generalized “ notions ” 3)Investigations of processes of generating causal relations in animal memory 4)Investigations of “ logical conclusions ” in animal minds. Comparison of animal “ logic ” with human logic

Model of several needs and motivations (step 1 of the sketch program)

Several needs and motivations Population of autonomous agents is considered. Any agent has the following needs: food, safety, reproduction. Needs are characterized by motivations M F, M S, M R, and factors F F, F S, F R. Any time moment only one motivation is leading. Agent control system is set of rules S k  A k Rule weights W k are adjusted by means of both reinforcement learning and Darwinian evolution of agent population. Situation S k : 1) activity of the predator in vicinity of the agent, 2) previous action of the agent, 3) current leading motivation of the agent. Actions: 1) searching for food, 2) eating of food, 3) preparing for reproduction, 4) reproduction, 5) defence from a predator, 6) resting.

Several needs and motivations Scheme of choosing of leading motivation T F, T S, T R are thresholds M N is additional motivation (it becomes leading very rare) Changes of the factor (F F, F S or F R ) corresponding to the leading motivation are rewards at reinforcement learning

Several needs and motivations Results of computer simulations Dynamics of factors Dynamics of motivations Cycles of agent behavior and chains of actions are observed

Model of formation of generalized notions (step 2 of the sketch program)

Agent is searching for food in cellular environment Agent There are 10x10 cells. Portions of food are randomly distributed in 50 cells. Agent control system is set of rules: S k  A k, S k and A k are situation and action. Situation S k : presence or absence of food in agent field of vision. Actions A k : moving forward, turning left/right, eating, resting. Rule weights W k are adjusted by means of reinforcement learning Circles indicate agent field of vision. Arrow shows forward direction of agent

Formation of internal notions 1) food is here  “ eating ” ; 2) food is forward  “ moving forward ”, then “ eating ” ; 3,4) food is right/left  turning right/left, then “ moving forward ”, then “ eating ” ; 5) there is no food in field of vision  “ moving forward ” … 5 heuristics generalize selected rules: Internal notions of the agent are formed: 1) food is here, 2) food is forward, 3,4) food is right/left, 5) there is no food in field of vision

Adaptive behavior of modeled “ organisms ” Witkowski M. An action-selection calculus // Adaptive Behavior, V. 15. No. 1. PP Butz M.V., Sigaud O., Pezzulo G., Baldassarre G. (Eds.). Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior. LNAI 4520, Berlin, Heidelberg: Springer Verlag, Vernon D., Metta G., Sandini G. A survey of artificial cognitive systems: Implications for the autonomous development of mental capabilities in computational agents // IEEE Transactions on Evolutionary Computation, special issue on Autonomous Mental Development, V. 11. No. 2. PP Intelligent autonomous agents

Sketch program: steps of modeling cognitive evolution 1)Modeling of adaptive behavior of autonomous agents that have natural needs: food, safety, reproduction 2)Investigation of the transition from the physical level of information processing in nervous system of animals to the level of the generalized “ notions ” 3)Investigations of processes of generating causal relations in animal memory 4)Investigations of “ logical conclusions ” in animal minds. Comparison of animal “ logic ” with human logic

Conclusion Comparing steps of the sketch program with our models and other works, it is possible to conclude that we can see some small fragments of a picture of cognitive evolution now, but we do not see the whole picture yet Nevertheless, investigations of cognitive evolution are interesting and important