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Behavior of an Adaptive Self-organizing Autonomous Agent Working with Cues and Competing Concepts

Jànos Bolyai Institute of Mathematics

Andràs Lórincz

Hungarian Academy of Sciences

A brain model-based alternative to reinforcement learning is presented that integrates artificial neural networks and knowledge-based systems into one unit or agent for goal-oriented problem solving. The agent may possess inherited and learned artificial neural networks and knowledge-based subsystems. The agent has and develops ANN cues to the environment for dimensionality reduction (data compression) to ease the problem of combinatorial explosion. Here, a dynamical concept model is put forward that builds cue models of the phenomena in the world, designs dynamical action sets (concepts), and makes them compete in a spreading-activation neural stage to reach decision. The agent works under closed-loop control. Here we examine a simple robotlike object in a two-dimensional conditionally probabilistic space.

Key Words: adaptivity • artificial neural networks • knowledge-based system; self-organization • activation spreading • autonomous system

Adaptive Behavior, Vol. 2, No. 2, 131-160 (1993)
DOI: 10.1177/105971239300200202


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