Behavior Selection and Memory-based Learning for Artificial Creature Using Two-layered Confabulation

Confabulations, where millions of items of relevant knowledge are applied in parallel in the human brain, are typically employed in thinking. This paper proposes a novel behavior selection architecture and memory-based learning method for an artificial creature based on two-layered confabulation. A behavior is selected by considering both internally generated will and the context of the external environment. Proposed behavior selection using a confabulation scheme is a parallel process in which a number of behaviors are considered simultaneously. An arbitration mechanism is employed to choose a proper behavior which is to be put into an action. Also memory-based behavior learning is proposed, w here the memory has the selection probabilities of behaviors based on will and context. The learning module updates the contents of memory according to the user-given reward or penalty signal. To demonstrate the effectiveness of the proposed scheme, an artificial creature is implemented in the 3D virtual environment such that it can interact with a human being considering its will and context.