Explorations of perceptional mechanisms for adaptive agents

The environment in the real world has an almost infinite number of events. From a Cartesian point of view, these events consist of more detailed factors. Those factors often correspond to the physical properties of the environment and this renders symbolic AI powerless. However, affordance, i.e. the availability of behaviours with respect to the environment, uses functional properties of which it is possible to be directly aware. To make the agent more adaptive, the useful information is obtained by the agent itself. This paper describes two perception mechanisms that have possibilities to allow the agents to perceive the affordance through learning. The mechanism is aimed for the perception of the environment and the self-perception, respectively. The computer simulations and some preliminary experiment by the real agent, a vehicle with vision system, show the possibilities of the mechanisms.