Abstract
Our proposed cognitive distance learning problem solver generates sequence of actions from initial state to goal states in problem state space. This problem solver learns cognitive distance (path cost) of arbitrary combination of two states. Action generation at each state is selection of next state that has minimum cognitive distance to the goal, like Q-learning agent. In this paper, first, we show that our proposed method reduces search cost than conventional search method by analytical simulation in spherical state space. Second, we show that an average search cost is more reduced more the prior learning term is long and our problem solver is familiar to the environment, by a computer simulation in a tile world state space. Third, we showed that proposed problem solver is superior to the reinforcement learning techniques when goal is changed by a computer simulation. Forth, we found that our simulation result consist with psychological experimental results.
