Comparing the Learning Processes of Cognitive Distance Learning and Search Based Agent

  • Authors:

  • Hiroshi Yamakawa;Yuji Miyamoto;Hiroyuki Okada

  • Affiliations:

  • -;-;-

  • Venue:
  • IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
  • Year:
  • 2001

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Abstract

Our proposed cognitive distance learning agent generates sequence of actions from a start state to goal state in problem state space. This agent learns cognitive distance (path cost) of arbitrary combination of two states. The action generation at each state is selection of next state that has minimum cognitive distance to the goal.In this paper, we investigate a learning process of the agent by a computer simulation in a tile world state space. An average search cost is more reduced more the prior learning term is long and our problem solver is familiar to the environment. After enough learning process, an average search cost of proposed method is reduced to 1/20 from that of conventional search method.