Hideaki Takeda's Publication

• A. Ueno, H. Takeda and T. Nishida: Cognitive Learning for Practical Solution of the Frame Problem, in Proceedings of the Second Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 98), Vol. 2 (1998).

  The main problem for agents in real environments is how to abstract useful information from a large amount of environmental data. This is called the frame problem. Learning how to perform abstraction is a key function in a practical solution to the frame problem. As such a learning system, we developed Situation Transition Network System (STNS). The system extracts situations and maintains them dynamically in a continuous state space on the basis of rewards from the environment. These situations can be regarded as empirically obtained symbols. In this way, the system learns how to perform abstraction in a dynamic environment. The results of computer simulations are given.