28.
Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking
down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment
while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning
in parallel with task execution. We call the architecture
Situation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors.
Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture
is able to learn efficiently and adapt to unexpected changes of the environment autonomously.
Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the
Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and
astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and
autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI).
Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the
Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University
of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental
study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for
knowledge base systems.
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