In press: M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press.

this article we discuss the problem of learning in modular and hierarchical systems. Modular and hierarchical systems allow complex learning problems to be solved by dividing the problem into a set of sub-problems, each of which may be simpler to solve than the original problem. Within the context of supervised learning---our focus in this article---modular architectures arise when we assume that the data can be well described by a collection of functions, each of which is defined over a relatively local region of the input space. A modular architecture can model such data by allocating di#erent modules to di#erent regions of the space. Hierarchical architectures arise when we assume that the data are well described by a multi-resolution model---a model in which regions are divided recursively into sub-region

A*探索による木構造の階層型モジュラー強化学習法の提案

The international conference on Mario AI Championship focusing on computational intelligence and games. In the 2009 conference, Robin wins the search of A* method. A* is a best-first search method that is widley used in pathfinding and traversal, and can find a least-cost path from a given initial node to one goal node. However, in a complicated problem such a dead-end street, the algorithm cannot find a by path. This paper explains the tree structure in such a dead-end street and its by path.開催日：平成24年7月14日　会場：広島市立大

Appears in the Proceedings of the 1997 IEEE International Conference on Robotics and Automation, Albequerque, NM.

This paper describes a hierarchical scheduling, planning, control, and execution monitoring architecture for automating operations of a worldwide network of communications antennas