進化型学習に基づく積木問題の自律分散的解法

概要

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We describe a new approach to a nonlinear block stacking problem in a traditional AI field. In our problem setting, we see the problem as one of planning in a dynamic environment and attempt to solve it in a distributed manner. Each block is considered as an autonomous agent having sensors and effectors and capable of moving in the block world. As the behaviors of the agents change the environment, the agents are required to resolve conflict among other agents. In our approach, motions of the agents are controlled using a recurrent neural network (RNN). Based on sensory inputs to the RNN, the agents select their alternative actions. The RNN implemented in each agent is trained through evolutionary learning and both the structure and connection weights of the network are altered appropriately using two kinds of mutations according to the degree of success of the planning. Actions of agents are triggered by activation levels which are calculated on the basis of received signals from other agents and the environment. Based on the proposed methodology, computational experiments are carried out and some experimental results are shown.
一般社団法人日本機械学会の論文
1996-05-25