engleski

A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization

This paper looks at designing a robotic assembly system as a multi-agent system. Any multi-device system, or any system whose performance is naturally decomposable, can be interpreted as a corporation of agents. Such a scheme comprises the ability to create a collaborative system that can provide the achieving of the social intelligence. Social behavior is the highest form of intelligence, which is able to solve very complex problems, autonomously create new procedures and efficiently adapt to new tasks. The presented multi-agent model is based on processing units that include recognition networks, problem-solving strategies and learning engines. It integrates perception, recognition, problem-solving, learning and communication capabilities. The reinforcement learning method is used here to evaluate the robot is behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience, which can be used in solving similar problems in the future. To recognize problem similarities we applied the Adaptive Fuzzy Shadowed (AFS) neural network.

robotic assembly; multiagent system; autonomous agent; learning methods; neural network

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