Combining a rule-based expert system and machine learning in a simulated mobile robot control system

Authors:
Kate Foster;Tim Hendtlass
Affiliations:
Centre for Intelligent Systems and Complex Processes, School of Information Technology, Swinburne University of Technology VIC 3122 Australia;Centre for Intelligent Systems and Complex Processes, School of Information Technology, Swinburne University of Technology VIC 3122 Australia
Venue:
Design and application of hybrid intelligent systems
Year:
2003

Citing 6
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Abstract

This paper presents the design of a novel, dynamic, rule-based expert system used for simulated robot control. Machine learning techniques are used to enable the robot to collect training examples by autonomously exploring an unknown environment. As the interaction between the robot and the environment is dynamic, sequences of perceptions and sequences of events should determine future actions. Thus, temporal information is encoded into the rule-base. It is shown that the robot is able to incrementally develop a rule-base that enable it to successfully navigate through its environment.