Decision analysis for an experimental robot with unreliable sensors

Authors:
L. Stephen Coles;Alan M. Robb;Paul L. Sinclair;Michael H. Smith;Ralph R. Sobek
Affiliations:
Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California;Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California;Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California;Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California;Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California
Venue:
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
Year:
1975

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Abstract

This paper reports on the continuing design of and experimentation with Jason, the Berkeley Robot. Progress has been made in various aspects of the hardware (including the chassis, communications controller, and onboard microprocessor) and software (including problem-solving programs and world models). A particular experiment, analogous to the classical "Monkey and Bananas" Problem, is described. A major feature of the reformulation of this problem is the use of Decision Analysis in coping with uncertainty. Based on the accimulated expected costs of executing the steps of various hypothetical plans, Jason can evaluate the relative merits of direct action versus prior information-gathering using potentially unreliable sensors.