Abstract
To improve the perfomance of robot action planners we must equip them with better and more realistic models of the robots' behavior and the physics of the world. These more realistic models together with the robots' lack of, and uncertainty in, information, however, yield so many ways the world might change as plan gets executed that the prediction of the probability of something happening gets infeasible. In this paper, we discuss FPPD (Fast Probabilistic Plan Debugging), a plan revision technique that can, with high probability, forestall probable situation-specific execution failures: if the original plan is reliable for standard situations, then FPPD can debug the plan's flaws in nonstandard situations based on randomly projecting a small number of execution scenarios — even when considering various types of uncertainty and temporally complex behavior.
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© 1997 Springer-Verlag Berlin Heidelberg
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Beetz, M., McDermott, D. (1997). Fast probabilistic plan debugging. In: Steel, S., Alami, R. (eds) Recent Advances in AI Planning. ECP 1997. Lecture Notes in Computer Science, vol 1348. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63912-8_77
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DOI: https://doi.org/10.1007/3-540-63912-8_77
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