Abstract
This paper describes ProCHiP, a planner that combines CBR with the techniques of decision-theoretic planning and HTN planning in order to deal with uncertain, dynamic large-scale real-world domains. We explain how plans are represented, generated and executed. Unlike in regular HTN planning, ProCHiP can generate plans in domains where there is no complete domain theory by using cases instead of methods for task decomposition. ProCHiP generates a variant of a HTN – a kind of AND/OR tree of probabilistic conditional tasks – that expresses all the possible ways to decompose an initial task network. As in Decision-Theoretic planning, the expected utility of alternative plans is computed, although in ProCHiP this happens beforehand at the time of building the HTN. ProCHiP is used by agents inhabiting multi-agent environments. We present an experiment carried out to evaluate the role of the size of the case-base on the performance of the planner. We verified that the CPU time increases monotonically with the case-base size while effectiveness is improved only up to a certain case-base size.
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Macedo, L., Cardoso, A. (2004). Case-Based, Decision-Theoretic, HTN Planning. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_20
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DOI: https://doi.org/10.1007/978-3-540-28631-8_20
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