Backwards State-space Reduction for Planning in Dynamic Knowledge Bases

Valerio Senni
Michele Stawowy
(IMT Institute for Advanced Studies)

In this paper we address the problem of planning in rich domains, where knowledge representation is a key aspect for managing the complexity and size of the planning domain. We follow the approach of Description Logic (DL) based Dynamic Knowledge Bases, where a state of the world is represented concisely by a (possibly changing) ABox and a (fixed) TBox containing the axioms, and actions that allow to change the content of the ABox. The plan goal is given in terms of satisfaction of a DL query. In this paper we start from a traditional forward planning algorithm and we propose a much more efficient variant by combining backward and forward search. In particular, we propose a Backward State-space Reduction technique that consists in two phases: first, an Abstract Planning Graph P is created by using the Abstract Backward Planning Algorithm (ABP), then the abstract planning graph P is instantiated into a corresponding planning graph P by using the Forward Plan Instantiation Algorithm (FPI). The advantage is that in the preliminary ABP phase we produce a symbolic plan that is a pattern to direct the search of the concrete plan. This can be seen as a kind of informed search where the preliminary backward phase is useful to discover properties of the state-space that can be used to direct the subsequent forward phase. We evaluate the effectiveness of our ABP+FPI algorithm in the reduction of the explored planning domain by comparing it to a standard forward planning algorithm and applying both of them to a concrete business case study.

In Dragan Bošnački, Stefan Edelkamp, Alberto Lluch Lafuente and Anton Wijs: Proceedings 3rd Workshop on GRAPH Inspection and Traversal Engineering (GRAPHITE 2014), Grenoble, France, 5th April 2014, Electronic Proceedings in Theoretical Computer Science 159, pp. 84–99.
Published: 29th July 2014.

ArXived at: bibtex PDF

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