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By Fausto Giunchiglia, Paolo Traverso (auth.), Susanne Biundo, Maria Fox (eds.)
This booklet constitutes the completely refereed post-proceedings of the fifth ecu convention on making plans, ECP'99, held in Durham, united kingdom, in September 1999.
The 27 revised complete papers provided including one invited survey have been conscientiously reviewed and chosen for inclusion within the publication. They handle all present facets of AI making plans and scheduling. numerous well-known making plans paradigms are represented, together with making plans as satisfiability and different version checking concepts, making plans as heuristic state-space seek, and Graph-plan-based methods. additionally, numerous new scheduling ways and combos of making plans and scheduling tools are brought.
Read or Download Recent Advances in AI Planning: 5th European Conference on Planning, ECP’99, Durham, UK, September 8-10, 1999. Proceedings PDF
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However, no formal notion of strong cyclic plan is given in [9] and, as far as we know, in any other work. In this paper we provide a framework for planning via model checking where weak, strong, and strong cyclic plans can be specified uniformly in temporal logic. In the paper, we focus on strong cyclic plans, since their formal specifications and the provision of a correct algorithm is still an open problem at the current stateof-the-art. Indeed, this paper builds on [9] making the following contributions.
Kartha, and V. Lifschitz. Representing action: Indeterminacy and ramifications. Artificial Intelligence, 95(2):409–438, 1997. 11. H. Kautz and B. Selman. BLACKBOX: A New Approach to the Application of Theorem Proving to Problem Solving. In Working notes of the Workshop on Planning as Combinatorial Search, Pittsburgh, PA, USA, June 1998. 12. Henry A. Kautz, David McAllester, and Bart Selman. Encoding Plans in Propositional Logic. In Proc. KR-96, 1996. 13. Henry A. Kautz and Bart Selman. Pushing the Envelope: Planning, Propositional Logic, and Stochastic Search.
For instance, for the BT problem, the variables in x could be Armed, In1 and In2 . A state corresponds to a complete assignment to the variables in x. The assignment {(Armed. )(In1 . ⊥)} (we write Conformant Planning via Model Checking 27 and ⊥ for the true and false truth values) corresponds to the state where the bomb is in package 1, and armed. We use formulae as representatives of the set of their models. Thus, a propositional formula in the variables in x, written φ(x), represents the set of the states corresponding to the assignments which make φ true.