Constructing Conditional Plans by a Theorem-Prover

TL;DR

This paper introduces a novel approach to conditional planning by translating it into quantified Boolean formulae and utilizing a theorem-prover, addressing the complexities of planning under uncertainty.

Contribution

It presents three formalizations of conditional planning as quantified Boolean formulae and demonstrates their effectiveness through experimental results with a theorem-prover.

Findings

Successful translation of conditional planning into quantified Boolean formulae

Experimental validation shows the approach's feasibility

Addresses computational challenges in conditional planning

Abstract

The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of operations that achieve the goals depend on the initial state and the outcomes of nondeterministic changes in the system. This setting raises the questions of how to represent the plans and how to perform plan search. The answers are quite different from those in the simpler classical framework. In this paper, we approach conditional planning from a new viewpoint that is motivated by the use of satisfiability algorithms in classical planning. Translating conditional planning to formulae in the propositional logic is not feasible because of inherent computational limitations. Instead, we translate conditional planning to quantified Boolean formulae. We discuss three formalizations of conditional planning as quantified Boolean formulae, and present experimental results obtained with a theorem-prover.