Son To

AAAI Conference 2011 Conference Paper

Conjunctive Representations in Contingent Planning: Prime Implicates Versus Minimal CNF Formula

• Son To
• Tran Son
• Enrico Pontelli

This paper compares in depth the effectiveness of two conjunctive belief state representations in contingent planning: prime implicates and minimal CNF, a compact form of CNF formulae, which were initially proposed in conformant planning research (To et al. 2010a; 2010b). Similar to the development of the contingent planner CNFct for minimal CNF (To et al. 2011b), the present paper extends the progression function for the prime implicate representation in (To et al. 2010b) for computing successor belief states in the presence of incomplete information to handle non-deterministic and sensing actions required in contingent planning. The idea was instantiated in a new contingent planner, called PIct, using the same AND/OR search algorithm and heuristic function as those for CNFct. The experiments show that, like CNFct, PIct performs very well in a wide range of benchmarks. The study investigates the advantages and disadvantages of the two planners and identifies the properties of each representation method that affect the performance.

AAAI Conference 2011 Conference Paper

On the Effectiveness of Belief State Representation in Contingent Planning

• Son To
• Tran Son
• Enrico Pontelli

AAAI Conference 2010 Conference Paper

On the Use of Prime Implicates in Conformant Planning

• Son To
• Tran Son
• Enrico Pontelli

The paper presents an investigation of the use of two alternative forms of CNF formulae—prime implicates and minimal CNF—to compactly represent belief states in the context of conformant planning. For each representation, we define a transition function for computing the successor belief state resulting from the execution of an action in a belief state; results concerning soundness and completeness are provided. The paper describes a system (PIP) which dynamically selects either of these two forms to represent belief states, and an experimental evaluation of PIP against state-of-the-art conformant planners. The results show that PIP has the potential of scaling up better than other planners in problems rich in disjunctive information about the initial state.