CPT-FDR: An Approach to Translating PPDDL Conformant Planning Tasks into Finite-Domain Representations

Getting a compact representation of belief spaces is one of the most important issues in a conformant planning task. In this paper, a translation approach to Conformant planning tasks in a Finite-domain representation, noted the CPT-FDR, is studied to translate conformant planning tasks specified in PPDDL formalism into a concise grounded representation that uses finite-domain state variables. It is extended for the semantic of the non-deterministic effects, fluents, axioms, and extended belief states. The FDR-based translation algorithm employs several techniques that can deal with the uncertainty in the initial state and in the non-deterministic operator effects. The experimental results show that the approach can map the conformant planning tasks in the PPDDL into finite-domain representation, and CPT-FDRs can save the memory space and reduce the size of belief states effectively.