Probabilistic Relational Planning with First Order Decision Diagrams
This work addresses the problem of efficient relational stochastic planning for AI researchers, representing an incremental advancement by enhancing an existing FODD algorithm to achieve feasibility.
The paper tackled the challenge of making first-order decision diagrams (FODD) practical for relational stochastic planning by introducing improvements like reduction operators and speedup techniques, resulting in a system, FODD-Planner, that shows competitive performance with top systems in evaluations including international planning competition problems.
Dynamic programming algorithms have been successfully applied to propositional stochastic planning problems by using compact representations, in particular algebraic decision diagrams, to capture domain dynamics and value functions. Work on symbolic dynamic programming lifted these ideas to first order logic using several representation schemes. Recent work introduced a first order variant of decision diagrams (FODD) and developed a value iteration algorithm for this representation. This paper develops several improvements to the FODD algorithm that make the approach practical. These include, new reduction operators that decrease the size of the representation, several speedup techniques, and techniques for value approximation. Incorporating these, the paper presents a planning system, FODD-Planner, for solving relational stochastic planning problems. The system is evaluated on several domains, including problems from the recent international planning competition, and shows competitive performance with top ranking systems. This is the first demonstration of feasibility of this approach and it shows that abstraction through compact representation is a promising approach to stochastic planning.