Transition Dominance in Domain-Independent Dynamic Programming

Domain-independent dynamic programming (DIDP) is a model-based paradigm for dynamic programming (DP) that enables users to define DP models based on a state transition system. Heuristic search-based solvers have demonstrated strong performance in solving combinatorial optimization problems. In this paper, we formally define transition dominance in DIDP, where one transition consistently leads to better solutions than another, allowing the search process to safely ignore dominated transitions. To facilitate the efficient use of transition dominance, we introduce an interface for defining transition dominance and propose the use of state functions to cache values, thereby avoiding redundant computations when verifying transition dominance. Experimental results on DP models across multiple problem classes indicate that incorporating transition dominance and state functions yields a 5 to 10 times speed-up on average for different search algorithms within the DIDP framework compared to the baseline.