Agent planning programs are finite-state programs, possibly containing loops, whose atomic instructions consist of a guard, a maintenance goal, and an achievement goal, which act as precondition-invariance-postcondition assertions in program specification. The execution of such programs requires generating plans that meet the goals specified in the atomic instructions, while respecting the program control flow. Recently, De Giacomo et al. (2016) presented a technique, based on iteratively solving classical planning problems with action costs, for realizing planning programs in deterministic domains. Such a technique works generally well for domains with no or very few dead-end states. In this paper, we propose an enhancement of this technique to handle deterministic domains that have potentially many dead-end states, and we study the effectiveness of our technique through an experimental analysis.

Planning programs are loose, high-level, declarative representations of the behavior of agents acting in a domain and following a path of goals to achieve. Such programs are specified through transition systems that can include cycles and decisions to make at certain points. We investigate a new effective approach for solving the problem of realizing a planning program, i.e., informally, for finding and combining a collection of plans that guarantee the planning program executability. We focus on deterministic domains and propose a general algorithm that solves the problem exploiting a planning technique handling goal constraints and preferences. A preliminary experimental analysis indicates that our approach dramatically outperforms the existing method based on formal verification and synthesis techniques.