Non-Deterministic Planning With Conditional Effects

Abstract

Recent advances in fully observable non-deterministic (FOND) planning have enabled new techniques for various applications, such as behaviour composition, among others. One key limitation of modern FOND planners is their lack of native support for conditional effects. In this paper we describe an extension to PRP, the current state of the art in FOND planning, that supports the generation of policies for domains with conditional effects and non-determinism. We present core modifications to the PRP planner for this enhanced functionality without sacrificing soundness and completeness. Additionally, we demonstrate the planner's capabilities on a variety of benchmarks that include actions with both conditional effects and non-deterministic outcomes. The resulting planner opens the door to models of greater expressivity, and does so without affecting PRP's efficiency.

Synthesis of Procedural Models for Deterministic Transition Systems

“…STRIPS. We synthesize 49 action schemes from 13 domains (FD benchmarks 3 and PLANNING.DOMAINS [39], ranging from Gripper to Transport in Table 2). For each domain, examples are generated using 10 IPC instances and computing: (i), one random walk per instance of at most 50 steps with TARSKI [20] and (ii), solution plans for those instances with the LAMA [44] setting of FD [27].…”

On timeline-based games and their complexity

“…In the meantime, the action-based planning community studied how to handle general nondeterminism quite extensively in the past years, following different approaches such as, for instance, reactive planning systems [4], deductive planning [45], model checking [20], and, especially, fully observable nondeterministic planning (FOND planning) [7,37,38,42]. However, these approaches to nondeterministic action-based planning do not support flexible plans and temporal uncertainty, and do not account for controllability issues.…”

Stubborn Sets for Fully Observable Nondeterministic Planning

“…In addi- tion, approaches that generalize classical planning concepts to FOND planning are particularly related to our work. For example, conditional effects for FOND planning (Muise, McIlraith, and Belle 2014), or pattern databases (Mattmüller et al 2010).…”