Flexible FOND Planning with Explicit Fairness Assumptions

TL;DR

This paper introduces FOND+ planning, a flexible approach that incorporates explicit fairness assumptions into FOND planning, unifying several planning models and enhancing expressiveness for generalized planning.

Contribution

It formalizes fairness assumptions within FOND planning, unifies FOND, strong, cyclic, and QNP planning under this framework, and implements a new planner based on answer set programming.

Findings

The FOND+ planner outperforms traditional FOND and QNP planners in various benchmarks.

FOND+ effectively handles fairness constraints that previous models could not.

The approach demonstrates versatility comparable to LTL in expressing fairness.

Abstract

We consider the problem of reaching a propositional goal condition in fully-observable non-deterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A new planner is implemented by reducing FOND+ planning to answer set programs, and the performance of the planner is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools.