Flexible FOND Planning with Explicit Fairness Assumptions
This work addresses the need for more versatile fairness constraints in planning for AI systems, though it appears incremental as it builds on and combines existing models.
The paper tackles the problem of flexible fully-observable non-deterministic (FOND) planning by introducing explicit fairness assumptions, showing that this approach generalizes existing models like strong and strong-cyclic FOND planning and QNP planning. It implements a new planner via reduction to answer set programs and evaluates performance against FOND, QNP planners, and LTL synthesis tools.
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.