Agent Planning Programs as Non-deterministic Planning under Fairness

Proceedings of the International Conference on Automated Planning and Scheduling, vol. 35, pp. 358–366

Abstract

We propose an approach for solving Agent Planning Programs (APP) based on a reduction to (strong-cyclic) Fully Observable Non-Deterministic (FOND) planning. APPs represent a middle-ground between automated planning and agent-oriented programming, in which the space of possible agent behavior is "programmed" as a network of declarative goals wrt an underlying planning domain. Each transition in an APP represents a local planning problem that may need to be addressed by the agent executing the APP. APPs allow the specification of continuous goal-driven behavior in which the "next" goal is externally chosen, thus going beyond one-shot planning. Two methods have been proposed for solving APPs: a principled but inefficient LTL reactive synthesis technique; and a more efficient but arguably ad-hoc approach that relies on multiple "local" classical planning and meta-level backtracking. We demonstrate how APPs can be solved in a principled manner by developing an elegant reduction to non-deterministic planning under fairness assumptions, and show experimentally with existing FOND solvers its practical value. We also provide a new solution concept that is simpler and closer to mainstream planning than the existing one.

Authors 3

1. Nitin Yadav

   The University of Melbourne

   Affiliation as printed

   University of Melbourne

2. Sebastian Sardiña

   RMIT University

   Affiliation as printed

   Royal Melbourne Institute of Technology

3. Héctor Geffner Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University