Proceedings Abstracts of the Twenty-Third International Joint Conference on Artificial Intelligence

Fair LTL Synthesis for Non-Deterministic Systems Using Strong Cyclic Planners / 2343
Fabio Patrizi, Nir Lipovetzky, Hector Geffner

We consider the problem of planning in environments where the state is fully observable, actions have non-deterministic effects, and plans must generate infinite state trajectories for achieving a large class of LTL goals. More formally, we focus on the control synthesis problem under the assumption that the LTL formula to be realized can be mapped into a deterministic B├╝chi automaton. We show that by assuming that action non-determinism is fair, namely that infinite executions of a non-deterministic action in the same state yield each possible successor state an infinite number of times, the (fair) synthesis problem can be reduced to a standard strong cyclic planning task over reachability goals. Since strong cyclic planners are built on top of efficient classical planners, the transformation reduces the non-deterministic, fully observable, temporally extended planning task into the solution of classical planning problems. A number of experiments are reported showing the potential benefits of this approach to synthesis in comparison with state-of-the-art symbolic methods.