Abstrakt
Automated Planning seeks to find a sequence of actions, a plan, transforming the environment from its initial state to some goal state. In real-world environments, however, exogenous events might occur and might modify the environment without agent's consent.
Besides disrupting agent's plan, events might hinder agent's pursuit towards its goals and even cause damage (e.g. destroying the robot). In this paper, we present how planning problems with non-deterministic events can be translated into Fully-Observable Non-Deterministic (FOND) planning problems and hence we can exploit FOND planning engines to solve them (i.e., find strong cyclic plans).
Specifically, we will consider two cases in a single agent scenario - at most one event can occur after agent's action or a set of independent events can occur after agent's action. We compare the FOND-based approach with a traditional planning/execution/replanning one to highlight the performance gap as well as success rate of the latter approach.