Tim Oates, Paul R. Cohen
Providing a complete and accurate domain model for an agent situated in a complex environment can be an extremely difficult task. Actions may have different effects depending on the context in which they are taken, and actions may or may not induce their intended effects, with the probability of success again depending on context. We present an algorithm for automatically learning planning operators with context-dependent and probabilistic effects in environments where exogenous events change the state of the world. Empirical results show that the algorithm successfully finds operators that capture the true structure of an agent’s interactions with its environment, and avoids spurious associations between actions and exogenous events.