  Planning agents are ill-equipped to act in novel situations in which their
domain model no longer accurately represents the world. We introduce an
approach for such agents operating in open worlds that detects the presence of
novelties and effectively adapts their domain models and consequent action
selection. It uses observations of action execution and measures their
divergence from what is expected, according to the environment model, to infer
existence of a novelty. Then, it revises the model through a heuristics-guided
search over model changes. We report empirical evaluations on the CartPole
problem, a standard Reinforcement Learning (RL) benchmark. The results show
that our approach can deal with a class of novelties very quickly and in an
interpretable fashion.

Artificial Intelligence;Machine Learning;Symbolic Computation