Learning Non-Deterministic Multi-Agent Planning Domains

In this paper, we present an algorithm for learning nondeterministic multi-agent planning domains from execution examples. The algorithm uses a master-slave decomposition of two population-based stochastic local search algorithms and integrates binary decision diagrams to reduce the size of the search space. Our experimental results show that the learner has high convergence rates due to an aggressive exploitation of example-driven search and an efficient separation of concurrent activities. Moreover, even though the learning problem is at least as hard as learning disjoint DNF formulas, large domains can be learned accurately within a few minutes.