Learning action models for multi-agent planning

In multi-agent planning environments, action models for each agent must be given as input. However, creating such action models by hand is difficult and time-consuming, because it requires formally representing the complex relationships among different objects in the environment. The problem is compounded in multi-agent environments where agents can take more types of actions. In this paper, we present an algorithm to learn action models for multi-agent planning systems from a set of input plan traces. Our learning algorithm Lammas automatically generates three kinds of constraints: (1) constraints on the interactions between agents, (2) constraints on the correctness of the action models for each individual agent, and (3) constraints on actions themselves. Lammas attempts to satisfy these constraints simultaneously using a weighted maximum satisfiability model known as MAX-SAT, and converts the solution into action models. We believe this to be one of the first learning algorithms to learn action models in the context of multi-agent planning environments. We empirically demonstrate that Lammas performs effectively and efficiently in several planning domains.