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One important challenge for a set of agents to achieve more efficientcollaboration is for these agents to maintain proper models of each other. Animportant aspect of these models of other agents is that they are often partialand incomplete. Thus far, there are two common representations of agent models:MDP based and action based, which are both based on action modeling. In manyapplications, agent models may not have been given, and hence must be learnt.While it may seem convenient to use either MDP based or action based models forlearning, in this paper, we introduce a new representation based on capabilitymodels, which has several unique advantages. First, we show that learningcapability models can be performed efficiently online via Bayesian learning,and the learning process is robust to high degrees of incompleteness in planexecution traces (e.g., with only start and end states). While high degrees ofincompleteness in plan execution traces presents learning challenges for MDPbased and action based models, capability models can still learn to {\emabstract} useful information out of these traces. As a result, capabilitymodels are useful in applications in which such incompleteness is common, e.g.,robot learning human model from observations and interactions. Furthermore,when used in multi-agent planning (with each agent modeled separately),capability models provide flexible abstraction of actions. The limitation,however, is that the synthesized plan is incomplete and abstract.

Learning of Agent Capability Models with Applications in Multi-agent Planning