Policy Recognition in the Abstract Hidden Markov Model
نویسندگان
چکیده
In this paper, we present a method for recognising an agent’s behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent’s plan. Our contributions in this paper are two fold. In terms of plan recognition, we propose a novel plan recognition framework based on the Abstract Hidden Markov Model (AHMM) as the plan execution model. In terms of probabilistic inference, we introduce the AHMM, a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network model. We describe a novel application of the Rao-Blackwellisation procedure to general DBN, which allows us to construct hybrid inference methods that take advantage of the tractable sub-structures present in the DBN. When applied to the AHMM, we are able to take advantage of the independence properties inherent to a model of action and plan execution and derive an online probabilistic plan recognition algorithm that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms.
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ورودعنوان ژورنال:
- J. Artif. Intell. Res.
دوره 17 شماره
صفحات -
تاریخ انتشار 2002