The Automated Mapping of Plans for Plan Recognition
This work addresses the challenge of modeling other agents' intentions in multi-agent systems, but it appears incremental as it builds on existing plan recognition approaches by introducing a new mapping technique.
The paper tackles the problem of plan recognition for agents in environments where communication is limited, by developing methods to convert plans from a procedural language into probabilistic belief networks for inference. The result is a uniform procedure that supports uncertain and incomplete observations, though no concrete numbers are provided.
To coordinate with other agents in its environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with a specification of the possible plans the other agents may be following, and develop special techniques for discriminating among the possibilities. Perhaps more desirable would be a uniform procedure for mapping plans to general structures supporting inference based on uncertain and incomplete observations. In this paper, we describe a set of methods for converting plans represented in a flexible procedural language to observation models represented as probabilistic belief networks.