Probabilistic plan recognition for proactive assistant agents

Jean Hyaejin Oh, Felipe Meneguzzi and Katia Sycara
Journal Article, Elsevier, Waltham MA, January, 2014

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Human users dealing with multiple objectives in a complex environment, e.g., mili- tary planners or emergency response operators, are subject to a high level of cognitive workload. When this load becomes an overload, it can severely impair the quality of the plans created. To address these issues, intelligent assistant systems have been rigor- ously studied in both the AI and the intelligent systems research communities. In this chapter, we discuss proactive assistant systems that predict future user activities that can be facilitated by the assistant. We focus on problems in which a user is solving a complex problem with uncertainty, and thus on plan recognition algorithms suitable for the target problem domain. Specifically, we discuss a generative model of plan recog- nition that represents user activities as an integrated planning and execution problem. We describe a proactive assistant agent architecture and its applications in practical problems including emergency response and military peacekeeping operations.

author = {Jean Hyaejin Oh and Felipe Meneguzzi and Katia Sycara},
title = {Probabilistic plan recognition for proactive assistant agents},
journal = {Elsevier, Waltham MA},
year = {2014},
month = {January},
keywords = {plan recognition, proactive assistants, intelligent agents, prognostic normative assistance},
} 2017-09-13T10:39:07-04:00