TY - CHAP
T1 - Probabilistic Plan Recognition for Proactive Assistant Agents
AU - Oh, Jean
AU - Meneguzzi, Felipe
AU - Sycara, Katia
N1 - Acknowledgment
The research for this chapter was sponsored by the U.S. Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. government. The U.S. government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation hereon.
PY - 2014
Y1 - 2014
N2 - Human users dealing with multiple objectives in a complex environment (e.g., military 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 rigorously studied in both the artificial intelligence (AI) and the intelligent systems research communities. This chapter discusses proactive assistant systems, which 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 recognition 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.
AB - Human users dealing with multiple objectives in a complex environment (e.g., military 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 rigorously studied in both the artificial intelligence (AI) and the intelligent systems research communities. This chapter discusses proactive assistant systems, which 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 recognition 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.
UR - http://store.elsevier.com/Plan-Activity-and-Intent-Recognition/isbn-9780123985323/
U2 - 10.1016/B978-0-12-398532-3.00011-7
DO - 10.1016/B978-0-12-398532-3.00011-7
M3 - Chapter
SN - 978-0123985323
SP - 275
EP - 288
BT - Plan, Activity, and Intent Recognition
PB - Elsevier Science
ER -
