Multi-Agent Plan Recognition: Formalization and Algorithms

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents, based on a library of known team-activities (plan library). It has important applications in analyzing data from automated monitoring, surveillance, and intelligence analysis in general. In this paper, we formalize MAPR using a basic model that explicates the cost of abduction in single agent plan recognition by "flattening" or decompressing the (usually compact, hierarchical) plan library. We show that single-agent plan recognition with a decompressed library can be solved in time polynomial in the input size, while it is known that with a compressed (by partial ordering constraints) library it is NP-complete. This leads to an important insight: that although the compactness of the plan library plays an important role in the hardness of single-agent plan recognition (as recognized in the existing...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Bikramjit Banerjee, Landon Kraemer, Jeremy Lyle
• Keywords: AAAI 2010, Intelligent Agents, Plan Library, Plan Recognition, Single-agent Plan Recognition