CIGAR: Concurrent and Interleaving Goal and Activity Recognition

In artificial intelligence and pervasive computing research, inferring users' high-level goals from activity sequences is an important task. A major challenge in goal recognition is that users often pursue several high-level goals in a concurrent and interleaving manner, where the pursuit of goals may spread over different parts of an activity sequence and may be pursued in parallel. Existing approaches to recognizing multiple goals often formulate this problem either as a single-goal recognition problem or in a deterministic way, ignoring uncertainty. In this paper, we propose CIGAR (Concurrent and Interleaving Goal and Activity Recognition) - a novel and simple two-level probabilistic framework for multiple-goal recognition where we can recognize both concurrent and interleaving goals. We use skip-chain conditional random fields (SCCRF) for modeling interleaving goals and we model concurrent goals by adjusting inferred probabilities through a correlation graph, which is a major...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Derek Hao Hu, Qiang Yang
• Keywords: AAAI 2008, Activity Sequence, Intelligent Agents, Interleaving Goals, Interleaving Manner