A formal theory of plan recognition
A formal theory of plan recognition
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
The Journal of Machine Learning Research
Location-based activity recognition
Location-based activity recognition
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Multiple-goal recognition from low-level signals
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Training conditional random fields using virtual evidence boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Cross-domain activity recognition
Proceedings of the 11th international conference on Ubiquitous computing
Context-aware middleware for pervasive elderly homecare
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
Activity recognition: linking low-level sensors to high-level intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
An unsupervised approach to activity recognition and segmentation based on object-use fingerprints
Data & Knowledge Engineering
Activity-centric support for weakly-structured business processes
Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems
Extending event-driven experiments for human activity for an assistive environment
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Three challenges in data mining
Frontiers of Computer Science in China
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Cross-domain activity recognition via transfer learning
Pervasive and Mobile Computing
Dynamic multi-component based activity detection and recognition within smart homes
Proceedings of the 2011 international workshop on Situation activity & goal awareness
GPARS: a general-purpose activity recognition system
Applied Intelligence
Pervasive and Mobile Computing
ARHMAM: an activity recognition system based on hidden Markov minded activity model
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Forecasting complex group behavior via multiple plan recognition
Frontiers of Computer Science in China
Metaphorical design of feedback interfaces in activity-aware ambient assisted-living applications
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
Forecasting the behavior of an elderly using wireless sensors data in a smart home
Engineering Applications of Artificial Intelligence
Affective and cognitive design for mass personalization: status and prospect
Journal of Intelligent Manufacturing
Latent-Dynamic Conditional Random Fields for recognizing activities in smart homes
Journal of Ambient Intelligence and Smart Environments - Ambient and Smart Component Technologies for Human Centric Computing
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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 advantage in that we are able to reason about goal interactions explicitly through the correlation graph. The two-level framework also avoids the high training complexity when modeling concurrency and interleaving together in a unified CRF model. Experimental results show that our method can effectively improve recognition accuracies on several real-world datasets collected from various wireless and sensor networks.