Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Statistical Language Learning
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Multivariate Clustering by Dynamics
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An interval-based representation of temporal knowledge
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Specific-to-general learning for temporal events
Eighteenth national conference on Artificial intelligence
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining temporal patterns of movement for video content classification
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
Data mining with Temporal Abstractions: learning rules from time series
Data Mining and Knowledge Discovery
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
A multi threaded and resolution approach to simulated futures evaluation
Proceedings of the 40th Conference on Winter Simulation
Journal of Artificial Intelligence Research
Structuring ordered nominal data for event sequence discovery
Proceedings of the international conference on Multimedia
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Fluents are logical descriptions of situations that persist, and composite fluents are statistically significant temporal relationships between fluents. This paper presents an algorithm for learning composite fluents incrementally from categorical time series data. The algorithm is tested with a large dataset of mobile robot episodes. It is given no knowledge of the episodic structure of the dataset (i.e., it learns without supervision) yet it discovers fluents that correspond well with episodes.