Temporal logics in AI: semantical and ontological considerations
Artificial Intelligence
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EWSL-91 Proceedings of the European working session on learning on Machine learning
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Maintaining knowledge about temporal intervals
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A Maximum-Likelihood Approach to Visual Event Classification
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Visual Event Classification via Force Dynamics
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Specific-to-general learning for temporal events
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Specific-to-general learning for temporal events
Eighteenth national conference on Artificial intelligence
A comprehensive study of visual event computing
Multimedia Tools and Applications
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We present and evaluate a novel implemented approach for learning to recognize events in video. First, we introduce a sublanguage of event logic, called k-AMA, that is sufficiently expressive to represent visual events yet sufficiently restrictive to support learning. Second, we develop a specific-to-general learning algorithm for learning event definitions in k-AMA. Finally, we apply this algorithm to the task of learning event definitions from video and show that it yields definitions that are competitive with hand-coded ones.