Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video

We develop, analyze, and evaluate a novel, supervised, specific-to-general learner for a simple temporal logic and use the resulting algorithm to learn visual event definitions from video sequences. First, we introduce a simple, propositional, temporal, event-description language called AMA that is sufficiently expressive to represent many events yet sufficiently restrictive to support learning. We then give algorithms, along with lower and upper complexity bounds, for the subsumption and generalization problems for AMA formulas. We present a positive-examples

• Type: Journal
• Year: 2002
• Where: JAIR
• Authors: Alan Fern, Robert Givan, Jeffrey Mark Siskind
• Keywords: Algorithm, Event Definitions, JAIR 2002, Simple Temporal Logic