Learning models and formulas of a temporal event logic

Alan Fern, Purdue University

Abstract

We study novel learning and inference algorithms for temporal, relational data and their application to trainable video interpretation. With these algorithms we extend an existing visual-event recognition system, Leonard (Siskind 2001), in two directions. First, we develop, analyze, and evaluate a supervised learning algorithm for automatically acquiring high-level visual event definitions from low-level force-dynamic interpretations of video—relieving the user of the need to hand code definitions. We introduce a simple temporal event-description logic called AMA and give algorithms and complexity bounds for the AMA subsumption and generalization problems. A learning method is developed based on these algorithms and applied to the task of learning relational event definitions from video. Experiments show that the learned definitions are competitive with hand-coded ones. Second, we study the problem of relational sequential inference with application to inferring force-dynamic models from video data for use in event learning and recognition. We introduce two frameworks for this problem that provide different approaches to leveraging “nearly sound” logical constraints on a process. We study learning and inference in both frameworks and our empirical results compare favorably to pre-existing hand-coded model reconstructors.

Degree

Ph.D.

Advisors

Givan, Purdue University.

Subject Area

Computer science

COinS