CAREER: Penalty Logic for Structured Machine Learning

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Proposal 0546867"CAREER: Penalty Logic for Structured Machine Learning"PI: Alan FernOregon State UniversityThis research will study penalty logic as a knowledge representation technique for structured machine learning. Such learning problems involve inducing complex mappings between structured data types. Examples include learning to map American football video to play descriptions, and mapping the state of multi-agent planning problems to joint agent actions. Such problems often contain many "nearly sound" logical constraints, which are generally true, but sometimes violated. These constraints can be explicitly represented using penalty logic models, which are sets of weighted logical formulas, where each weight represents the cost of violating a formula. Penalty-logic models allow the synergistic combination of robust training methods for linear cost functions and years of work on logic-based representations. The project will study leveraging penalty logics in four directions: (1) learning model structure, (2) achieving practically efficient inference, (3) incorporating human provided knowledge, and (4) reducing labeling effort via active learning. The broader impact of this work will be to advance the applicability of structured machine learning to a wide range of interpretation and decision making problems, including those above. Planned educational activities include initiating an annual competition for Oregon high school students aimed at increasing CS enrollment and interest in AI.