CAREER: Penalty Logic for Structured Machine Learning

建议0546867《职业生涯：结构化机器学习的惩罚逻辑》PI：Alan FernOregon州立大学本研究将研究惩罚逻辑作为结构化机器学习的知识表示技术。这样的学习问题涉及在结构化数据类型之间引入复杂的映射。例如，学习将美式足球视频映射到比赛描述，以及将多代理规划问题的状态映射到联合代理行动。这类问题通常包含许多“近乎合理”的逻辑约束，这些约束通常是正确的，但有时会被违反。这些约束可以使用惩罚逻辑模型显式表示，惩罚逻辑模型是加权逻辑公式集，其中每个权重表示违反公式的成本。惩罚逻辑模型允许将线性成本函数的稳健训练方法与多年基于逻辑的表示法的工作协同结合。该项目将从四个方向研究惩罚逻辑的杠杆作用：(1)学习模型结构，(2)实现实际有效的推理，(3)结合人类提供的知识，(4)通过主动学习减少标记工作量。这项工作的更广泛影响将是将结构化机器学习的适用性推进到包括上述问题在内的广泛的解释和决策问题中。计划的教育活动包括为俄勒冈州的高中生发起一年一度的竞赛，旨在增加CS入学人数和对人工智能的兴趣。

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.