CAREER: Penalty Logic for Structured Machine Learning

职业：结构化机器学习的惩罚逻辑

• 批准号: 0546867
• 负责人: Alan Fern
• 金额: $ 50万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546867&HistoricalAwards=false
• 关键词: CAREER; Penalty; Logic; Structured; Machine

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.

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.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。例子包括学习映射美式足球视频播放描述，并映射多代理规划问题的联合代理行动的状态。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来显式表示，惩罚逻辑模型是加权逻辑公式的集合，其中每个权重表示违反公式的成本。惩罚逻辑模型允许线性成本函数的鲁棒训练方法和基于逻辑的表示的多年工作的协同组合。该项目将研究在四个方向上利用惩罚逻辑：（1）学习模型结构，（2）实现实际有效的推理，（3）整合人类提供的知识，以及（4）通过主动学习减少标记工作。这项工作的更广泛影响将是推进结构化机器学习对广泛的解释和决策问题的适用性，包括上述问题。计划中的教育活动包括为俄勒冈州高中生发起年度竞赛，旨在提高计算机科学的入学率和对人工智能的兴趣。