The Integration of Visual Perceptual Reasoning with a Multi-Agent, Decision-Making Expert

视觉感知推理与多智能体决策专家的集成

• 批准号: 9423085
• 负责人: Susan Epstein
• 金额: $ 36.98万
• 依托单位: CUNY Hunter College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-01 至 2000-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9423085&HistoricalAwards=false
• 关键词: Integration; Visual; Perceptual; Reasoning; Multi

As they gain expertise in problem solving, humans increasingly rely on patterns and spatially-oriented reasoning. The goals of this project are to learn pattern associations and spatially-oriented reasoning, and then to integrate the knowledge to make decisions in conjunction with other high-level reasoning. This research develops an associative visual pattern classifier and algorithms for the automated acquisition of new, spatially-oriented reasoning agents. These components are readily integrated into a multi-agent decision making program for broad classes of related problems. The pre-existing program robustly combines agents with conflicting perspectives and withstands incomplete and erroneous evidence for the early stages of learning. It makes decisions with a hierarchy of modules representing individual, limitedly rational, heuristic agents. Each agent may draw upon a variety of knowledge bases generated and maintained by various learning strategies. The new visual pattern classifier learns meaningful patterns from experience. In addition, domain-specific spatial concepts are generalized from these patterns to create new heuristic agents.

作为 他们 增益 解决问题的专业知识， 人类越来越多 依赖 模式和 面向空间的推理 这个项目的目标是学习模式关联和面向空间的推理，然后 结合其他高级推理来整合决策知识。本研究开发了一个关联的视觉模式分类器和算法，用于自动获取新的，面向空间的推理代理。 这些组件很容易集成到一个多智能体决策程序的广泛类的相关问题。预先存在的程序将代理人 与 矛盾的观点 和 在学习的早期阶段能够承受不完整和错误的证据。 它通过一个模块层次结构来做出决策， 个人的，有限理性的， 启发式代理。 每个代理可以利用各种知识库 生成 和维护 各种 学习策略 新的视觉模式分类器 学习有意义 模式 从 体验. 在 此外，领域特定的空间概念从这些模式中概括出来，以创建新的启发式代理。