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

随着人类在解决问题方面获得专业知识，他们越来越依赖于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，然后将这些知识与其他高级推理结合起来做出决策。这项研究开发了一种联想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。这些组件可以很容易地集成到一个多智能体决策程序中，用于解决广泛类别的相关问题。预先存在的计划强有力地结合了具有相互冲突的观点的代理人，并经受住了早期学习阶段的不完整和错误的证据。它通过代表个体的、有限理性的启发式代理的模块层次结构来做出决策。每个代理可以利用由各种学习策略生成和维护的各种知识库。新的视觉模式分类器从经验中学习有意义的模式。此外，从这些模式中概括了特定于领域的空间概念，以创建新的启发式代理。

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.