SGER: An Architecture of Diversity for Common Sense Reasoning

SGER：常识推理的多样性架构

• 批准号: 0450770
• 负责人: Walter Bender
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2005-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0450770&HistoricalAwards=false
• 关键词: SGER; Architecture; Diversity; Common; Sense

Artificial Intelligence research has made limited progress towards computational understanding of human reasoning: we have failed to emphasize the creation of programs with "resourcefulness" in the face of unfamiliar problems. In computing theory and applications the emphasis has largely been on specific algorithms and mathematical techniques; complex systems have rarely yielded reproducible results or fundamental insights into a theory of system construction. Resourceful programs will require the application of multiple reasoning techniques, a layered architecture for coordinating activity whereby higher-order, reflective processes can diagnose failures in lower-order, deliberative processes.Scenarios requiring "commonsense reasoning" provide an anvil of sufficient scope that only highly-resourceful programs will meet the requirements. Two projects support these aims: Roboverse establishes a virtual world where humanoid characters engage in complex, multi-realm scenarios, applying common sense to physical, social and mental activities; Panalogy is a specification for interconnect among multiple representations, combining diverse reasoning techniques such as analogy, case-based reasoning, statistical analysis and logical inference. An early version of Panalogy instantiates a set of meta-management components, demonstrating different ways to coordinate and repair baseline reasoning processes.These resources enable wider research-community involvement by providing motivating demonstrations of new theoretical principles and evaluation tools and methodologies for experimental reproduction and comparison. We aim, through workshops, publications and direct engagement to encourage broader support from corporate and governmental sources. This increased effort in resourceful computing research leads the way towards programs that engage readily with humans in the search for solutions to complex problems of importance to science and society.

人工智能研究在人类推理的计算理解方面取得了有限的进展：我们未能强调在面对不熟悉的问题时创建具有“足智多谋”的程序。在计算理论和应用中，重点主要放在特定的算法和数学技术上;复杂系统很少产生可重复的结果或对系统构造理论的基本见解。资源丰富的程序将需要应用多种推理技术，这是一种用于协调活动的分层体系结构，通过这种体系结构，高阶的反射过程可以诊断低阶的深思熟虑过程中的故障。需要“常识推理”的场景提供了足够范围的铁砧，只有资源丰富的程序才能满足要求。两个项目支持这些目标：Roboverse建立了一个虚拟世界，其中人形角色参与复杂的多领域场景，将常识应用于物理，社会和心理活动; Panalogy是多种表示之间的互连规范，结合了多种推理技术，如类比，基于案例的推理，统计分析和逻辑推理。Panalogy的早期版本实例化了一组元管理组件，展示了协调和修复基线推理过程的不同方法。这些资源通过提供新的理论原理和评估工具以及实验复制和比较方法的激励演示，使更广泛的研究社区参与。我们的目标是通过研讨会、出版物和直接参与，鼓励企业和政府提供更广泛的支持。在资源丰富的计算研究中，这种不断增加的努力引领了一条道路，即在寻找对科学和社会具有重要意义的复杂问题的解决方案时，可以随时与人类接触。