U.S.-U.K. Cooperative Research: Constructing Diagnostic Explanations Using Schema-Structured Bayesian Networks

9900485 Luger这个为期三年的奖项支持新墨西哥大学的George Luger和爱丁堡大学认知神经科学和智能系统实验室的Brendan McGonigle在知识和认知系统方面的美英合作研究。他们的研究包括开发诊断推理的概率模型，以及测试由美国调查人员开发的新算法。这两个小组都关注通过错误识别和故障恢复来提高机器人的性能。研究人员提出，通过在机器人的环境中嵌入溯因推理，迭代性能将得到改善。这位美国调查人员将基于模式的绑架方面的专业知识带到了这次合作中，这是一种使用贝叶斯网络作为底层表示的因果推理形式。这与爱丁堡集团在智能系统方面的专业知识相辅相成，并利用他们的游牧机器人进行学习实验。与爱丁堡小组的合作为研究美国调查人员提出的诊断推理模型的认知合理性提供了机会。

9900485LugerThis three-year award supports US-UK collaborative research in knowledge and cognitive systems between George Luger of the University of New Mexico and Brendan McGonigle at the University of Edinburgh's Laboratory for Cognitive Neuroscience and Intelligent Systems. Their research involves the development of probabilistic models of diagnostic reasoning and testing of new algorithms developed by the US investigator. Both groups are concerned with performance improvement of robots through error identification and failure recovery. The investigators propose that iterative performance will improve by embedding abductive reasoning in the robot's environment. The US investigator brings to this collaboration expertise in schema-based abduction, a form of causal reasoning, which employs Bayesian Networks as the underlying representation. This is complemented by the Edinburgh group's expertise in intelligent systems and takes advantage of learning experiments utilizing their NOMAD robot. Collaboration with the Edinburgh group provides an opportunity to study the cognitive plausibility of the US investigator's proposed model of diagnostic reasoning.