This paper examines methods of decision making that are able to accommodate limitations on both the form in which uncertainty pertaining to a decision problem can be realistically represented and the amount of computing time available before a decision must be made. The methods are anytime algorithms in the sense of Boddy and Dean 1991. Techniques are presented for use with Frisch and Haddawy's [1992] anytime deduction system, with an anytime adaptation of Nilsson's [1986] probabilistic logic, and with a probabilistic database model.
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
Lattice Lab；Toyota Motor Corporation（丰田汽车公司）；Department of Computer Science（计算机科学系）；University of Tokyo（东京大学）；Rational Intelligence Lab（理性智能实验室）；CISPA Helmholtz Center for Information Security（CISPA 欧洲信息安全中心）；University of Manchester（曼彻斯特大学）；Manchester Centre for AI Fundamentals（曼彻斯特人工智能基础中心）；EPIC Lab（EPIC实验室）；College of Computing & Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
Toyota Research Institute of North America（丰田美国研究院）；Toyota Motor North America Frontier Research Center（丰田北美前沿研究中心）；Toyota Motor Corporation（丰田公司）；Toyota Motor North America（丰田北美）
