Heuristic Solution Approaches for Models of Sequential Decisionmaking Under Uncertainty and Risk

本研究将探讨应用启发式搜索算法求解各类马尔可夫决策过程的有效性，启发式搜索算法是一类重要的人工智能问题求解技术。研究的目的是提高这些不确定条件下的序贯决策模型的可操作性。预计这项研究将产生一大类启发式搜索算法所需的扩展，并建议将启发式搜索中的概念与与随机控制和决策科学相关的概念相结合的潜在领域。

This research will investigate the usefulness of applying heuristic search algorithms, an important class of artificial intelligence problem solving techniques, to solve various types of Markov decision processes. The aim of the research is to enhance the tractability of these models of sequential decision making under uncertainty. It is anticipated that the research will produce needed extensions of a wide class of heuristic search algorithms as well as suggesting potential areas for integrating concepts in heuristic search with concepts associated with stochastic control and the decision sciences.