Probabilistic Methods in Dynamic Environments

我们提出了人工智能中贝叶斯表示和推理方法的研究，特别是在动态环境下的决策。动态环境为智能系统研究提供了机遇和挑战：在这样的环境中表现良好所需的灵活性很可能需要人工智能方法，但人工智能研究传统上更侧重于静态环境。本研究是建立在我们目前对贝叶斯网络推理的研究基础上，重点关注实时推理和动态表示构建。这些结果将有助于我们理解决策理论技术在动态环境中的问题和潜力，并将为复杂的实时监测、评估和控制应用提供决策理论基础。

We propose research on Bayesian representation and inference methods in AI, focused especially on decision-making in dynamic environments. Dynamic environments provide both an opportunity and a challenge to intelligent systems research: the flexibility required to perform well in such environments will most likely demand AI methods, but AI research traditionally has focused on more static environments. The proposed research builds on our current research on inference in Bayes nets, and focuses on real- time inference and dynamic representation construction. The results will contribute to our understanding of the problems of and potential for decision-theoretic techniques in dynamic environments, and will provide a decision-theoretic foundation for complex real-time monitoring, assessment, and control applications.