Belief change in knowledge-based agents

在人工智能的知识表示中，代理的知识通常是不完整的，不准确的，并且会随着时间的推移而发展。 无论代理是物理域中的机器人，还是网络上的软件代理，还是处理医学信息语料库的诊断代理，情况都是如此。一方面，智能体必须能够整合新的信息，可能是纠正或填补其错误或不完整的知识。另一方面，代理人必须尽可能地处理它所拥有的信息，通过得出合理的结论或处理不完整的信息。 我的研究计划集中在这些问题上，特别是在管理信念的变化和对可废止和优先推理的密切相关的问题。虽然在这些领域开展了大量工作，但仍然存在重大挑战：有些根本问题尚未得到充分解决或没有得到充分理解;信念改变理论在实际环境中的应用有限;在动态环境中发展信念改变理论的工作仍然存在。 研究在以前的补助金期间集中在信念的变化，重点是非经典逻辑和经典逻辑的片段，并在行动域的信念变化。 在下一个资助期，我将主要关注信念的改变，同时也讨论使用可废止推理处理不完整知识的相关主题。 将开展三个相互关联的项目。 第一个涵盖了信仰改变的形式方面及其在现实环境中的应用。 我将研究处理（ir）相关信息和在一阶环境中修正的具体问题;这可以说是信念改变中两个未解决（或未完全解决）的主要问题。 我也将继续在描述逻辑（基本上是一阶逻辑的片段）中应用信念变化的工作，目标是开发一个应用程序，用于更新基于“常识”原则的大型知识库（暂称为SNOMED CT）。 第二个项目将解决定性，可废止，推理的主题：一种新的方法，非单调推理将开发的目的是适合描述逻辑;第二，基于偏好的方法道义推理，处理指示代理它应该做什么，将开发和实施。 第三个主题继续工作，从以前的补助金期间，其目标是提供一个定性的，高层次的规范代理在行动域的行动可能会失败或有意外或不确定的影响，传感器可能不可靠，代理的信念可能是不正确的。

In Knowledge Representation in Artificial Intelligence, an agent's knowledge will generally be incomplete, inaccurate, and will evolve over time. This will be the case whether the agent is a robot in a physical domain, or a software agent on the web, or a diagnostic agent dealing with a corpus of medical information. On the one hand, an agent must be able to incorporate new information, perhaps correcting or filling in its erroneous or incomplete knowledge. On the other hand, an agent must deal as best it can with the information it has, by drawing plausible conclusions or otherwise dealing with incomplete information. My research program focusses on these issues, specifically on managing belief change and on the closely-related issues of defeasible and preferential reasoning. While a great deal of work has been carried out in these areas, significant challenges remain: there are fundamental issues that have not been fully addressed or that are not fully understood; applications of belief change theories in practical settings are limited; and work remains in developing belief change theories within a dynamic environment. Research in the previous grant period focussed on belief change, with emphasis on non-classical logics and fragments of classical logic, and on belief change in an action domain. For the next grant period, I will focus primarily on belief change, while also addressing the related topic of using defeasible reasoning for dealing with incomplete knowledge. Three interrelated projects will be undertaken. The first covers formal aspects of belief change and its application in realistic settings. I will look at the specific problems of dealing with (ir)relevant information and in revising in a first-order setting; these are arguably the two major unaddressed (or incompletely addressed) problems in belief change. I also will continue ongoing work in applying belief change in description logics (essentially, fragments of first-order logic), where the goal will be to develop an application for updating a large knowledge base (tentatively SNOMED CT) based on "commonsense" principles. The second project will address topics in qualitative, defeasible, reasoning: a new approach to nonmonotonic reasoning will be developed that is intended to be suitable for description logics; and second, a preference-based approach to deontic reasoning, dealing with instructing an agent what it should do, will be developed and implemented. The third topic continues work from the previous grant period, where the goal is to provide a qualitative, high-level specification of an agent in an action domain where actions may fail or have unanticipated or nondeterministic effects, sensors may not be reliable, and the agent's beliefs may be incorrect.