RI: Small: Knowledge Representation and Reasoning under Uncertainty with Probabilistic Answer Set Programming

结合逻辑和概率是人工智能中的一个重要课题，最近在统计关系学习领域得到了广泛的研究，其中表示的主要目标是以紧凑的方式表达概率模型，反映域的关系结构，并理想地支持有效的学习和推理。然而，与主要的知识表示语言相比，这种语言不允许自然的，精心制作的常识知识的宽容表示。目前，在知识表示中使用的最先进的语言和机器学习中使用的最先进的语言之间存在很大的差距。 该项目的成功将确定弥合这两个领域之间差距的基本问题，将为表达表示和学习提供统一的框架，并将有助于知识表示和机器学习的集成。研究的结果将是有用的许多应用程序，需要集成的知识表示和其他领域，如视觉，机器人和事件识别，常识推理必须应用于不确定的知识和数据。在该项目下开发的软件系统将作为开放源码软件免费提供。这项研究将涉及研究生和本科生，有助于加强教育和研究之间的关系。该项目的目标是设计和实现一种知识表示语言，允许涉及逻辑和概率的表达性常识知识的详细描述，这些知识可以通过相关领域开发的技术有效地计算。 该研究旨在将当前基于逻辑的答案集编程基础转变为一种结合逻辑和概率的新基础，并通过智能地适应和结合概率推理和机器学习的方法来实现其计算。它将建立在回答集编程，统计关系学习和概率逻辑编程的现有工作。 该项目将（i）将答案集编程的数学基础提高到结合逻辑和概率的新基础。(ii)将其与统计关系学习中的其他现有方法，Pearl的因果模型和P-Log联系起来;（iii）设计推理和学习算法;（iv）设计一种高级动作语言，允许对概率转换系统进行详细说明;（v）将概率答案集编程应用于事件识别;（vi）实现和评估所涉及的软件系统。

Combining logic and probability is an important subject in Artificial Intelligence, and is recently being extensively studied in the area of statistical relational learning, where the main goal of representation is to express probabilistic models in a compact way that reflects the relational structure of the domain and ideally supports efficient learning and inference. However, in comparison with main knowledge representation languages, such languages do not allow natural, elaboration tolerant representation of commonsense knowledge. Currently, there is a big gap between the state of the art languages that are used in knowledge representation and the state of the art languages in which machine learning is done. The success of this project will identify fundamental issues in bridging the gap between the two areas, will produce a uniform framework for both expressive representation and learning, and will contribute to the integration of knowledge representation and machine learning. The outcome of the research will be useful for many applications that require integration of knowledge representation and other areas, such as vision, robotics, and event recognition, where commonsense reasoning has to be applied on uncertain knowledge and data. The software systems developed under this project will be freely available as open source software. The research will involve both graduate and undergraduate students, contributing to a strengthened relationship between education and research. The goal of the project is to design and implement a knowledge representation language that allows elaboration tolerant representation of expressive commonsense knowledge involving logic and probability, which can be efficiently computed by the techniques developed in related areas. The proposed research aims at shifting the current logic-based foundation of answer set programming to a novel foundation that combines logic and probability, and achieving its computation by intelligently adapting and combining the methods from probabilistic reasoning and machine learning. It will build upon the existing works on answer set programming, statistical relational learning, and probabilisitic logic programming. The project will (i) enhance the mathematical foundation of answer set programming to the novel foundation that combines logic and probability. (ii) relate it to other existing approaches in statistical relational learning, Pearl's causal models, and P-Log; (iii) design inference and learning algorithms; (iv) design a high level action language that allows elaboration tolerant representation of probabilistic transition systems; (v) apply probabilistic answer set programming to event recognition; (vi) implement and evaluate involved software systems.