RI: Small: Expressive Reasoning and Learning about Actions under Uncertainty via Probabilistic Extension of Action Language

动态世界的自动推理是鲁棒智能系统的一项重要能力。动作语言允许以一种基于自然语言的方式描述动态领域中的动作及其效果，但对于基于知识的系统中的建模来说，这种方式足够正式。今天的动作语言不容易允许这样的系统来解释概率和不确定性，这是模拟人类常识性推理所必需的。现有的动作语言在逻辑程序的一次性执行之前也假定了系统的完整规范，这不容易处理连续的数据流。本项目将开发一种基于逻辑与概率相结合的数学基础的动作语言。该研究将把逻辑人工智能的表示和推理优势与统计人工智能的优势结合起来，从数据中计算和学习定量规范。新的动作语言将联合处理不确定动态域中的常识性推理和动作学习。这样的系统允许我们仔细检查和理解系统行为，这对于设计可解释和可解释的系统是至关重要的。该项目旨在设计和实现一种新颖的动作语言，该语言具有很强的表达能力，可以对不确定情况下动态系统的各个方面进行建模，并适用于知识丰富的诊断和流推理。该形式主义将建立在最近的答案集规划的概率扩展上，称为LPMLN，它将马尔可夫逻辑的权重方案合并到答案集规划的语言中。这种形式将使动态域的概率诊断推理和反事实推理成为可能。概率动作语言的推理和学习方法将从逻辑规划和统计关系学习的方法中衍生出来。该框架将进一步扩展，以整合对作为数据流给出的观察结果的推理。所产生的方法将对需要集成知识表示和其他领域（如机器人和自主系统）的几个应用程序有用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Automated reasoning about dynamic worlds is an important capability for robust intelligent systems. Action languages allow for the description of actions and their effects in dynamic domains in a way that is based on natural language but sufficiently formal for modeling in knowledge-based systems. Today's action languages do not easily allow such systems to account for the probability and uncertainty necessary to model human-like commonsense reasoning. Existing action languages also assume full specification of a system in advance of one-shot execution of the logic program, which does not easily operate with continuous streams of data. This project will develop an action language based on the mathematical foundation that combines logic and probability. The research will join the representation and reasoning advantages of logical AI to the advantages in statistical AI to compute and learn quantitative specifications from data. The new action language will jointly address commonsense reasoning and learning about actions in uncertain dynamic domains. Such a system allows us to scrutinize and understand the system behavior, which is vital to the design of systems that are explainable and interpretable.The project is to design and implement a novel action language that is highly expressive for modeling various aspects of dynamic systems under uncertainty and which applies to knowledge-rich diagnosis and stream reasoning. The formalism will be built upon a recent probabilistic extension of answer set programs, called LPMLN, which incorporates the weight scheme of Markov Logic into the language of answer set programming. The formalism will enable probabilistic diagnostic reasoning and counterfactual reasoning about dynamic domains. Inference and learning methods for the probabilistic action language will be derived from the methods in logic programming and statistical relational learning. The framework will be further extended to integrate reasoning over observations given as streams of data. The methods produced will be useful for several applications that require integration of knowledge representation and other areas, such as robotics and autonomous systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.