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

RI：小：通过动作语言的概率扩展来表达推理和学习不确定性下的动作

基本信息

批准号：
1815337
负责人：
Joohyung Lee
金额：
$ 36.38万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815337&HistoricalAwards=false
关键词：
RI Small Expressive Reasoning Learning

项目摘要

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.

动态世界的自动推理是健壮智能系统的一项重要功能。动作语言允许以一种基于自然语言但足够形式化的方式描述动态领域中的动作及其效果，以便在基于知识的系统中建模。今天的动作语言不容易允许这样的系统考虑建模类似人类的常识推理所必需的概率和不确定性。现有的动作语言还假设在逻辑程序的一次执行之前完全规范系统，这不容易与连续的数据流一起操作。这个项目将开发一种基于逻辑和概率相结合的数学基础上的动作语言。这项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。新的动作语言将共同解决常识推理和对不确定动态领域中的动作的学习。这样的系统可以让我们仔细观察和理解系统行为，这对于设计可解释和可解释的系统至关重要。该项目旨在设计和实现一种新的动作语言，该语言对不确定性下动态系统的各个方面建模具有高度的表达能力，并适用于知识丰富的诊断和流推理。这种形式将建立在最近对答案集程序的概率扩展LPMLN的基础上，LPMLN将马尔可夫逻辑的权重方案结合到答案集编程语言中。形式主义将使关于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编程和统计关系学习的方法中推导出来。该框架将进一步扩展，以整合对以数据流形式给出的观测数据的推理。所产生的方法将对几个需要将知识表示与其他领域相结合的应用程序有用，例如机器人和自主系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。