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

• 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

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.

关于动态世界的自动推理是强大智能系统的一项重要功能。 动作语言允许以基于自然语言但足够正式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进行建模。当今的动作语言不容易让此类系统解释模拟人类常识推理所需的概率和不确定性。 现有的动作语言还在逻辑程序的一次性执行之前假设了系统的完整规范，这不容易与连续的数据流一起操作。 该项目将开发一种基于结合逻辑和概率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。新的动作语言将共同解决常识推理和学习不确定动态领域中的动作的问题。 这样的系统使我们能够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至关重要。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现力，可以对动态系统的各个方面进行建模不确定性，适用于知识丰富的诊断和流推理。形式主义将建立在答案集程序的最新概率扩展之上，称为 LPMLN，它将马尔可夫逻辑的权重方案合并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源自逻辑编程和统计关系学习的方法。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。所产生的方法对于需要整合知识表示和其他领域的多种应用非常有用，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价值和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

Elaboration Tolerant Representation of Markov Decision Process via Decision Theoretic Extension of Action Language pBC+

通过动作语言 pBC 的决策理论扩展对马尔可夫决策过程进行精细化容忍表示

• 发表时间: 2019-04
• 期刊: LPNMR 2019
• 作者: Wang, Yi;Lee, Joohyung
• 通讯作者: Lee, Joohyung

Computing Logic Programs with Ordered Disjunction Using asprin

使用阿司匹林通过有序析取计算逻辑程序

• 发表时间: 2018-01
• 期刊: Proceedings of the 16th International Conference on Principles of Knowledge Representation and Reasoning
• 作者: Lee, Joohyung;Yang, Zhun
• 通讯作者: Yang, Zhun

A Simple Extension of Answer Set Programs to Embrace Neural Networks (Extended Abstract)

答案集程序的简单扩展以支持神经网络（扩展摘要）

• DOI: 10.4204/eptcs.325
• 发表时间: 2020-09
• 期刊: Electronic proceedings in theoretical computer science
• 作者: Yang, Zhun;Ishay, Adam;Lee, Joohyung
• 通讯作者: Lee, Joohyung

First-order stable model semantics with intensional functions

具有内涵函数的一阶稳定模型语义

• DOI: 10.1016/j.artint.2019.01.001
• 发表时间: 2019-08-01
• 期刊: Artif. Intell.
• 作者: M. Bartholomew;Joohyung Lee
• 通讯作者: Joohyung Lee

A Model-Based Approach to Visual Reasoning on CNLVR Dataset

CNLVR 数据集上基于模型的视觉推理方法

• 发表时间: 2018-10
• 期刊: Proceedings of the 16th International Conference on Principles of Knowledge Representation and Reasoning
• 作者: Sampat, Shailaja;Lee, Joohyung
• 通讯作者: Lee, Joohyung