基于情感常识的情感分析

近年来,随着人工智能领域的蓬勃发展,计算机对于常识知识的需求逐渐增加。情感常识作为常识知识中的一部分,同样是当前情感计算领域的一个重要方面。鉴于情感词典结构与内容的局限性,该文设计了一种二元情感常识结构并以此为基础构建中文情感常识库。其构建过程首先通过知识提取获得情感常识知识候选集,再经过人工筛选与自动化扩展形成最终的情感常识库。在公开数据集上的实验结果表明,该文所构建的二元情感常识库有利于提高文本情感分析的精度。     

常识问题——常识推理的逻辑基础

本文主要讨论常识推理的逻辑基础，基于一条从非单调推理到常识推理的技术途径，由此指出在更一般意义上形式化常识推是的一些结果，它建立常识逻辑和解决常识问题提供了有用的基础工具。     

Language,logic and ontology: Uncovering the structure of commonsense knowledge

The purpose of this paper is twofold: (i) we argue that the structure of commonsense knowledge must be discovered, rather than invented; and (ii) we argue that natural language, which is the best known theory of our (shared) commonsense knowledge, should itself be used as a guide to discovering the structure of commonsense knowledge. In addition to suggesting a systematic method to the discovery of the structure of commonsense knowledge, the method we propose seems to also provide an explanation for a number of phenomena in natural language, such as metaphor, intensionality, and the semantics of nominal compounds. Admittedly, our ultimate goal is quite ambitious, and it is no less than the systematic ‘discovery’ of a well-typed ontology of commonsense knowledge, and the subsequent formulation of the long-awaited goal of a meaning algebra.     

心理常识概念的表示、获取和分析

常识知识是一类重要的人类知识，对自然语言分析、机器智能研究和自动推理研完等都有重要的意义。本文围绕心理常识，主要讨论与心理相关的常识概念的表示、获取和分析的方法。针对现有的概念模型中的概念主要由手工获取．缺乏自动方法，使得概念获取的非冗余性、一致性不能得到保证的问题，本文提出了获取心理常识概念的基本策略：依据心理学中的心理范畴手工获取心理常识的基础概念及概念间关系；根据属性的心理特征手工得到心理属性和属性问关系；以心理常识基础概念和心理常识属性为语义成分，通过“子类生成规则”自动完成获取和组织心理常识概念的任务。并且通过“子类检查规则”检查和分析新加入概念库的常识概念的冗余性和一致性。     

An organization of knowledge for problem solving and language comprehension

Plan synthesis and language comprehension, or more generally, the act of discovering how one perception relates to others, are two sides of the same coin, because they both rely on a knowledge of cause and effect—algorithmic knowledge about how to do things and how things work. I will describe a new theory of representation for commonsense algorithmic world knowledge, then show how this knowledge can be organized into larger memory structures, as it has been in a LISP implementation of the theory. The large-scale organization of the memory is based on structures called bypassable causal selection networks. A system of such networks serves to embed thousands of small commonsense algorithm patterns into a larger fabric which is directly usable by both a plan synthesizer and a language comprehender. Because these bypassable networks can adapt to context, so will the plan synthesizer and a language comprehender. I will propose that the model is an approximation to the way humans organize and use algorithmic knowledge, and as such, that it suggests approaches not only to problem solving and language comprehension, but also to learning. I'll describe the commonsense algorithm representation, show how the system synthesizes plans using this knowledge, and trace through the process of language comprehension, illustrating how it threads its way through these algorithmic structures.     

Topological Spatio–Temporal Reasoning and Representation

We present here a theory of motion from a topological point of view, in a symbolic perspective. Taking space–time histories of objects as primitive entities, we introduce temporal and topological relations on the thus defined space–time to characterize classes of spatial changes. The theory thus accounts for qualitative spatial information, dealing with underspecified, symbolic information when accurate data are not available or unnecessary. We show that these structures give a basis for commonsense spatio–temporal reasoning by presenting a number of significant deductions in the theory. This can serve as a formal basis for languages describing motion events in a qualitative way.     

常识问答研究综述

常识问答是一项重要的自然语言理解任务, 旨在利用常识知识对自然语言问句进行自动求解, 以得到准确答案. 常识问答在虚拟助手或社交聊天机器人等领域有着广泛的应用前景, 且其蕴涵了知识挖掘与表示、语言理解与计算、答案推理和生成等关键科学问题, 因而受到工业界和学术界的广泛关注. 首先介绍常识问答领域的主要数据集; 其次, 归纳不同常识知识源在构建方式、常识来源和表现形式上的区别; 同时, 重点分析并对比前沿常识问答模型, 以及融合常识知识的特色方法. 特别地, 根据不同问答任务场景中常识知识的共性和特性, 建立包含属性、语义、因果、语境、抽象和意图6大类的知识分类体系. 以此为支撑, 针对常识知识数据集建设, 感知知识融合和预训练语言模型的协作机制, 以及在此基础上的常识知识预分类技术, 进行前瞻性的研究, 并具体报告上述模型在跨数据集迁移场景下的性能变化, 及其在常识答案推理中的潜在贡献. 总体上, 包含对现有数据和前沿技术的回顾, 也包含面向跨数据知识体系建设、技术迁移与通用化的预研内容, 借以在汇报领域技术积累的前提下, 为其理论和技术的进一步发展提供参考意见.     

Pouring liquids: A study in commonsense physical reasoning

This paper presents a theory that supports commonsense, qualitative reasoning about the flow of liquid around slowly moving solid objects; specifically, inferring that liquid can be poured from one container to another, given only qualitative information about the shapes and motions of the containers. It shows how the theory and the problem specification can be expressed in a first-order language; and demonstrates that this inference and other similar inferences can be justified as deductive conclusions from the theory and the problem specification.     

常识问题——常识,人工智能与数理逻辑

本文提出了常识问题，通过确立常识准则和常识模型，指出一个建立常识逻辑的技术途径。     

一种交互式事件常识知识的获取方法

赋予机器常识知识是使机器具有真正智能的必备条件之一,而获得这些常识一直是人工智能研究的一个重要课题。该文提出了一种通过交互的方式来引导知识贡献者给出关于事件的常识知识的方法。方法获取过程是一个机器与贡献者的交互过程: 机器动态地生成问题,对知识贡献者进行提问;知识贡献者通过回答问题给出常识知识。交互过程通过包含提示信息的提问问题对知识贡献者进行提示,运用七种类型问题层层递进地引导知识贡献者思考,以此唤醒他们大脑中的常识知识;通过动态变化的问题改善知识贡献者贡献常识知识过程的趣味性。同时,该文还引入可接受性和有效性两个定量标准评价提问问题,用于进一步改善交互过程。实验结果表明,知识贡献者运用此方法给出的知识量增加了451.61%,同时知识的正确率也达到了92.5%。
     

Being and acting rational [agent design]

Rationality alone is insufficient to specify agent design. Using economic theory, we can program agents to behave in ways that maximize their utility while responding to environmental changes. However, economic models for agents, although general in principle, are typically limited in practice because the value functions that are tractable essentially reduce an agent to acting selfishly. Building a stable social system from a collection of agents motivated by self-serving interests is difficult. Finally, understanding rationality and knowledge requires interdisciplinary results from artificial intelligence, distributed computing, economics and game theory, linguistics, philosophy, and psychology. A complete theory involves semantic models for knowledge, belief, action, uncertainty; bounded rationality and resource-bounded reasoning; commonsense epistemic reasoning; reasoning about mental states; belief revision; and interactions in multiagent systems.     

Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution

Answer set programming (ASP) is a knowledge representation and reasoning paradigm with high-level expressive logic-based formalism, and efficient solvers; it is applied to solve hard problems in various domains, such as systems biology, wire routing, and space shuttle control. In this paper, we present an application of ASP to housekeeping robotics. We show how the following problems are addressed using computational methods/tools of ASP: (1) embedding commonsense knowledge automatically extracted from the commonsense knowledge base ConceptNet, into high-level representation, and (2) embedding (continuous) geometric reasoning and temporal reasoning about durations of actions, into (discrete) high-level reasoning. We introduce a planning and monitoring algorithm for safe execution of plans, so that robots can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. Some of the recoveries require collaboration of robots. We illustrate the applicability of ASP on several housekeeping robotics problems, and report on the computational efficiency in terms of CPU time and memory.     

Representing defaults with epistemic concepts

Reasoning about defaults—implications that typically hold, but which may have exceptions—is an important part of commonsense reasoning. We present some parts of a theory of defaults, concentrating on distinctions between various subtle ways in which defaults can be defeated, and on inferences which seem plausible but which are not correct in all cases. To represent this theory in a formal system, it is natural to use the epistemic concept of self-belief. We show how to express the theory by a local translation into autoepistemic logic, which contains the requisite epistemic operators.     

初等数学应用题题意理解中的常识库系统构建

类人计算领域, 题意的机器理解是数学应用题自动求解的难点. 常识性知识的缺失直接影响到题意理解的准确性. 本研究以常识为研究对象, 收集了历年初等数学古典概型的典型案例, 分析了古典概型类应用题的常识特征, 并进行了常识类型划分; 设计了XML结构存储常识性知识, 构建常识库系统实现古典概型常识的分类、表征及存储, 辅助计算机进行题意理解. 通过典型案例的应用, 其结果显示本研究构建的常识库对古典概型应用题的题意正确理解是十分有帮助的.     

一个模糊常识库的模型

常识的表示及推理是人工智能的一个核心难题。文章提出了一个模糊常识库的模型,描述了模糊概念的表示方法以及常识的模糊推理,并给出了模型解题的两个例子。     

A new approach toward modeling causality in commonsense reasoning

This article examines the issue of causality in commonsense reasoning and proposes a connectionist approach for modeling commonsense causal reasoning. Based on an analysis of the advantages and limitations of existing accounts, especially Shoham's logic, a generalized rule-based model FEL is proposed that can take into account the inexactness and the cumulative evidentiality of commonsense reasoning; this model corresponds naturally to a connectionist architecture. Detailed analyses are performed to show how the model handles commonsense causal reasoning. This work shows that a logic-based account of causality can be viewed as an (over)idealization of a more realistic model, which is simpler in form but deals with causality better. This work directly maps a (causal) rule-encoding scheme into a connectionist model; thus, it serves to link rule-based reasoning to connectionist models, notably with direct one-to-one correspondence between the basic structures of the two formalisms. © 1995 John Wiley & Sons, Inc.     

基于常识的亲属关系推理模型

常识知识的研究与发展得到了人工智能界的很大重视。文章建立了一个基于常识的人物亲属关系推理模型,研究了亲属关系常识以及人物信息的表示与存储。此外,对实际所要解决的问题进行了总结。     

Towards concept anchoring for cognitive robots

We present a model for anchoring categorical conceptual information which originates from physical perception and the web. The model is an extension of the anchoring framework which is used to create and maintain over time semantically grounded sensor information. Using the augmented anchoring framework that employs complex symbolic knowledge from a commonsense knowledge base, we attempt to ground and integrate symbolic and perceptual data that are available on the web. We introduce conceptual anchors which are representations of general, concrete conceptual terms. We show in an example scenario how conceptual anchors can be coherently integrated with perceptual anchors and commonsense information for the acquisition of novel concepts.