基于上下文的知识表示和推理--人工智能的观点

本文中,我们从知识表示和推理(KRR)的角度概括地阐述了上下文推理的概念和基本原理。首先阐述了上下文的概念;然后介绍了上下文推理的三种基本形式和上下文理论的两个基本原理,也就是局部性原理和一致性原理;接着讨论了上下文推理的形式化问题;最后通过对一个叫“魔术盒问题”的求解来展示如何利用多上下文系统MCS对问题进行形式化表示和求解。     

信息集成中上下文知识的描述与推理

基于本体的语义信息集成主要解决了异构数据源之间的模式级异构,但对于分布环境下普遍存在的上下文异构无法解决,这导致用户从信息集成系统得到的结果仍然存在语义异构。针对这种不足,本文提出了一种上下文知识的形式化描述和推理机制,并将这种上下文机制引入基于本体的语义信息集成中,同时对其进行扩展,使得扩展后的语义信息集成系统可以自动检测和消除上下文语义异构,从而形成了完整的语义异构解决方案。     

上下文推理技术在智能家庭中的研究与应用

针对智能家庭具有预感知能力、可以为用户提供主动的智能服务的特点,建立一个智能家庭本体模型,利用Jena框架的推理接口实现上下文的基本推理,通过在推理层和知识库之间增加一个上下文过滤器,有效过滤掉推理过程中产生的无用数据。实验结果验证了规则推理在智能家庭中的可行性和有效性。     

基于情感上下文的语音情感推理算法

针对前后相邻情感语句的情感变化存在相互关联的特性,提出基于情感上下文的情感推理算法.该算法首先利用传统语音情感特征和上下文语音情感特征分别识别待分析情感语句的情感状态,然后借助情感交互矩阵及两类情感特征识别结果的置信度对待测试语句的情感状态进行融合推理.在此基础上,建立语音情感上下文推理规则,利用该规则根据相邻语句的情感状态对待分析情感语句情感状态进行调整,最终得出待分析情感语句所属的情感类别.在自行录制的包含6种基本情感数据库上的实验结果表明,与仅采用声学特征的方法相比,文中提出方法平均识别率提高12.17%.     

基于帧图像语义上下文的地形推理策略

基于场景帧图像语义上下文概念,提出一种融合不同场景时空信息的自适应在线地形推理策略TIBSC(Terrain Inference Based on Semantic Context).首先,提取不同场景图像近视场像素的特征向量和地形类别,构建地形样本候选数据库;然后,从地形样本候选数据库中选取与当前场景图像远视场区域语义上下文最相似的若干帧地形样本,构建当前场景地形判别数据库;最后,基于当前场景地形判别数据库及贝叶斯法则,对当前场景远视场区域像素所属地形类别进行推理.基于数据集的仿真实验表明:在影响TIBSC推理精度的所有因素中,基于语义距离准则的最优样本选择以及样本数据的在线扩充两大模型变量起主导作用.同时,基于相同分类特征的TIBSC策略在推理精度上优于同类研究结果.     

智能空间中上下文推理问题的研究

针对以往上下文推理方法中上下文描述能力差、解决不确定性问题能力低下的现状，提出了一个新的上下文表示和推理的方法。该方法通过用陈述性表示方法描述上下文，提高了上下文的描述能力；用基于规则的逻辑推理方法提高了解决不确定性问题的能力；且该方法将求解问题的复杂度控制在合理的范围内。最后，文中通过实例证实了该方法的可行性。     

基于上下文推理的指挥所业务交互研究

基于指挥所操作业务的应用需求,定义适用于指挥所中人机交互的上下文信息。在选取该上下文信息的过程中,利用OWL语言完成指挥所业务上下文描述,建立交互信息上下文感知模型。运用SWRL逻辑规则对交互信息上下文进行推理,获得业务交互过程中高层上下文语义后进行自适应调整。以一个师指挥所作战指挥业务想定为例,说明计算机通过用户交互位置和上级指令上下文信息进行调整,从而满足当前用户的交互位置和交互业务。     

上下文感知在Smart Home系统中的应用

针对Smart Home环境的复杂性问题,提出了利用上下文技术来提高其智能化的方法.阐述了在普适环境下的上下文在智能家居环境中的重要性,介绍了上下文和上下文感知计算的定义,采用了基元组的形式定义了不同的上下文信息;提出了基于上下文感知的Smart Home系统模型,并对其功能进行了详细的描述;在Smart Home系统中,应用该模型对具体的上下文实例进行了描述和推理.     

上下文在人工智能中的应用

对上下文的基本概念进行了探讨。阐述了上下文在知识获取、机器学习、通信、数据库和本体中的应用。最后,对人工智能中的上下文所存在的一些问题进行讨论。     

Pragmatism and purism in artificial intelligence and legal reasoning

The paper identifies and assesses the implications of two approaches to the field of artificial intelligence and legal reasoning. The first — pragmatism — concentrates on the development of working systems to the exclusion of theoretical problems. The second — purism — focuses on the nature of the law and of intelligence with no regard for the delivery of commercially viable systems. Past work in AI and law is classified in terms of this division. By reference to The Latent Damage System, an operational system, the paper articulates and responds to conceivable purist (jurisprudential and AI) objections to such a program. The methods of the pragmatist are also called into question and refined. The author concludes that pragmatism within a purist framework is the only sound approach to developing reliable AI systems in law.     

Human reasoning about artificial intelligence

Abstract

Recently, several authors (Searle, Penrose, Rychlak) have suggested that AI is a doomed undertaking. In his recent book, Artificial Intelligence and Human Reasoning, Joseph Rychlak repeats many of the arguments of the other critics, as well as offering several of his own. In this paper, taking Rychlak as symptomatic of this new anti-computational intellectual movement, we respond to these arguments and defend AI and personal construct theory against some of the misunderstandings and confusions which we find there.     

Strategies for contextual reasoning with conflicts in ambient intelligence

Ambient Intelligence environments host various agents that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of such environments and the special characteristics of ambient agents have introduced new research challenges in the study of Distributed Artificial Intelligence. This paper proposes a solution based on the Multi-Context Systems paradigm, according to which local knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To resolve potential inconsistencies that may arise from the interaction of contexts through their mappings (global conflicts), we use a preference ordering on the system contexts, which may express the confidence that an agent has in the knowledge imported by other agents. On top of this model, we have developed four alternative strategies for global conflicts resolution, which mainly differ in the type and extent of context and preference information that is used to resolve potential conflicts. The four strategies have been respectively implemented in four versions of a distributed algorithm for query evaluation and evaluated in a simulated P2P system.     

Dialectical models in artificial intelligence and law

Dialogues and dialectics have come to playan important role in the field of ArtificialIntelligence and Law. This paper describes thelegal-theoretical and logical background of this role,and discusses the different services into whichdialogues are put. These services include:characterising logical operators, modelling thedefeasibility of legal reasoning, providing the basisfor legal justification and identifying legal issues,and establishing the law in concrete cases. Specialattention is given to the requirements oflaw-establishing dialogues.     

多媒体智能：当多媒体遇到人工智能

过去10年中涌现出大量新兴的多媒体应用和服务,带来了很多可以用于多媒体前沿研究的多媒体数据。多媒体研究在图像/视频内容分析、多媒体搜索和推荐、流媒体服务和多媒体内容分发等方向均取得了重要进展。与此同时,由于在深度学习领域所取得的重大突破,人工智能(artificial intelligence,AI)在20世纪50年代被正式视为一门学科之后,迎来了一次“新”的发展浪潮。因此,一个问题就自然而然地出现了：当多媒体遇到人工智能时会带来什么？为了回答这个问题,本文通过研究多媒体和人工智能之间的相互影响引入了多媒体智能的概念。从两个方面探讨多媒体与人工智能之间的相互影响：一是多媒体促使人工智能向着更具可解释性的方向发展;二是人工智能反过来为多媒体研究注入了新的思维方式。这两个方面形成了一个良性循环,多媒体和人工智能在其中不断促进彼此发展。本文对相关研究及进展进行了讨论,并围绕值得进一步探索的研究方向分享见解。希望可以对多媒体智能的未来发展带来新的研究思路。     

Trends in distributed artificial intelligence

Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented, based on the social abilities of an individual agent, the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally, the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.     

行为理解的认知推理方法

目的 人类行为理解是机器智能研究中最富有挑战性的领域。其根本问题是语义获取,即从动作推理得到人的行为,需要跨越两者之间的语义鸿沟,为此提出一种人关于日常行为知识与人体动作行为、环境信息之间的建模方法,以及可扩展的开放式结构环境—行为关系模型,基于该模型提出一种新的行为理解的渐进式认知推理方法。方法 首先根据知识,建立多种特征、复合特征和行为之间的关系模型。系统根据当前的输入流,处理得到当前的特征与复合特征集,推理得到当前的可能行为集。该行为集指导处理模块,更新特征集,得到新的行为集。结果 应用本文渐进式连续推理方法,系统可以把人关于日常行为的知识与人体运动、环境变化等传感器数据处理获取到的信息动态绑定,实现知识辅助的行为理解。结论 提出的推理方法能连续处理长时间、同时发生的行为。     

普适计算环境下上下文信息推理的研究

从各种低层上下文信息得到对人们更加有用的高层上下文信息即上下文推理是当前研究的热点.针对该问题,采用描述逻辑,研究基于本体模型的上下文推理方法.首先简要介绍基于本体的上下文模型,该模型增加了对上下文特性的建模,然后分别研究基于本体的推理、基于规则的推理及不一致性验证3种推理方式,借助Jena框架的推理接口实现,推理功能全面,通用性强,基本满足了普适计算系统中上下文推理的需求,最后给出了推理的可用性.