人工智能中的上下文推理

对上下文推理的原理和形式进行了阐述,讨论了上下文推理的形式化问题,并给出一个利用MCS对问题进行形式化表示和求解的实例。     

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。     

基于粗糙FCA-概念代数的上下文本体建模

在上下文本体模型中,根据现有上下文信息推导出新知识,但在推理过程中存在两个问题:(1)现有上下文中可能隐含多个有用信息,而现有方法在推理前并未对其针对这一点进行处理,上下文具有不完整性,推理出的知识可能不全面;(2)推理后有新知识出现,新知识与旧知识可能存在不协同等问题,使得本体可能没有较好的可扩展性。针对以上两个问题,借鉴粗糙FCA的粗糙处理方法,提出基于粗糙FCA上下文抽取方法以获得隐含上下文;再使用概念代数将得到的所有上下文深度形式化表示,并构建具有较好可扩展性的概念网。实验结果表明,在提出的方法基础上进行上下文推理的正确率高于直接使用原推理方法,而且在本体可扩展性方面有明显优势。     

神经网络问题求解机制

本文提出了神经网络知识表示的形式化描述语言和知识单元的概念，用于给出传统符号逻辑中的概念，用于给出传统符号逻辑中的概念，属性及它们之间的层次关系如何在神经网络中进行表示，提出了激活强度的度量标准，从而在理论上给出了ＮＮ中继承和识别问题的形式化处理方法。在此基础上，提出了ＮＮ正向和反向问题求解机制。这里，值得一提的是，本文通过在某一特定领域中对ＮＮ推理机制的成功探讨，有力地反驳了人们对于ＮＮ模型的一     

基于永久本体的上下文信息推理的应用研究

介绍了本体的定义及其描述语言，OSGi服务网关模型及其架构，接着提出基于本体推理的智能家庭网关概念及其框架结构，并对其原理和上下文信息本体进行了描述以及构造了上下文本体，并且利用Jena框架的推理接口来实现了上下文的基本推理，最后是一个例子及其代码。     

基于描述逻辑的上下文知识获取与推理方法

针对上下文感知计算中缺乏清晰统一的模型与自动推理支持的问题,提出一种基于描述逻辑的上下文知识获取与推理方法。该方法首先提出了一种本体引导的上下文模型框架,根据抽象层次的不同将上下文模型分为元模型与领域特定模型两层结构;然后采用描述逻辑表示语言SHOIN(D)形式化描述该上下文模型,设计上下文模型向描述逻辑知识库的转换算法。最后以一个实际案例说明该方法的可行性。     

一种基于问题求解理论的密码协议形式模型

提出了一种基于问题求解理论的密码协议模型，给出了模型的基本语法以及基于ρ演算的形式语义，明确了模型推理过程中涉及到的一些关键性的概念和命题。该模型具有以下特点：能够对密码协议进行精确的形式化描述；具有合理可靠的可证明语义；对密码协议安全性的定义精确合理；便于实现自动化推理。所有这些均确保了基于该模型的密码协议安全性分析的合理性和有效性，为正确的分析密码协议的安全性提供了可靠依据。     

一种基于上下文的语义映射方法

提出了一种基于上下文的语义映射方法SM-Context (semantic mapping based on context).SM-Context首先为本体模型中的概念找出表示其语义信息的上下文,然后采用谓词逻辑的形式表示概念的上下文,最后将本体映射问题转换成命题可满足性问题(SAT),并通过推理方式建立本体之间的语义映射关系.为了验证所提方法在处理本体映射问题时的可行性与有效性,采用OAEI所提供的共享数据集来测试SM-Context.实验结果表明,SM-Context可以有效地利用概念的上下文为本体之间建立语义映射关系.     

基于混合推理的知识库的构建及其应用研究

该文提出了基于OWL本体与Prolog规则的平面几何知识库的构建方法,从而可形式化地表示平面几何中丰富的语义信息.一方面,用类型、定义域、值域、分类、属性、实例等本体描述来表达结构化的知识,为领域内概念与概念之间关系的描述提供形式化的语义;另一方面,用Prolog规则来解决本体不能有效表达的诸如属性之间的关系和操作等问题,从而支持复杂关系间的推理.在此基础上,用Protégé和Prolog构建了一个基于本体和规则的平面几何知识库.实验证明:此知识库可实现知识和语义层次上的信息查询,还可进行复杂问题求解,其丰富的语义描述和混合推理能力弥补了传统知识库的不足.     

服务自适应模型及其应用

提出了一个基于模糊理论的服务自适应模型。完整地形式化了服务自适应选择过程，使用模糊语言变量和隶属度方程定义了上下文状态和服务策略选择规则。基于当前上下文与服务实现策略标准上下文之间的模糊距离概念，提出了计算服务实现策略合适程度的合适度方程。结合名为“校园助理”的上下文敏感应用场景，阐述了该模型的有效性和具体应用。     

Contextual reasoning distilled

In this paper we provide a foundation of a theory of contextual reasoning from the perspective of a theory of knowledge representation. Starting from the so-called metaphor of the box, we firstly show that the mechanisms of contextual reasoning proposed in the literature can be classified into three general forms (called localized reasoning, push and pop, and shifting). Secondly, we provide a justification of this classification, by showing that each mechanism corresponds to operating on a fundamental dimension along which context dependent representations may vary (namely, partiality, approximation and perspective). From the previous analysis, we distill two general principles of a logic of contextual reasoning. Finally, we show that these two principles can be adequately formalized in the framework of MultiContext Systems. In the last part of the paper, we provide a practical illustration of the ideas discussed in the paper by formalising a simple scenario, called the Magic Box problem.     

A multicontext architecture for formalizing complex reasoning

We propose multicontext systems (MC systems) as a formal framework for the specification of complex reasoning. MC systems provide the ability to structure the specification of “global” reasoning in terms of “local” reasoning subpatterns. Each subpattern is modeled as a deduction in a context, formally defined as an axiomatic formal system. the global reasoning pattern is modeled as a concatenation of contextual deductions via bridge rules, i.e., inference rules that infer a fact in one context from facts asserted in other contexts. Besides the formal framework, in this article we propose a three-layer architecture designed to specify and automatize complex reasoning. At the first level we have object-level contexts (called s-contexts) for domain specifications. Problem-solving principles and, more in general, meta-level knowledge about the application domain is specified in a distinct context, called Problem-Solving Context (PSC). On top of s-contexts and PSC, we have a further context, called MT, where it is possible to specify strategies to control multicontext reasoning spanning through s-contexts and PSC. We show how GETFOL can be used as a computer tool for the implementation of MC systems and for the automatization of multicontext deductions. © 1995 John Wiley & Sons, Inc.     

A goal-based framework for contextual requirements modeling and analysis

Requirements engineering (RE) research often ignores or presumes a uniform nature of the context in which the system operates. This assumption is no longer valid in emerging computing paradigms, such as ambient, pervasive and ubiquitous computing, where it is essential to monitor and adapt to an inherently varying context. Besides influencing the software, context may influence stakeholders’ goals and their choices to meet them. In this paper, we propose a goal-oriented RE modeling and reasoning framework for systems operating in varying contexts. We introduce contextual goal models to relate goals and contexts; context analysis to refine contexts and identify ways to verify them; reasoning techniques to derive requirements reflecting the context and users priorities at runtime; and finally, design time reasoning techniques to derive requirements for a system to be developed at minimum cost and valid in all considered contexts. We illustrate and evaluate our approach through a case study about a museum-guide mobile information system.     

Putting Web Services in Context

When considering the full range of Web service-related activities, it becomes clear that dealing with context is a major challenge, requiring greater expressiveness, reasoning capabilities, and architectural components than are provided by the current widely accepted building blocks of the Web services stack. This paper presents an informal overview of concepts, requirements and challenges for handling contextual knowledge in connection with Web services, and briefly discusses several interesting projects in this area of research.     

Context-based mobile GeoBI: enhancing business analysis with contextual metrics/statistics and context-based reasoning

Business professionals are increasingly mobile and should be supported by suitable mobile Decision Support Systems (DSS). In our previous work, we have established that such suitable mobile DSS should be (i) GeoBI(Geospatial Business Intelligence)-enabled and (ii) context-based, and have addressed issues regarding context characterization and context modeling. The present paper deals with mobile GeoBI context-based reasoning. Through realistic scenarios, it highlights (i) the requirement for context-based reasoning to enhance mobile GeoBI experience, (ii) the need for contextual metrics/statistics to help mobile business professionals discover their local context, (iii) the need for crossing business performance metrics with contextual metrics to help mobile business professionals in discovering the context hidden behind business performance figures, and proposes convenient solutions to tackle these needs.     

CA-FCM: Towards a formal representation of expert’s causal judgements over construction project changes

Aimed at improving the proactive benefits of Fuzzy Cognitive Mapping (FCM) for predicting construction project changes, this paper presents CA-FCM: a Context-aware Fuzzy Cognitive Mapping approach. CA-FCM’s main functionality is to imitate the intuitive causal judgements of project experts over change causation in different contextual settings. Invoking the logical inference capabilities of semantic web tools, a hybrid inference mechanism is embedded within the proposed framework which enables establishing contextual connections between prospective causal factors through a semi-automated process of generating relevant causal statements. Hence, CA-FCM can assist decision-makers with (1) a shared sense-making of the domain concepts which would significantly facilitate the manual construction of FCM scenarios, (2) providing contextualized recommendations of causal information required for developing FCM scenarios, (3) dynamic modelling of causal inferences, imitating expert reasoning on change causation and propagation. Towards providing a detailed delineation of CA-FCM’s effectiveness on providing assistance in planning for project changes, a partial implementation of the proposed framework was conducted within a real case scenario.     

Concept generalization and fusion for abstractive sentence generation

Text summarization is either extractive or abstractive. Extractive summarization is to select the most salient pieces of information (words, phrases, and/or sentences) from a source document without adding any external information. Abstractive summarization allows an internal representation of the source document so as to produce a faithful summary of the source. In this case, external text can be inserted into the generated summary. Because of the complexity of the abstractive approach, the vast majority of work in text summarization has adopted an extractive approach.In this work, we focus on concepts fusion and generalization, i.e. where different concepts appearing in a sentence can be replaced by one concept which covers the meanings of all of them. This is one operation that can be used as part of an abstractive text summarization system. The main goal of this contribution is to enrich the research efforts on abstractive text summarization with a novel approach that allows the generalization of sentences using semantic resources. This work should be useful in intelligent systems more generally since it introduces a means to shorten sentences by producing more general (hence abstractions of the) sentences. It could be used, for instance, to display shorter texts in applications for mobile devices. It should also improve the quality of the generated text summaries by mentioning key (general) concepts. One can think of using the approach in reasoning systems where different concepts appearing in the same context are related to one another with the aim of finding a more general representation of the concepts. This could be in the context of Goal Formulation, expert systems, scenario recognition, and cognitive reasoning more generally.We present our methodology for the generalization and fusion of concepts that appear in sentences. This is achieved through (1) the detection and extraction of what we define as generalizable sentences and (2) the generation and reduction of the space of generalization versions. We introduce two approaches we have designed to select the best sentences from the space of generalization versions. Using four NLTK¹ corpora, the first approach estimates the “acceptability” of a given generalization version. The second approach is Machine Learning-based and uses contextual and specific features. The recall, precision and F1-score measures resulting from the evaluation of the concept generalization and fusion approach are presented.