超协调限制逻辑

本文给出了一阶超协调限制逻辑ＬＰｓ的定义，并证明了它与悖论逻辑（ＬＰ与ＬＰｍ）和限制逻辑（ＣＩＲＣ）的关系，ＬＰ作为一种非单调超协调逻辑具有非单逻辑和超协调逻辑的优点，而用能解决非单调逻辑和超协调逻辑存在的问题，它可作为在不完全与不协调知识下常识推理的形式化，因此它的知识表示中具有广泛的应用。     

基于完备拟序的开放逻辑

开放逻辑是一个可以刻画知识的增长，更新以及假说的进化的逻辑理论，它开辟了常识推理研究一条新途径，并在机器学习，知识获取，故障诊断及知识库维护等领域有广泛的应用，基本的开放逻辑不能体现认知主体对所拥有的知识在相信程度上的区别，为此，文中给出了基于完备拟序的开放逻辑，该文采用假说上的完备拟序刻画信度区别，给出了新的重构概念，讨论了假说及完备拟序在知识进化过程中的更新，定义了新的认识进程并证明了所定义的认识进程具有收敛性。     

经验逻辑：一种非单调逻辑的统一形式

人的常识推理是一种充满经验性知识的累积过程,而经验推理具有非单调性。本文提出一种关于典型与例外的经验逻辑,特别研究它的非单调性,它提供了一个现存的主要非单调逻辑的统一基础,这是通过一种类似的规则把它们翻译成经验逻辑获得的。因此,经验逻辑给出了一类更一般而且直观的非单调推理形式。     

超协调限制逻辑的计算复杂性分析

超协调限制逻辑LPc是一种同时具有非单调性和超协调性的非经典逻辑，它可作为在不完全与不协调知识下常识推理的形式化.给出了命题LPc的计算复杂性结果和算法实现，指出LPc是NP完全问题，并给出了将LPc转化为等价的优先限制逻辑的线性时间算法，由于限制逻辑具有实用的实现算法且可用归结方法实现，因而该算法为LPc的实现提供了新的途径.     

具有两种否定的描述逻辑系统MALC

否定信息在知识表示和推理中具有非常重要的作用。随着信息科学的发展,大量的事实表明：信息科学的许多领域需要区分概念的矛盾否定和对立否定。描述逻辑作为一阶谓词逻辑的可判定子集,并没有区分概念的矛盾否定与对立否定。本文将模糊否定词～和对立否定词 引入描述逻辑ALC,建立了一个扩展的描述逻辑系统MALC,使其具有处理模模糊知识的能力。同时,文章给出了基于中介无穷值语义模型的语义解释;在推理机制上,给出了可满足性的定义和可满足性的Tableau算法。     

一种基于多Agent的CSCD异常处理方法

针对协同设计中经常出现的异常情况，定义了异常及其分类，并基于多Agent和知识相结合的思想，提出异常检测和处理的方法。介绍了包括该方法的协作设计的原型系统——CoopDesigncr。     

基于中介逻辑的模糊推理算法

中介逻辑系统完整地反映了知识中的矛盾和对立等否定关系。针对具体处理模糊知识的需要,本文首先改进了中介无穷值语义模型,对其进行了语义描述;在此基础上扩展了Zadeh提出的近似推理方法即CRI算法,给出了基于中介逻辑思想的一种更为具体的算法,并通过一具体的例子进行了说明分析。     

基于关系数据分析的知识服务模型

研究生产分析与关系模式相互转换的工程生产知识结构,包括领域元概念、工程事实、设计规则、生产活动的设计与规范化描述。采用框架表示法表达元概念及事实,以服务的方式定义工程作业的常规方案。引入本体描述工程基本概念与关联,完成逻辑概念与关系模式的映射。针对需要生产状态信息作为中间知识的事实进行工程生产-关系模式解读,给出油田开发知识服务设计。     

认识逻辑(1):关于知识和信念的逻辑框架

知识和信念是人工智能领域研究中经常涉及到的两个重要概念。本文讨论了知识和信念的涵义与关系，定义了认识逻辑系统ＥＩ，讨论了它的语法和语义，证明了认识逻辑ＥＬ不但是可靠的而且是完备的，认为逻辑ＥＬ不但可以用来描述人类的认识过程，还可以用于对常识推理以及分布式系统的形式化描述。     

非单调推理的研究现状

一、引言 早在1959年,McCart~[1]就发现常识和常识推理很难处理,因为在常识推理中当前得出的结论,可能会由于以后新事实的加入而被取消.这就是所谓的“非单调性”。     

Classical negation in logic programs and disjunctive databases

An important limitation of traditional logic programming as a knowledge representation tool, in comparison with classical logic, is that logic programming does not allow us to deal directly with incomplete information. In order to overcome this limitation, we extend the class of general logic programs by including classical negation, in addition to negation-as-failure. The semantics of such extended programs is based on the method of stable models. The concept of a disjunctive database can be extended in a similar way. We show that some facts of commonsense knowledge can be represented by logic programs and disjunctive databases more easily when classical negation is available. Computationally, classical negation can be eliminated from extended programs by a simple preprocessor. Extended programs are identical to a special case of default theories in the sense of Reiter.     

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.     

An inquiry into computer understanding

This essay addresses a number of issues centered around the question of what is the best method for representing and reasoning about common sense (sometimes called plausible inference). Drew McDermott has shown that a direct translation of commonsense reasoning into logical form leads to insurmountable difficulties, from which McDermott concluded that we must resort to procedural ad hocery. This paper shows that the difficulties McDermott described are a result of insisting on using logic as the language of commonsense reasoning. If, instead, (Bayesian) probability is used, none of the technical difficulties found in using logic arise. For example, in probability, the problem of referential opacity cannot occur and nonmonotonic logics (which McDermott showed don't work anyway) are not necessary. The difficulties in applying logic to the real world are shown to arise from the limitations of truth semantics built into logic–probability substitutes the more reasonable notion of belief. In Bayesian inference, many pieces of evidence are combined to get an overall measure of belief in a proposition. This is much closer to commonsense patterns of thought than long chains of logical inference to the true conclusions. Also it is shown that English expressions of the “IF A THEN B” form are best interpreted as conditional probabilities rather than universally quantified expressions. Bayesian inference is applied to a simple example of linguistic information to illustrate the potential of this type of inference for AI. This example also shows how to deal with vague information, which has so far been the province of fuzzy logic. It is further shown that Bayesian inference gives a theoretical basis for inductive inference that is borne out in practice. Instead of insisting that probability is the best language for commonsense reasoning, a major point of this essay is to show that real inference is a complex interaction between probability, logic, and other formal representation and reasoning systems.     

A practical measure of the information in a logical theory

The information in data depends on the subjective value system that the receiver of the data uses to interpret them. This paper looks at the information in a theory of first order logic (a knowledge base) from the perspective of a decision maker for whom the validation of formulae (facts and rules) have varying importance. The decision maker's preferences and prior knowledge are both incorporated into the information measure. The value of information is determined by what it conveys about the formulae of importance to the decision maker. The information measure is applied as a heuristic in commonsense reasoning; in relevance assessment ; and as a preference function in belief revision.     

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。     

怎样利用语言知识资源进行语义理解和常识推理

该文讨论怎样利用语言知识资源来帮助机器进行语义理解和常识推理。首先,指出人类生活在常识和意义世界中,人工智能机器人必须理解自然语言的意义,能够在此基础上进行常识推理。接着,简单梳理了基于知识和基于统计两种自然语言处理路线各自的优长和短缺。然后,说明完全绕开知识的统计方法和深度学习,都不能真正理解概念和语言。该文通过具体案例说明,《实词信息词典》已经配备了有关词项的语义角色关系及其句法配置信息;把这种语言知识加入知识图谱和内容计算中,可以为人工智能提供理解和解释从而造就一种可解释的人工智能。由于“物性角色”描述了名词所指事物的百科知识,可用以回答相关事物是什么(形式角色)、有哪些部件(构成角色)、用什么做的(材料)、怎么形成的(施成)、有什么用途(功用)等常识性问题。     

一种基于PROLOG的ES建造工具库的研究与实现

本文描述了一种基于ＰＲＯＬＯＧ的专家系统建造工具库ＰＴＥＳ的实验系统。ＰＴＥＳ是用ＰＲＯＬＯＧ编写的，该系统根据支持基于规则的知识表示及近似推理对ＰＲＯＬＯＧ的知识处理能力进行了扩充。ＰＴＥＳ的推理机制使用了可能性能逻辑及模糊集合理论作为其逻辑基础，并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。     

一种基于PROLOG的ES建造工具库的研究与实现

本文描述了一基于ＰＲＯＬＯＧ的专家系统建造工具库ＰＴＥＳ的实验系统。ＰＴＥＳ是用ＰＲＯＬＯＧ编写的，该系统根据支持基于规则的知识表示及近似推理对ＰＲＯＬＯＧ的知识处理能力进行了扩充。ＰＴＥＳ的推理机制使用了可能性逻辑及模糊集合理论作为其逻辑基础并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。