基于一致性的中心诊断及中心溯因诊断

本文对溯因诊断的过程和中心溯因诊断的过程进行了刻画。将求中心溯因诊断的过程清晰地分为与领域有关的冲突识别及与领域无关的候选产生两大步骤。不仅指出了基于一致性的中心诊断与中心溯因诊断之间的关系，而且论证了中心溯因诊断与本原蕴含／蕴含式的直接关系。显示出基于一致性的中心诊断空间和中心溯因诊断空间不仅可同时计算，而且可用ＡＴＭＳ这类算法来计算，从而将我们的理论结果与实现联系起来。     

泛“蕴含”运算和泛“串行推理”运算研究

泛“蕴含”运算是广泛存在于经验性思维、不确定性推理和各种多值逻辑系统具有普遍意义的逻辑运算之一.但常见蕴含算子往往凭主观经验给定,缺乏理论指导和使用的有效性分析,具有很大的随意性和盲目性.本文首先研究了“蕴含”运算的思想基础,认为“蕴含”运算是“串行推理”运算的逆运算.然后提出了“蕴含”公理,从代数系统角度给出了“蕴含”运算的定义,提出并证明了“蕴含”运算的表示定理,对常见的蕴含算子进行了有效性分析.最后研究了“蕴含”运算在“串行推理”运算中的运用.从而克服了已有的蕴含运算理论存在的不足.这样实际应用就可     

中文文本蕴含类型及语块识别方法研究

文本蕴含识别（RTE）是判断两个句子语义是否具有蕴含关系的任务.近年来英文蕴含识别研究取得了较大发展,但主要是以类型判断为主,在数据中精确定位蕴含语块的研究比较少,蕴含类型识别的解释性较低.从中文文本蕴含识别（CNLI）数据中挑选12 000个中文蕴含句对,人工标注引起蕴含现象的语块,结合语块的语言学特征分析归纳了7种具体的蕴含类型.在此基础上,将中文蕴含识别任务转化为7分类的蕴含类型识别和蕴含语块边界-类型识别任务,在深度学习模型上达到69.19%和62.09%的准确率.实验结果表明,所提出的方法可以有效发现中文蕴含语块边界及与之对应的蕴含类型,为下一步研究提供了可靠的基准方法.     

基于Transformer与HowNet义原知识融合的双驱动语义蕴含识别

语义蕴含识别旨在检测和判断两个语句的语义是否一致,以及是否存在蕴含关系.然而现有方法通常面临中文同义词、一词多义现象困扰和长文本难理解的挑战.针对上述问题,本文提出了一种基于Transformer和HowNet义原知识融合的双驱动中文语义蕴含识别方法,首先通过Transformer对中文语句内部结构语义信息进行多层次编码和数据驱动,并引入外部知识库HowNet进行知识驱动建模词汇之间的义原知识关联,然后利用soft-attention进行交互注意力计算并与义原矩阵实现知识融合,最后用BiLSTM进一步编码文本概念层语义信息并推理判别语义一致性和蕴含关系.本文所提出的方法通过引入HowNet义原知识手段解决多义词及同义词困扰,通过Transformer策略解决长文本挑战问题.在BQ、AFQMC、PAWSX等金融和多语义释义对数据集上的实验结果表明,与DSSM、MwAN、DRCN等轻量化模型以及ERNIE等预训练模型相比,该模型不仅可以有效提升中文语义蕴含识别的准确率(相比DSSM模型提升2.19%),控制模型的参数量(16 M),还能适应50字及以上的长文本蕴含识别场景.     

基于时间贝叶斯Petri网的溯因故障诊断

针对复杂系统发生故障时告警信号间的时序约束关系,提出一种时间贝叶斯Petri网模型(TBPN),并基于该模型提出一种复杂系统的溯因故障诊断方法.该方法首先对观测到的告警信息建立时间Petri网,随后将其求逆并转换为TBPN.通过时间区间计算和溯因推理,分析告警信号的时序一致性并验证故障假说,最后对时序正确的故障事件链计算其故障概率并进一步分析干扰信息.仿真实验表明,该方法可对复杂故障进行快速诊断,并在告警信息存在丢失、虚警、时标差错时表现出较好的鲁棒性.与不考虑时间约束的同类方法相比,该方法具有搜索空间更小,抗干扰性更强的特点.     

命题公式真值表的生成算法

真值表是命题逻辑理论中的一个重要概念,利用它可以求命题公式的主范式、判定命题公式的类型以及进行命题逻辑的推理等。本文给出了任意命题公式真值表的生成算法,为利用计算机解决命题逻辑中的其它问题奠定了基础.     

确定最小函数依赖集的新方法

根据函数依赖集的逻辑蕴含与对应逻辑函数的蕴含项之间的等价性,将求给定函数依赖集的所有等价最小函数依赖集归结为对应逻辑函数的化简,且关系数据理论中的一系列问题都能利用此方法统一简洁地处理,如确定所有候选码、任意属性集闭包及依赖基等.     

强表达描述逻辑本体的后继式公理定位研究

公理定位能够挖掘描述逻辑中可解释的缺陷,并为逻辑蕴含结果寻找隐藏的理由,因此在描述逻辑研究中引起了广泛的关注.平衡描述逻辑表达能力和推理机求解效率问题一直是公理定位研究的重点内容.本文基于一种后继式判定算法,从白盒和黑盒两个角度将其用于公理定位.白盒方法利用修正的后继式规则（即定位规则）来追踪推理的具体过程,并引入定位公式的概念建立子句的布尔公式标签与逻辑蕴含的所有极小公理集之间的对应关系.黑盒方法则直接调用基于未修正的后继式规则的推理机,并进一步利用碰集树方法求得逻辑蕴含的所有理由.最后,基于这样的两种面向强表达描述逻辑本体的公理定位算法设计推理工具,从理论和实验两方面验证其可行性,并与已有的推理工具比较求解性能.     

基于MiniSAT的命题极小模型计算方法

计算命题公式的极小模型在人工智能推理系统中是一项必不可少的任务.然而,即使是正CNF(conjunctive normal form)公式,其极小模型的计算和验证都不是易处理的.当前,计算CNF公式极小模型的主要方法之一是将其转换为析取逻辑程序后用回答集程序(answer set programming,ASP)求解器计算其稳定模型/回答集.针对计算CNF公式的极小模型的问题,提出一种基于可满足性问题(satisfiability problem,SAT)求解器的计算极小模型的方法MMSAT;然后结合最近基于极小归约的极小模型验证算法CheckMinMR,提出了基于极小模型分解的计算极小模型方法MRSAT;最后对随机生成的大量的3CNF公式和SAT国际竞赛上的部分工业基准测试用例进行测试.实验结果表明:MMSAT和MRSAT对随机3CNF公式和SAT工业测试用例都是有效的,且计算极小模型的速度都明显快于最新版的clingo,并且在SAT工业实例上发现了 clingo有计算出错的情况,而MMSAT和MRSAT则更稳定.     

求多元函数总体极小点的下楼法

引言 在许多理论和实际问题的研究当中,人们常常希望(也需要)找到最优(最佳)的选择,以便获得最优的效果。这些问题常可以归纳或抽象为一个求多元函数f(x)的极小点问题(因极小和极大无本质区别,故只需讨论极小问题),即求     

Abduction in logic programming: A new definition and an abductive procedure based on rewriting

A long outstanding problem for abduction in logic programming has been on how minimality might be defined. Without minimality, an abductive procedure is often required to generate exponentially many subsumed explanations for a given observation. In this paper, we propose a new definition of abduction in logic programming where the set of minimal explanations can be viewed as a succinct representation of the set of all explanations. We then propose an abductive procedure where the problem of generating explanations is formalized as rewriting with confluent and terminating rewrite systems. We show that these rewrite systems are sound and complete under the partial stable model semantics, and sound and complete under the answer set semantics when the underlying program is so-called odd-loop free. We discuss an application of abduction in logic programming to a problem in reasoning about actions and provide some experimental results.     

CLAUSE MANAGEMENT SYSTEMS (CMS)

This paper provides an extension to the study of the clause management system (CMS) proposed by Reiter and de Kleer. The CMS is adapted specifically for aiding a reasoning system in explanations generation. The reasoning system transmits propositional formulae representing its knowledge to the CMS and in return, it requests the CMS for minimal and consistent explanations of a query with respect to the CMS knowledge base. The CMS knowledge base is represented by a set of prime implicates. The classification of implicates as minimal, prime, trivial, and minimal trivial is carefully examined. Similarly, the notion of a support for a clause including minimal, prime, trivial, and minimal trivial is also elaborated. The methods to compute these supports from implicates and a preference ordering scheme expressible by logical specificity for the set of supports for a given clause are also presented. The generalization of the notion of a minimal support for a conjunction of clauses is also shown. Finally, two logic-based diagnostic reasoning paradigms aided by the CMS are shown to exemplify the functionality of the CMS.     

Compiling a default reasoning system into Prolog

Artificial intelligence researchers have been designing representation systems for default and abductive reasoning. Logic Programming researchers have been working on techniques to improve the efficiency of Horn clause deduction systems This paper describes how one such default and abductive reasoning system (namelyTheorist) can be translated into Horn clauses (with negation as failure), so that we can use the clarity of abductive reasoning systems and the efficiency of Horn clause deduction systems. We thus show how advances in expressive power that artificial intelligence workers are working on can directly utilise advances in efficiency that logic programming researchers are working on. Actual code from a running system is given.     

Generation and Selection of Abductive Explanations for Non-Omniscient Agents

Among the non-monotonic reasoning processes, abduction is one of the most important. Usually described as the process of looking for explanations, it has been recognized as one of the most commonly used in our daily activities. Still, the traditional definitions of an abductive problem and an abductive solution mention only theories and formulas, leaving agency out of the picture. Our work proposes a study of abductive reasoning from an epistemic and dynamic perspective. In the first part we explore syntactic definitions of both an abductive problem in terms of an agent’s information and an abductive solution in terms of the actions that modify the agent’s information. We look at diverse kinds of agents, including not only omniscient ones but also those whose information is not closed under logical consequence and those whose reasoning abilities are not complete. In the second part, we look at an existing logical framework whose semantic model allows us to interpret the previously stated formulas, and we define two actions that represent forms of abductive reasoning.     

基于支持度理论的广义Modus Ponens问题的最优解

为了将模糊推理纳入逻辑的框架并从语构和语义两个方面为模糊推理奠定严格的逻辑基础,通过将模糊推理形式化的方法移植到经典命题逻辑系统中,把FMP(fuzzy modus ponens)问题转化为GMP(generalized modus ponens)问题,并基于公式的真度概念提出了公式之间的支持度,进一步利用支持度的思想引入了GMP问题以及CGMP(collective generalized modus ponens)问题的一种新型最优求解机制.证明了最优解的存在性,同时指出,在经典命题逻辑系统中存在着与模糊逻辑完全相似的推理机制.该方法是一种程度化的方法,这就使得求解过程从算法上实现成为可能,并对知识的程度化推理有所启示.     

CONSTRAINED RESOLUTION AND ABDUCTTVE TEMPORAL REASONING

This article presents our work on the effective implementation of abduction in temporal reasoning. This works builds on some results, both in the logic programming field and in the automated reasoning area. We have defined and implemented an abductive procedure, which is well adapted for temporal reasoning because it is based on a constrained resolution principle. Constrained resolution has two advantages for temporal reasoning: First, it allows us to deal efficiently with temporal ordering and equality predicates, which are otherwise too much trouble with classical resolution; second, it allows a restricted form of abduction where hypotheses are limited to ordering relationships. From the logic programming area, our work uses results and procedures developed by others in the abductive logic programming field. The procedure we define and implement in this work is relatively independent of the temporal formalism: It has been used with some reified temporal logics and with the event calculus. More generally it can be used on any point-based temporal formalism, provided that a correct and complete algorithm is available for checking the consistency of a set of temporal ordering relationships in this language.