Reasoning with contextual requirements: Detecting inconsistency and conflicts

ContextThe environment in which the system operates, its context, is variable. The autonomous ability of a software to adapt to context has to be planned since the requirements analysis stage as a strong mutual influence between requirements and context does exist. On the one hand, context is a main factor to decide whether to activate a requirement, the applicable alternatives to meet an activated requirement as well as their qualities. On the other hand, the system actions to reach requirements could cause changes in the context.ObjectivesModelling the relationship between requirements and context is a complex task and developing error-free models is hard to achieve without an automated support. The main objective of this paper is to develop a set of automated analysis mechanisms to support the requirements engineers to detect and analyze modelling errors in contextual requirements models.MethodWe study the analysis of the contextual goal model which is a requirements model that weaves together the variability of both context and requirements. Goal models are used during the early stages of software development and, thus, our analysis detects errors early in the development process. We develop two analysis mechanisms to detect two kinds of modelling errors. The first mechanism concerns the detection of inconsistent specification of contexts in a goal model. The second concerns the detection of conflicting context changes that arise as a consequence of the actions performed by the system to meet different requirements simultaneously. We support our analysis with a CASE tool and provide a systematic process that guides the construction and analysis of contextual goal models. We illustrate and evaluate our framework via a case study on a smart-home system for supporting the life of people having dementia problems.ResultsThe evaluation showed a significant ability of our analysis mechanisms to detect errors which were not notable by requirements engineers. Moreover, the evaluation showed acceptable performance of these mechanisms when processing up to medium-sized contextual goal models. The modelling constructs which we proposed as an input to enable the analysis were found easy to understand and capture.ConclusionsOur developed analysis for the detection of inconsistency and conflicts in contextual goal models is an essential step for the entire system correctness. It avoids us developing unusable and unwanted functionalities and functionalities which lead to conflicts when they operate together. Further research to improve our analysis to scale with large-sized models and to consider other kinds of errors is still needed.     

Reasoning tractably about explicit belief: A model‐theoretic approach

Existing epistemic logics such as the logic of implicit and explicit belief and the logic of awareness adopt a deductive‐theoretic approach for characterizing belief. In this approach, an agent represents the state of the world with a conjunction of axioms in its knowledge base (KB) and evaluates queries by trying to prove or disprove that they follow from KB. This paper presents a multivalued epistemic logic (MEL) that allows agents to reason both deductively and model theoretically about implicit and explicit belief. By characterizing an agent's KB with a class of finite models, the set of formulas that an agent believes can be determined by checking their validity in all these models. This rests on the fact that MEL has a complete axiomatization (sentences that are true in all these models will also be provable). In this paper, the soundness, completeness, and decidability of MEL are proven. Furthermore, a polynomial time model‐checking algorithm for determining the satisfiability of a sentence at a particular state in a given model of MEL is also presented. © 2000 John Wiley & Sons, Inc.     

A state-based knowledge representation approach for information logical inconsistency detection in warning systems

Detecting logical inconsistency in collected information is a vital function when deploying a knowledge-based warning system to monitor a specific application domain for the reason that logical inconsistency is often hidden from seemingly consistent information and may lead to unexpected results. Existing logical inconsistency detection methods usually focus on information stored in a knowledge base by using a well-defined general purpose knowledge representation approach, and therefore cannot fulfill the demands of a domain-specific situation. This paper first proposes a state-based knowledge representation approach, in which domain-specific knowledge is expressed by combinations of the relevant objects’ states. Based on this approach, a method for information logical inconsistency detection (ILID) is developed which can flexibly handle the demands of various domain-specific situations through reducing part of restrictions in existing methods. Finally, two real-case based examples are presented to illustrate the ILID method and its advantages.     

非一致OWL本体的推理方法研究

针对目前OWL推理机无法在非一致本体上进行推理的问题,提出了一种基于回答集程序的非一致本体推理方式,将OWL本体翻译为一类逻辑程序：HEX回答集程序,把本体推理问题规约为回答集程序求解问题。并通过在非一致本体上的推理实验,验证了该类方法的有效性。     

多变量规则的线性插值推理方法

插值推理是稀疏规则条件下的一类重要的推理方法,单变量的情况已有较多研究,但针对多变量情况的研究还不多,仅有的几种插值方法,存在着难以保证推理结果的凸性和正规性等问题。多变量规则的插值推理是插值推理研究的重要方面,为了在多变量稀疏规则条件下能得到好的插值推理结果,本文对多变量规则的插值推理方法进行了研究,提出了一个多变量规则的线性插值推理方法。该方法能较好地保证推理结果隶属函数的凸性和正规性,为智能系统中的模糊推理提供了一个十分有用的工具。     

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”。     

A logic for reasoning with inconsistency

Most known computational approaches to reasoning have problems when facing inconsistency, so they assume that a given logical system is consistent. Unfortunately, the latter is difficult to verify and very often is not true. It may happen that addition of data to a large system makes it inconsistent, and hence destroys the vast amount of meaningful information. We present a logic, called APC (annotated predicate calculus; cf. annotated logic programs of [4, 5]), that treats any set of clauses, either consistent or not, in a uniform way. In this logic, consequences of a contradiction are not nearly as damaging as in the standard predicate calculus, and meaningful information can still be extracted from an inconsistent set of formulae. APC has a resolution-based sound and complete proof procedure. We also introduce a novel notion of epistemic entailment and show its importance for investigating inconsistency in predicate calculus as well as its application to nonmonotonic reasoning. Most importantly, our claim that a logical theory is an adequate model of human perception of inconsistency, is actually backed by rigorous arguments.A preliminary report on this research appeared in LICS'89.Work of M. Kifer was supported in part by the NSF grants DCR-8603676, IRI-8903507.Work of E. L. Lozinskii was supported in part by Israel National Council for Research and Development under the grants 2454-3-87, 2545-2-87, 2545-3-89 and by Israel Academy of Science, grant 224-88.     

Nonmonotonic inconsistency

Nonmonotonic consequence is the subject of a vast literature, but the idea of a nonmonotonic counterpart of logical inconsistency—the idea of a defeasible property representing internal conflict of an inductive or evidential nature—has been entirely neglected. After considering and dismissing two possible analyses relating nonmonotonic consequence and a nonmonotonic counterpart of logical inconsistency, this paper offers a set of postulates for nonmonotonic inconsistency, an analysis of nonmonotonic inconsistency in terms of nonmonotonic consequence, and a series of results showing that nonmonotonic inconsistency conforms to these postulates given the analysis of nonmonotonic inconsistency presented here and certain postulates for nonmonotonic consequence.The results presented here establish the interest of certain previously undiscussed postulates of nonmonotonic consequence. These results also show that nonmonotonicity, which has never seemed useful in the formulation of general principles governing nonmonotonic reasoning, is relevant to the positive characterization of nonmonotonic inference after all.     

Reasoning composite beliefs using a qualitative approach

A Bayesian network is a knowledge representation technique for use in expert system development. The probabilistic knowledge encoded in a Bayesian network is a set of composite hypotheses expressed over the permutation of a set of variables (propositions). Ordering these composite hypotheses according to their a posteriori probabilities can be exponentially hard. This paper presents a qualitative reasoning approach which takes advantage of certain types of topological structures and probability distributions of a Bayesian network to derive the partial ordering of composite hypotheses. Such an approach offers an attractive alternative to reduce the computational complexity of deriving a partial ordering in which consistency is guaranteed.This work is supported in part by a grant to Queens College from the General Research Branch, National Institute of Health under grant No. RR-07064.     

A fixed-parameter algorithm for minimum quartet inconsistency

Given n taxa, exactly one topology for every subset of four taxa, and a positive integer k (the parameter), the Minimum Quartet Inconsistency (MQI) problem is the question whether we can find an evolutionary tree inducing a set of quartet topologies that differs from the given set in only k quartet topologies. The more general problem where we are not necessarily given a topology for every subset of four taxa appears to be fixed-parameter intractable. For MQI, however, which is also NP-complete, we can compute the required tree in time O(4^kn+n⁴). This means that the problem is fixed-parameter tractable and that in the case of a small number k of “errors” the tree reconstruction can be done efficiently. In particular, for minimal k, our algorithm can produce all solutions that resolve k errors. Additionally, we discuss significant heuristic improvements. Experiments underline the practical relevance of our solutions.     

Spider Diagrams: A Diagrammatic Reasoning System

Spider diagrams combine and extend Venn diagrams and Euler circles to express constraints on sets and their relationships with other sets. These diagrams can be used in conjunction with object-oriented modelling notations such as the Unified Modelling Language. This paper summarises the main syntax and semantics of spider diagrams. It also introduces inference rules for reasoning with spider diagrams and a rule for combining spider diagrams. This system is shown to be sound but not complete. Disjunctive diagrams are considered as one way of enriching the system to allow combination of diagrams so that no semantic information is lost. The relationship of this system of spider diagrams to other similar systems, which are known to be sound and complete, is explored briefly.     

Reasoning under uncertainty for plant disease diagnosis

The present work describes an operational knowledge-based system able to reason with uncertain knowledge, by using methodologies and formalisms that provide for representation of facts and rules with various degrees of certainty and precision. The user interface is windows based, and its design allows high flexibility both in describing the facts in the knowledge base and in adapting the reasoning mechanism. It can therefore be used to evaluate user-generated hypotheses on plant disorders and diagnostic scenarios, as well as provide an environment for education and training in phytopathology.     

A rule‐based approach to detect and prevent inconsistency in the domain‐engineering process

A medium‐sized domain‐engineering process can contain thousands of features that all have constraint dependency rules between them. Therefore, the validation of the content of domain‐engineering process is vital to produce high‐quality software products. However, it is not feasible to do this manually. This paper aims to improve the quality of the software products generated by the domain‐engineering process by ensuring the validity of the results of that process. We propose rules for two operations: inconsistency detection and inconsistency prevention. We introduce first‐order logic (FOL) rules to detect three types of inconsistency and prevent the direct inconsistency in the domain‐engineering process. Developing FOL rules to detect and prevent inconsistency in the domain‐engineering process directly without the need to the configuration process is our main contribution. We performed some experiments to test the scalability and applicability of our approach on domain‐engineered software product lines containing 1000 assets to 20000 assets. The results show that our approach is scalable and could be utilized to improve the domain‐engineering process.     

面向知识图谱的知识推理研究进展

近年来,随着互联网技术和应用模式的迅猛发展,引发了互联网数据规模的爆炸式增长,其中包含大量有价值的知识.如何组织和表达这些知识,并对其进行深入计算和分析,备受关注.知识图谱作为丰富直观的知识表达方式应运而生.面向知识图谱的知识推理是知识图谱的研究热点之一,已在垂直搜索、智能问答等应用领域发挥了重要作用.面向知识图谱的知识推理旨在根据已有的知识推理出新的知识或识别错误的知识.不同于传统知识推理,由于知识图谱中知识表达形式的简洁直观、灵活丰富,面向知识图谱的知识推理方法也更加多样化.本文将从知识推理的基本概念出发,介绍近年来面向知识图谱知识推理方法的最新研究进展.具体地,本文根据推理类型划分,将面向知识图谱的知识推理分为单步推理和多步推理,根据方法的不同,每类又包括基于规则的推理、基于分布式表示的推理、基于神经网络的推理以及混合推理.本文详细总结这些方法,并探讨和展望面向知识图谱知识推理的未来研究方向和前景.     

System LS: A Three-Tiered Nonmonotonic Reasoning System

In this paper, a formal system of nonmonotonic reasoning is developed, which takes as its inspiration the manner in which some people make logically justifiable conclusions about nonmonotonic reasoning problems. The people, when asked about individuals, compare the logical strength of the arguments relating any sets to which the individual belongs, to other sets. A three-tiered system of rules including rules of System P as well as Transitivity and Monotonicity is developed. The system, known as System LS for logical strength, deals with three levels of non-strict relationships:

• α ₁β  α are normally β ( more than half of the α are β ) ;

       
  
  

The Wonham filter under uncertainty: A game–theoretic approach

The paper is devoted to a state filtering problem of Markov jump processes given the continuous and/or counting observations. All the transition intensity matrix, observation plan and counting intensity are parameterized by a random vector with uncertain distribution on a known support set.The estimation problem is formulated in minimax settings with a conditional optimality criterion. We reduce the initial minimax problem to a dual problem of constrained quadratic optimization. The corresponding numerical algorithm of minimax filtering is presented as well as its illustrative implementation in the monitoring of a TCP link status under uncertainty.