A self-adaptive multi-engine solver for quantified Boolean formulas

In this paper we study the problem of engineering a robust solver for quantified Boolean formulas (QBFs), i.e., a tool that can efficiently solve formulas across different problem domains without the need for domain-specific tuning. The paper presents two main empirical results along this line of research. Our first result is the development of a multi-engine solver, i.e., a tool that selects among its reasoning engines the one which is more likely to yield optimal results. In particular, we show that syntactic QBF features can be correlated to the performances of existing QBF engines across a variety of domains. We also show how a multi-engine solver can be obtained by carefully picking state-of-the-art QBF solvers as basic engines, and by harnessing inductive reasoning techniques to learn engine-selection policies. Our second result is the improvement of our multi-engine solver with the capability of updating the learned policies when they fail to give good predictions. In this way the solver becomes also self-adaptive, i.e., able to adjust its internal models when the usage scenario changes substantially. The rewarding results obtained in our experiments show that our solver AQME—Adaptive QBF Multi-Engine—can be more robust and efficient than state-of-the-art single-engine solvers, even when it is confronted with previously uncharted formulas and competitors.     

Boolean Functions as Models for Quantified Boolean Formulas

In this paper, we introduce the notion of models for quantified Boolean formulas. For various classes of quantified Boolean formulas and various classes of Boolean functions, we investigate the problem of determining whether a model exists. Furthermore, we show for these classes the complexity of the model checking problem, which is to check whether a given set of Boolean functions is a model for a formula. For classes of Boolean functions, we establish some characterizations in terms of classes of quantified Boolean formulas that have such a model. This research has been supported in part by the Air Force Office of Scientific Research under grant FA9550-06-1-0050. This research has been supported in part by the NSFC under grants 60573011 and 10410638.     

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.     

基于布尔函数的Rough集差别矩阵属性约简方法

Rough集理论是对大型数据库进行知识发现的主要方法之一。根据属性集核和相对等价类的概念,对数据库属性集中的属性进行约简,提取相应的规则(知识),是用Rough集知识发现的精髓。该文基于Rough集差别矩阵,提出了属性集的布尔函数的构造方法,并应用吸收律、分配律和等幂律对属性集布尔函数化简。论文证明了属性集布尔函数的化简与属性集的差别矩阵约简等价,同时给出了求相对决策属性基本集的算法和IRIS提供的数据仿真实验结果。     

Common Knowledge and Update in Finite Environments

Logics of knowledge have been shown to provide a useful approach to the high level specification and analysis of distributed systems. It has been proposed that such systems can be developed using knowledge- based protocols, in which agents' actions have preconditions that test their state of knowledge. Both computer-assisted analysis of the knowledge properties of systems and automated compilation of knowledge-based protocols require the development of algorithms for the computation of states of knowledge. This paper studies one of the computational problems of interest, the model checking problem for knowledge formulae in the S5_nKripke structures generated by finite state environments in which states determine an observation for each agent. Agents are assumed to have perfect recall and may operate synchronously or asynchronously. It is shown that, in this setting, model checking of common knowledge formulae is intractable, but efficient incremental algorithms are developed for formulae containing only knowledge operators. Connections to knowledge updates and compilation of knowledge-based protocols are discussed.     

Deontic logic in the representation of law: Towards a methodology

There seems to be no clear consensus in the existing literature about the role of deontic logic in legal knowledge representation — in large part, we argue, because of an apparent misunderstanding of what deontic logic is, and a misplaced preoccupation with the surface formulation of legislative texts. Our aim in this paper is to indicate, first, which aspects of legal reasoning are addressed by deontic logic, and then to sketch out the beginnings of a methodology for its use in the analysis and representation of law.The essential point for which we argue is that deontic logic — in some form or other —needs to be taken seriously whenever it is necessary to make explicit, and then reason about, the distinction between what ought to be the case and what is the case, or as we also say, between the ideal and the actual. We take the library regulations at Imperial College as the main illustration, and small examples from genuinely legal domains to introduce specific points. In conclusion, we touch on the role of deontic logic in the development of the theory of normative positions.Deontic logic and the theory of normative positions are of relevance to legal knowledge representation, but also to the analysis and. representation of normative systems generally. The emphasis of the paper is on legal knowledge representation, but we seek to place the discussion within the context of a broader range of issues concerning the role of deontic logic in Computer Science.     

Handling Qualitative Aspects of Human Knowledge in Diagnosis

Knowledge involved in diagnosis of real complex systems comes from human experts and requires appropriate discrete and qualitative representation. The large amount of information resulted is difficult to be managed and prepared to enter the diagnosis system without the help of an appropriate tool. The paper proposes a knowledge elicitation scheme for multifunctional conductive flow systems under fault diagnosis along with appropriate representation of normative and faulty models. Prototype and instance manifestations get a semi-qualitative representation and symptoms refer to means-end and bond–graph entities in a new approach, suited to human diagnostician’s conceptual view. The Computer Aided Knowledge Elicitation (CAKE) tool proposed copes with knowledge involved in the diagnosis. The case study on knowledge elicitation for fault diagnosis of a hydraulic installation and the conclusions highlight advantages of the present approach.