基于格式塔的建筑物图画抽象

格式塔(Gestalt)理论总结了人类认知规则、模式和语义的若干规律,已有大量的研究针对独立的格式塔规则建立计算模型,但建立多个格式塔规则相互作用的耦合模型仍然是一个挑战.基于格式塔理论的若干主要规则建立了一个针对多个格式塔规则耦合的计算模型,并将该模型用于建筑物线画图的抽象;通过减少图画中重复结构元素的数目,或者将复杂的结构用简单结构替换的方式对建筑物结构进行概括和抽象.实验结果表明,该方法可以应用于风格各异的建筑物线画图,并可以扩展到其他类型的图画以及三维模型.     

Compositional semantics and behavioral equivalences for P Systems

The aim of the paper is to give a compositional semantics in the style of the Structural Operational Semantics (SOS) and to study behavioral equivalence notions for P Systems. Firstly, we consider P Systems with maximal parallelism and without priorities. We define a process algebra, called P Algebra, whose terms model membranes, we equip the algebra with a Labeled Transition System (LTS) obtained through SOS transition rules, and we study how some equivalence notions defined over the LTS model apply in our case. Then, we consider P Systems with priorities and extend the introduced framework to deal with them. We prove that our compositional semantics reflects correctly maximal parallelism and priorities.     

Why untyped nonground metaprogramming is not (much of) a problem

We study a semantics for untyped, vanilla metaprograms, using the nonground representation for object level variables. We introduce the notion of language independence, which generalizes range restriction. We show that the vanilla metaprogram associated with a stratified normal object program is weakly stratified. For language independent, stratified normal object programs, we prove that there is a natural one-to-one correspondence between atoms p(t₁,…,t_r) in the perfect Herbrand model of the object program and solve(p(t₁,…,t_r)) atoms in the weakly perfect Herb and model of the associated vanilla metaprogram. Thus, for this class of programs, the weakly perfect Herbrand model provides a sensible semantics for the metaprogram. We show that this result generalizes to nonlanguage independent programs in the context of an extended Herbrand semantics, designed to closely mirror the operational behavior of logic programs. Moreover, we also consider a number of interesting extensions and/or variants of the basic vanilla metainterpreter. For instance, we demonstrate how our approach provides a sensible semantics for a limited form of amalgamation.     

Probabilistic knowledge bases

We define a new fixpoint semantics for rule based reasoning in the presence of weighted information. The semantics is illustrated on a real world application requiring such reasoning. Optimizations and approximations of the semantics are shown so as to make the semantics amenable to very large scale real world applications. We finally prove that the semantics is probabilistic and reduces to the usual fixpoint semantics of stratified Datalog if all information is certain. We implemented various knowledge discovery systems which automatically generate such probabilistic decision rules. In collaboration with a bank in Hong Kong we use one such system to forecast currency exchange rates     

Logic programming with solution preferences

Preference logic programming (PLP) is an extension of logic programming for declaratively specifying problems requiring optimization or comparison and selection among alternative solutions to a query. PLP essentially separates the programming of a problem itself from the criteria specification of its solution selection. In this paper we present a declarative method for specifying preference logic programs. The method introduces a precise formalization for the syntax and semantics of PLP. The syntax of a preference logic program contains two disjoint sets of definite clauses, separating a core program specifying a general computational problem from its preference rules for optimization; the semantics of PLP is given based on the Herbrand model and fixed point theory, where how preferences affects the least Herbrand model of a logic program is interpreted as a sequence of meta-level mapping operations. In addition, we present an operational semantics based on a new resolution strategy and a memoized recursive algorithm for computing strictly stratified logic programs with well-formed preferences, and further show that the operational semantics of such a preference logic program is consistent to its declarative semantics.     

A visual token-based formalization of BPMN 2.0 based on in-place transformations

ContextThe Business Process Model and Notation (BPMN) standard informally defines a precise execution semantics. It defines how process instances should be updated in a model during execution. Existing formalizations of the standard are incomplete and rely on mappings to other languages.ObjectiveThis paper provides a BPMN 2.0 semantics formalization that is more complete and intuitive than existing formalizations.MethodThe formalization consists of in-place graph transformation rules that are documented visually using BPMN syntax. In-place transformations update models directly and do not require mappings to other languages. We have used a mature tool and test-suite to develop a reference implementation of all rules.ResultsOur formalization is a promising complement to the standard, in particular because all rules have been extensively verified and because conceptual validation is facilitated (the informal semantics also describes in-place updates).ConclusionSince our formalization has already revealed problems with the standard and since the BPMN is still evolving, the maintainers of the standard can benefit from our results. Moreover, tool vendors can use our formalization and reference implementation for verifying conformance to the standard.     

Aligning Logical and Psychological Perspectives on Diagrammatic Reasoning

We advance a theoretical framework which combines recent insights of research in logic, psychology, and formal semantics, on the nature of diagrammatic representation and reasoning. In particular, we wish to explain the varied efficacy of reasoning and representing with diagrams. In general we consider diagrammatic representations to be restricted in expressive power, and we wish to explain efficacy of reasoning with diagrams via the semantical and computational properties of such restricted `languages'. Connecting these foundational insights (from semantics and complexity theory) to the psychology of reasoning with diagrams requires us to develop the notion of the availability (to an agent) of constraints operating within representation systems, as a consequence of their direct semanticinterpretation. Thus we offer a number of fundamentaldefinitions as well as a research programme which alignscurrent efforts in the logical and psychological analysis ofdiagrammatic representation systems.     

On stratified disjunctive programs

We address the problem of a consistent fixpoint semantics for general disjunctive programs restricted to stratifiable programs which do not recurse through negative literals. We apply the nonmonotonic fixpoint theory developed by Apt, Blair and Walker to a closure operatorT ^c and develop a fixpoint semantics for stratified disjunctive programs. We also provide an iterative definition for negation, called the Generalized Closed World Assumption for Stratified programs (GCWAS), and show that our semantics captures this definition. We develop a model-theoretic semantics for stratified disjunctive programs and show that the least state characterized by the fixpoint semantics corresponds to a stable-state defined in a manner similar to the stable-models of Gelfond and Lifschitz. We also discuss a weaker stratification semantics for general disjunctive programs based on the Weak Generalized Closed World Assumption.     

Completeness and Counter-Example Generations of a Basic Protocol Logic: (Extended Abstract)

We give an axiomatic system in first-order predicate logic with equality for proving security protocols correct. Our axioms and inference rules derive the basic inference rules, which are explicitly or implicitly used in the literature of protocol logics, hence we call our axiomatic system Basic Protocol Logic (or BPL, for short). We give a formal semantics for BPL, and show the completeness theorem such that for any given query (which represents a correctness property) the query is provable iff it is true for any model. Moreover, as a corollary of our completeness proof, the decidability of provability in BPL holds for any given query. In our formal semantics we consider a “trace” any kind of sequence of primitive actions, counter-models (which are generated from an unprovable query) cannot be immediately regarded as realizable traces (i.e., attacked processes on the protocol in question). However, with the aid of Comon-Treinen's algorithm for the intruder deduction problem, we can determine whether there exists a realizable trace among formal counter-models, if any, generated by the proof-search method (used in our completeness proof). We also demonstrate that our method is useful for both proof construction and flaw analysis by using a simple example.     

Learning a factor model via regularized PCA

We consider the problem of learning a linear factor model. We propose a regularized form of principal component analysis (PCA) and demonstrate through experiments with synthetic and real data the superiority of resulting estimates to those produced by pre-existing factor analysis approaches. We also establish theoretical results that explain how our algorithm corrects the biases induced by conventional approaches. An important feature of our algorithm is that its computational requirements are similar to those of PCA, which enjoys wide use in large part due to its efficiency.     

A Logic Based Asynchronous Multi-Agent System

We present a logic programming based asynchronous multi-agent system in which agents can communicate with one another; update themselves and each other; abduce hypotheses to explain observations, and use them to generate actions. The knowledge base of the agents is comprised of generalized logic programs, integrity constraints, active rules, and of abducibles. We characterize the interaction among agents via an asynchronous transition rule system, and provide a stable models based semantics. An example is developed to illustrate how our approach works.     

Parallel simulation for a fish schooling model on a general‐purpose graphics processing unit

We consider an individual‐based model for fish schooling, which incorporates a tendency for each fish to align its position and orientation with an appropriate average of its neighbors' positions and orientations, in addition to a tendency for each fish to avoid collisions. To accurately determine the statistical properties of the collective motion of fish whose dynamics are described by such a model, many realizations are typically required. This carries a very high computational cost. The current generation of graphics processing units is well suited to this task. We describe our implementation and present computational experiments illustrating the power of this technology for this important and challenging class of problems. Copyright © 2008 John Wiley & Sons, Ltd.     

Agent Programming in 3APL

An intriguing and relatively new metaphor in the programming community is that of an intelligent agent. The idea is to view programs as intelligent agents acting on our behalf. By using the metaphor of intelligent agents the programmer views programs as entities which have a mental state consisting of beliefs and goals. The computational behaviour of an agent is explained in terms of the decisions the agent makes on the basis of its mental state. It is assumed that this way of looking at programs may enhance the design and development of complex computational systems.To support this new style of programming, we propose the agent programming language 3APL. 3APL has a clear and formally defined semantics. The operational semantics of the language is defined by means of transition systems. 3APL is a combination of imperative and logic programming. From imperative programming the language inherits the full range of regular programming constructs, including recursive procedures, and a notion of state-based computation. States of agents, however, are belief or knowledge bases, which are different from the usual variable assignments of imperative programming. From logic programming, the language inherits the proof as computation model as a basic means of computation for querying the belief base of an agent. These features are well-understood and provide a solid basis for a structured agent programming language. Moreover, on top of that 3APL agents use so-called practical reasoning rules which extend the familiar recursive rules of imperative programming in several ways. Practical reasoning rules can be used to monitor and revise the goals of an agent, and provide an agent with reflective capabilities.Applying the metaphor of intelligent agents means taking a design stance. From this perspective, a program is taken as an entity with a mental state, which acts pro-actively and reactively, and has reflective capabilities. We illustrate how the metaphor of intelligent agents is supported by the programming language. We also discuss the design of control structures for rule-based agent languages. A control structure provides a solution to the problem of which goals and which rules an agent should select. We provide a concrete and intuitive ordering on the practical reasoning rules on which such a selection mechanism can be based. The ordering is based on the metaphor of intelligent agents. Furthermore, we provide a language with a formal semantics for programming control structures. The main idea is not to integrate this language into the agent language itself, but to provide the facilities for programming control structures at a meta level. The operational semantics is accordingly specified at the meta level, by means of a meta transition system.     

Specification of communicating processes: temporal logic versus refusals-based refinement

In this paper we consider the relationship between refinement-oriented specification and specifications using a temporal logic. We investigate the extent to which one can check whether a program in a process algebra, such as Communicating Sequential Processes (CSP), satisfies a temporal logic specification using a refinement-based model checker, such as FDR. We consider what atomic formulae are appropriate in a temporal logic for specifying communicating processes, in particular where one wants to talk about the availability of events. We then show that, perhaps surprisingly, the standard stable failures model is not adequate for capturing specifications in such a logic: instead the refusal traces model must be used. We formalise the logic by giving it a semantics in this model. We show that the temporal operators eventually and until, and negation, cannot, in general, be tested for via simple refinement checks. For the remaining fragment of the logic, we present a translation into simple refinement checks. Finally, we show that refusal traces equivalence is characterised by a slightly augmented version of that fragment. M. J. Butler     

Semantics of roundoff error propagation in finite precision calculations

We introduce a concrete semantics for floating-point operations which describes the propagation of roundoff errors throughout a calculation. This semantics is used to assert the correctness of a static analysis which can be straightforwardly derived from it. In our model, every elementary operation introduces a new first order error term, which is later propagated and combined with other error terms, yielding higher order error terms. The semantics is parameterized by the maximal order of error to be examined and verifies whether higher order errors actually are negligible. We consider also coarser semantics computing the contribution, to the final error, of the errors due to some intermediate computations. As a result, we obtain a family of semantics and we show that the less precise ones are abstractions of the more precise ones.     

Tool support for verifying UML activity diagrams

We describe a tool that supports verification of workflow models specified in UML activity diagrams. The tool translates an activity diagram into an input format for a model checker according to a mathematical semantics. With the model checker, arbitrary propositional requirements can be checked against the input model. If a requirement fails to hold, an error trace is returned by the model checker, which our tool presents by highlighting a corresponding path in the activity diagram. We summarize our formal semantics, discuss the techniques used to reduce an infinite state space to a finite one, and motivate the need for strong fairness constraints to obtain realistic results. We define requirement-preserving rules for state space reduction. Finally, we illustrate the whole approach with a few example verifications.     

A Constructor-Based Approach to Positive/Negative-Conditional Equational Specifications

We study algebraic specifications given by finite sets R of positive/negative-conditional equations (i.e. universally quantified first-order implications with a single equation in the succedent and a conjunction of positive and negative (i.e. negated) equations in the antecedent). The class of models of such a specification R does not contain in general a minimum model in the sense that it can be mapped to any other model by some homomorphism. We present a constructor-based approach for assigning appropriate semantics to such specifications. We introduce two restrictions: firstly, for a condition to be fulfilled we require the evaluation values of the terms of the negative equations to be in the constructor sub-universe which contains the evaluation values of all constructor ground terms; secondly, we restrict the constructor equations to have "Horn"-form and to be "constructor-preserving". A reduction relation for R is defined, which allows to generalize the fundamental results for positive-conditional rewrite systems. This reduction relation is monotonic w. r. t. consistent extension of the specification, which is of practical importance as it allows for an incremental construction process of complex specifications without destroying reduction steps which were possible before. Under the assumption of confluence, the factor algebra of the term algebra modulo the congruence of the reduction relation is a minimal model which is (beyond that) the minimum of all models that do not identify more objects of the constructor sub-universe than necessary. We define several kinds of compatibility of R with a reduction ordering for achieving decidability of reducibility, and present several criteria for the confluence of our reduction relation.     

Application of fuzzy Naive Bayes and a real-valued genetic algorithm in identification of fuzzy model

We present a method to identify a fuzzy model from data by using the fuzzy Naive Bayes and a real-valued genetic algorithm. The identification of a fuzzy model is comprised of the extraction of “if–then” rules that is followed by the estimation of their parameters. The involved parameters include those which determine the membership function of fuzzy sets and the certainty factors of fuzzy if–then rules. In our method, as long as the fuzzy partition in the input–output space is given, the certainty factor of each rule is computed with the fuzzy conditional probability of the consequent conditioned on the antecedent by using the fuzzy Naive Bayes, which is a generalization of Naive Bayes. The fuzzy model involves the rules characterized by the highest values of certainty factors. The certainty factor of each rule is the fuzzy conditional probability, and it reflects the inner relationship between the antecedent and the consequent. In order to improve the accuracy of the fuzzy model, the real-valued genetic algorithm is incorporated into our identification process. This process concerns the optimization of the membership functions occurring in the rules. We just involve the parameters of membership function of the fuzzy sets into the real-valued genetic algorithm, since the certainty factor of each rule can be computed automatically. The performance of the model is shown for the backing-truck problem and the prediction of Mackey–Glass time series.