Temporal Contexts for Discourse Representation: An Extension of the Conceptual Graph Approach

A discourse is composed of a sequence of sentences that must be interpreted with respect to the context in which they are uttered and to the actions that produce them: locutors' speech acts. The analysis of discourse content must be based on a pragmatic approach to the study of language in use. Some of the most obvious linguistic elements that require contextual information for their representation are deictic forms such as here, now, I, you, this , and verb tenses.Several authors have recognized a need for introducing contextual structures in knowledge representation models such as semantic networks. Sowa's Conceptual Graph Theory is a powerful approach to conceptually represent knowledge contained in discourses. However, it must be extended in order to represent several semantic and pragmatic mechanisms related to the expression of time in natural language. In this paper we present such an extension as a framework for modeling temporal knowledge in discourses integrating several features borrowed from speech act theory.First, we introduce the notions of time interval, temporal object, temporal situation, and temporal relation. Then, we discuss the importance of explicitly introducing the concept of time coordinate system in a discourse representation and we present different kinds of temporal contexts: narrator's perspective, agent's perspective and temporal localization. We show how this conceptual framework can be used to represent various referential mechanisms in discourse such as anaphoras, indexicals, direct and indirect styles. We also discuss how to model several linguistic phenomena such as speech act characteristics and the specification of performative and attitude utterances. Finally, we briefly discuss how verb tenses can be determined in a discourse on the basis of this temporal approach.     

汉语篇章时间短语的分析与时制验算

汉英机器翻译中，汉语篇章的时间信息是生成正确英语词时态的基础，时制是时间信息重要组成部分，需要在篇间中通过时间短语的语义分析获得，首先对汉语篇章时间短时间了语义分类，然后设计了时间短语语义表示结构TPSRS，用概念信息体关联网络CIURN表示了汉语篇间语境知识，给出了在篇章语境中分析时间短语的算法TPPA，提出了通过时制验算来推导汉语篇章中时间短语的时制和事件的时制，最后在汉英机译系统ICENT中进行了实现，对已知写作时间的汉语篇章取得了较好的实验结果。     

Learning new programming languages: an analysis of the process and problems encountered

Experienced programmers transferring to a new language have a far easier time than the novice learning a first language. However, they still experience considerable difficulties. The objective in this exploratory study was to characterize the kinds of learning and transfer that take place in the early stages of using a new programming language and where difficulties develop. 'Think-aloud' protocols were videotaped as subjects went about trying to write a program in a new programming language. Subjects used One of two unfamiliar languages, one similar to their known language (Pascal) and the other dissimilar. Three types of analyses were done on the recorded protocols: a procedural analysis showing the activities the subjects engaged in as they learned the new language, a programming knowledge analysis showing in which areas of program development difficulties were encountered, and a solutions analysis showing how successful the programmers were at using unique features of the new language. We found that the procedures programmers used to learn a new language were independent of the language being learned. The slight differences that existed in procedures were between levels of expertise. Programmers spent the majority of their time reading a language textbook. The programming knowledge analysis showed that programmers main area of concentration was planning how to implement their approach given the constructs available in the language. We observed many iterations of programmers trying to implement plans, failing and having to revise their plans. Examination of the subjects solutions and implementation approaches in Pascal led us to believe that programmers learning a new language are often biased by their implementation of algorithms in previous languages.     

Learning new programming languages: an analysis of the process and problems encountered

Abstract

Experienced programmers transferring to a new language have a far easier time than the novice learning a first language. However, they still experience considerable difficulties. The objective in this exploratory study was to characterize the kinds of learning and transfer that take place in the early stages of using a new programming language and where difficulties develop. ‘Think-aloud’ protocols were videotaped as subjects went about trying to write a program in a new programming language. Subjects used One of two unfamiliar languages, one similar to their known language (Pascal) and the other dissimilar. Three types of analyses were done on the recorded protocols: a procedural analysis showing the activities the subjects engaged in as they learned the new language, a programming knowledge analysis showing in which areas of program development difficulties were encountered, and a solutions analysis showing how successful the programmers were at using unique features of the new language. We found that the procedures programmers used to learn a new language were independent of the language being learned. The slight differences that existed in procedures were between levels of expertise. Programmers spent the majority of their time reading a language textbook. The programming knowledge analysis showed that programmers main area of concentration was planning how to implement their approach given the constructs available in the language. We observed many iterations of programmers trying to implement plans, failing and having to revise their plans. Examination of the subjects solutions and implementation approaches in Pascal led us to believe that programmers learning a new language are often biased by their implementation of algorithms in previous languages.     

Benchmarking a multi-access system

This paper describes an attempt to test the multi-access capabilities of three widely differing systems. Some of the difficulties encountered in trying to find a common approach are detailed.     

CONSISTENCY CHECKING ALONG TIME

Most model-based approaches to diagnosis require a consistency-checking procedure to perform their task. When dealing with a dynamically changing system, such a procedure must take into account time-varying data. This requires suitable techniques for reasoning over time. Additional difficulties arise when delays are involved in interactions between variables. The worst case occurs when some of the delays are completely unspecified. This report presents an approach to consistency checking that handles qualitative models of dynamic systems exhibiting time lags. A component-centered ontology is adopted to model the structure of the physical system, and an episode-based approach is adopted for representing its behavior over time. An example consisting of a physical process exhibiting transportation lags is used to illustrate the power of the approach. We present algorithms and an implementation in PROLOG called C-CAT (consistency checking along time). Some meaningful outputs from the program are used as examples. The solution proposed represents an extension to B. C. Williams' temporal constraint propagation methodology. It also extends the applicability range of existing approaches to model-based diagnosis, permitting its use in tasks such as online diagnosis of dynamic systems.     

Collaborative environments and task design in the university

This paper is a revision of the field of the so called “Computer supported collaborative learning”, showing its potentialities as well as some of the problems. It also makes an analysis of the types of task that are well designed to be collaborative, in the thread to find “genuine interdependent” tasks and not just subtasks that are only joined. At last, it raises some difficulties encountered when trying to think collaborative learning as a form of group proximal development zone.     

Automating knowledge acquisition: a propositional approach torepresenting expertise as an alternative to repertory gridtechnique

Repertory grid technique plays a central role in the elicitation methodology of many well-reported knowledge acquisition tools or workbenches. However, the dependability of these systems is low where the technique breaks down or proves inadequate due to limited expressive power and other problems. The paper introduces an alternate approach based on Personal Construct Theory that elicits an expert's knowledge as a network of terms that constitutes a propositional formalism. An extended example is used to both highlight the difficulties encountered using repertory grids and illustrate how these are overcome using the proposed approach. The results of an empirical study are presented where an experienced clinician compared the knowledge structures that she constructed for a diagnostic task using each elicitation technique. Furthermore, although the network representation is amenable to inductive learning methods for generating production rules, an inference method is demonstrated which reveals the formalism's categorical reasoning potential. The authors conclude that it is more appropriate to classify such methods as either mediating or immediate rather than the knowledge structures they employ. The paper contributes to a better understanding of constructivist formalisms developed for knowledge acquisition     

A math-heuristic Dantzig-Wolfe algorithm for capacitated lot sizing

The multi-item multi-period capacitated lot sizing problem with setups (CLST) is a well known optimization problem with wide applicability in real-world production planning problems. Based on a recently proposed Dantzig-Wolfe approach we present a novel math-heuristic algorithm for the CLST. The major contribution of this paper lies in the presentation of an algorithm that exploits exact techniques (Dantzig-Wolfe) in a metaheuristic fashion, in line with the novel trend of math-heuristic algorithms. To the best of the authors’ knowledge, it is the first time that such technique is employed within a metaheuristic framework, with the aim of tackling challenging instances in short computational time. Moreover, we provide reasoning that the approach may be beneficial when additional constraints like perishability constraints are added. This also constitutes an important extension when looking at it from the view of solution methods.     

时间约束的RBAC模型及应用

基于角色的访问控制模(RBAC)自提出以来一直被视为用来进行访问控制的普遍方法。时间约束的RBAC模型是RBAC模型在时间约束上的扩充。描述了时间约束和时间约束模型,重点研究了时间约束模型的系统结构。分析了校园网计费系统的应用需求,设计并实现了基于时间约束机制模型TRBAC的访问控制实例。     

Causal knowledge elicitation based on elicitation failures

The paper presents an approach to causal knowledge elicitation supported by a tool directly used by the domain expert. This knowledge elicitation approach is characterized by trying to guess an interpretation of the knowledge entered by the expert. The tool (initially general), as it is used, self customizes its guessing capability, remembers failures in guessing (in order to avoid similar failures in the future) and when they occur elicits their explanations. Even in this case, elicitation is supported by guessing on the basis of previous similar failures. The resulting overall effect is that the tool digs up tenaciously causal knowledge from the expert's mind, playing in this way a cooperative role for model building     

Knowledge-based public service transactions: An intelligent model-driven approach in co-learning contexts

Public administrations pursue the efficiency and quality of administrative services they offer as well as the reduction of time and operational costs in executing service transactions. However, some issues arise when trying to achieve these goals: (a) the lack of procedure formalization to describe public services, (b) a mechanism to guarantee services’ obligatory compliance with frequently changing legal regulations, and (c) the efficient deployment of service transactions in e-government platforms. The aim of this work is to identify the phases of the development cycle of eGovernment transactions and provide the support to automatize them efficiently by using a model driven engineering (MDE) and knowledge-based approach. The main source of the knowledge extraction process comes from a collaborative learning environment where public servants share acquired domain knowledge. A web survey has been conducted to evaluate the approach acceptance degree by software developers and domain experts. The main conclusion is that 61% of the surveyed experts strongly agree that our approach improves actual eGovernment transactions practices and the phases needed to develop them.     

Autonomic-computing approach to secure knowledge management: a game-theoretic analysis

The explosion of knowledge management systems (KMS) and the need for their wide accessibility and availability has created an urgent need for reassessing the security practices and policies in organizations. Security of these assets is a day-to-day job placing a tremendous cognitive load on information-technology (IT) professionals, which can make it almost impossible to manage the security aspects of KMS. Autonomic-computing systems are well suited to manage KMS, as they use high-level system objectives provided by administrators as the basis for managing the security of KMS. The authors model the self-protection and self-healing configuration attributes in autonomic systems through game-theoretic models. The proposed modeling approach progressively moves from a manual intervention-oriented security setup to an autonomic security setup. This allows the authors to compare and contrast the different approaches and provide insights on their applicability to different security environments. The authors find that moving to a partial autonomic system with self-healing mechanisms can provide a stable environment for securing enterprise knowledge assets and can reduce hacking. It is beneficial to implement an autonomic system when manual investigation costs are higher and/or when the volume of malicious traffic is very low. An autonomic approach is especially attractive when it is difficult to impose penalties on malicious users. Autonomic systems can be effective in securing organizational knowledge assets and in reducing the potential damage from malicious users.     

从C. S. Peirce的存在图表到John F. Sowa的概念图表的形成来看人工智能的应用和发展

John F.Sowa的概念图表在现代人工智能的研究中得到了广泛的应用。本文从符号记号学和语言学的角度分析了从C.S.Peirce存在逻辑图表到John F.Sowa概念图表形成的过程。文章指出，存在图表是概念图表形成的基础，而概念图表是在存在图表的基础上对高阶图形逻辑的完善和发展。在分析过程中同时得出语言学和符号记号学的研究在人工智能发展中起到了重要作用；而在人工智能的研究和发展中跨学科研究是至关重要的。     

Mechanism-based emulation of dynamic simulation models: Concept and application in hydrology

Many model-based investigation techniques, such as sensitivity analysis, optimization, and statistical inference, require a large number of model evaluations to be performed at different input and/or parameter values. This limits the application of these techniques to models that can be implemented in computationally efficient computer codes. Emulators, by providing efficient interpolation between outputs of deterministic simulation models, can considerably extend the field of applicability of such computationally demanding techniques. So far, the dominant techniques for developing emulators have been priors in the form of Gaussian stochastic processes (GASP) that were conditioned with a design data set of inputs and corresponding model outputs. In the context of dynamic models, this approach has two essential disadvantages: (i) these emulators do not consider our knowledge of the structure of the model, and (ii) they run into numerical difficulties if there are a large number of closely spaced input points as is often the case in the time dimension of dynamic models. To address both of these problems, a new concept of developing emulators for dynamic models is proposed. This concept is based on a prior that combines a simplified linear state space model of the temporal evolution of the dynamic model with Gaussian stochastic processes for the innovation terms as functions of model parameters and/or inputs. These innovation terms are intended to correct the error of the linear model at each output step. Conditioning this prior to the design data set is done by Kalman smoothing. This leads to an efficient emulator that, due to the consideration of our knowledge about dominant mechanisms built into the simulation model, can be expected to outperform purely statistical emulators at least in cases in which the design data set is small. The feasibility and potential difficulties of the proposed approach are demonstrated by the application to a simple hydrological model.     

Bayesian estimation via sequential Monte Carlo sampling—Constrained dynamic systems

Nonlinear and non-Gaussian processes with constraints are commonly encountered in dynamic estimation problems. Methods for solving such problems either ignore the constraints or rely on crude approximations of the model or probability distributions. Such approximations may reduce the accuracy of the estimates since they often fail to capture the variety of probability distributions encountered in constrained linear and nonlinear dynamic systems. This article describes a practical approach that overcomes these shortcomings via a novel extension of sequential Monte Carlo (SMC) sampling or particle filtering. Inequality constraints are imposed by accept/reject steps in the algorithm. The proposed approach provides samples representing the posterior distribution at each time point, and is shown to satisfy the same theoretical properties as unconstrained SMC. Illustrative examples show that results of the proposed approach are at least as accurate as moving horizon estimation, but computationally more efficient and in addition, the approach indicates the uncertainty associated with these estimates.     

Analysing inconsistent first-order knowledgebases

It is well-known that knowledgebases may contain inconsistencies. We provide a framework of measures, based on a first-order four-valued logic, to quantify the inconsistency of a knowledgebase. This allows for the comparison of the inconsistency of diverse knowledgebases that have been represented as sets of first-order logic formulae. We motivate the approach by considering some examples of knowledgebases for representing and reasoning with ontological knowledge and with temporal knowledge. Analysing ontological knowledge (including the statements about which concepts are subconcepts of other concepts, and which concepts are disjoint) can be problematical when there is a lack of knowledge about the instances that may populate the concepts, and analysing temporal knowledge (such as temporal integrity constraints) can be problematical when considering infinite linear time lines isomorphic to the natural numbers or the real numbers or more complex structures such as branching time lines. We address these difficulties by providing algebraic measures of inconsistency in first-order knowledgebases.     

Modeling temporal processes via belief networks and Petri nets,with application to expert systems

We show how Bayesian belief networks (BNs) can be used to model common temporal knowledge. Two approaches to their structuring are proposed. The first leads to BNs with nodes representing states of a process and times spent in such states, and with a graphical structure reflecting the conditional independence assumptions of a Markovian process. A second approach leads to BNs whose topology represents a conditional independence structure between event-times. Once required distributional specifications are stored within the nodes of a BN, this becomes a powerful inference machine capable, for example, of reasoning backwards in time. We discuss computational difficulties associated with propagation algorithms necessary to perform these inferences, and the reasons why we chose to adopt Monte Carlo-based propagation algorithms. Two improvements to existing Monte Carlo algorithms are proposed; an enhancement based on the principle of importance sampling, and a combined technique that exploits both forward and Markov sampling. Finally, we consider Petri nets, a very interesting and general representation of temporal knowledge. A combined approach is proposed, in which the user structures temporal knowledge in Petri net formalism. The obtained Petri net is then automatically translated into an equivalent BN for probability propagation. Inferred conclusions may finally be explained with the aid of Petri nets again.     

用带时钟变量的线性时态逻辑扩充Object-Z*

Object-Z是形式规格说明语言Z的面向对象扩充,适合描述大型面向对象软件规格说明,但它不能很好地描述连续性实时变量和时间限制。线性时态逻辑能够描述实时系统,但不能很好地处理连续时间关系,也不能很好地模块化描述形式规格说明。首先用时钟变量扩充线性时态逻辑,接着提出了一个方法——用带时钟变量的时态逻辑(LTLC)来扩充Object-Z。用LTLC扩充的Object-Z是一个模块化规格说明语言,是Object-Z语法和语义的最小扩充,其最大优点在于它能方便地描述和验证复杂的实时软件规格说明。     

多智体系统时序认知规范的SPIN模型检测

SPIN模型检测器主要用来检测线性时序逻辑描述的规范,而多智体系统的规范采用时序认知逻辑描述比较方便。本文着重讨论了如何利用SPIN模型检测线性时序认知逻辑的方法,根据局部命题的理论,将模型检测知识算子和公共算子表述的规范规约为模型检测线性时序逻辑的问题,从而使SPIN的检测功能由线性时序逻辑扩充到线性时序认知逻辑。本文通过一个RPC协议分析实例来说明模型检测线性时序认知逻辑的方法。