A Survey of Languages for Specifying Dynamics A Knowledge Engineering Perspective
con97
In: Proceedings of the 2nd European Conference on Cognitive Science, Manchester, UK, April 9-11, 1997A Dynamic Theory of Implicit ContextBoicho KokinovInstitute of Mathematics and Cognitive Science DepartmentBulgarian Academy of Sciences New Bulgarian UniversityBl.8, Acad. G. Bonchev Str.21 Montevideo Str.,Sofia 1113, BULGARIA Sofia 1635, BULGARIAe-mail: KOKINOV@BGEARN.ACAD.BG e-mail: KOKINOV@COGS.NBU.ACAD.BGAbstractSeveral distinctions between various concepts of context are discussed: internal vs. external,intrinsic vs. model-based, and implicit vs. explicit. Finally, a dynamic theory of implicit,intrinsic, internal context is briefly discussed and its application to a context-sensitive generalcognitive architecture DUAL and a context-sensitive model of human reasoning, AMBR, arebriefly outlined.Keywords: implicit context, intrinsic context, internal context, dynamic approachCONTEXT EFFECTSPsychologists have demonstrated context effects on virtually all cognitive processes. Thus, for example, context effects on perception have been demonstrated by Gestalt psychologists in various forms: different interpretations of ambiguous figures; visual illusions depending on the background elements or on the presence of other stimuli. In language comprehension context effects can be exemplified by lexical, syntax, semantic, inference, thematic and other types of context effects (Tiberghien, 1988). In memory studies various effects of context have been demonstrated – context-dependence of recall and even recognition, memory illusions in false recognition, context-based interference, priming effects, etc. (Davies & Thomson, 1988a, Levandowsky, Kirsner, & Bainbridge, 1989). In problem solving various forms of context effects have been demonstrated: functional fixedness (Maier, 1931, Dunker, 1945), set effects (Luchins, 1942), lack of transfer from previous problem solving experience (Gick & Holyoak, 1980), priming effects (Kokinov, 1990, Schunn & Dunbar, 1996), effects of casual elements of the environment (Maier, 1931, Kokinov & Yoveva, 1996). In decision-making various context effects have been demonstrated: framing effects – the effects of alternative descriptions, e.g. percentage died or saved; effects of alternative methods of elicitation; and effects of added alternatives (Shafir, Simonson, & Tversky, 1993). Barsalou (1993) demonstrated context effects on concepts characterisation.Thus every change in the experimental conditions that proved to change the behaviour of the subject is called a context effect. In the case of perception, language comprehension, problem-solving, and decision-making most often context effects can be described as a change to the external environment which causes a change to the cognitive performance or subjects’ response; in the case of learning, memory, and problem solving transfer (like in analogical problem solving) context effects typically demonstrated are due to the change of the external environment between the initial learning stage and the later memory or problem solving test.According to the dynamic theory of context (Kokinov, 1995) context is the set of entities that influence human (or system’s) behaviour on a particular occasion, i.e. the set of elements that produce context effects. INTERNAL VS. EXTERNAL CONTEXTTwo different notions of context have been used in the literature which will be called here external and internal context. External context refers to the physical and social environment or the setting within which the subject’s behaviour is generated (Davies & Thomson, 1988a, Roediger & Srinivas, 1993). Internal context refers to subject’s current mental state (Sperber & Wilson, 1986, Kintsch, 1988, Giunchiglia &Bouquet, in press) withinwhich the subject’s behaviour is generated.1 Obviously there is a relation between the external and internal contexts – the external context is being perceived and this changes the mental state of the subject, i.e. his/her internal context. However, what part of the external context will be perceived and reflected is purely subjective and depends on subject’s current state (incl. goals, currently used social and common sense schemas, currently active concepts, etc.), i.e. on subject’s internal context.Psychological experiments are typically manipulating external context and very rarely the internal context (exceptions are mood-dependence studies and priming effects), however, what we are really interested in are the mechanisms of context influence, i.e. how the internal context is being formed and how it is used.The dynamic theory of context (Kokinov, 1995) accepts that the internal context is being formed by the interaction between at least three processes: perception of the environment or building new representations; accessing memory traces or reactivating and possibly modifying old representations; and reasoning or constructing new representations. It also assumes that it is the internal context which on its turn influences perception, memory, and reasoning processes.INTRINSIC CONTEXT VS. MODEL-BASED CONTEXTThe currently dominating view on context is model-based, i.e. it assumes the existence of a model of the cognitive process according to which some factors (e.g. the instructions, the stimuli, the goals of the subject) are variable and important for the process and are called inputs to the model while others are supposed to be either constant or irrelevant to the process and therefore they are not included in the model’s inputs (they are “hardwired” in the model and can be considered as constants or parameters that rarely change). Thus if such a supposedly irrelevant factor turns out to influence subject’s behaviour then this is called context effect and model’s failure to predict the outcome of the experiment is explained by “interaction effect”. This view has been expressed very clearly by Bernard Amy (1989): “Context effects are interactive effects, in the physical sense of the term. The course of an ongoing central process is modified by interaction with other ongoing processes. Contexts act not on the inputs of the central process but on the function itself”. Similar views have been expressed by Davies & Thomson (1988b): “All distinctions of context assume a distinction between stimulus and setting, figure and ground”; and Lockhart (1988): “The vary phrase context effects assumes that it is both possible and useful to distinguish a core stimulus from other aspects of the total stimulus configuration and that core and context can be varied independently”.This view implies that the observer (external or internal) focuses on a specific part of the system’s behaviour considering it as central and modelling it, and allows other contextual factors to influence or modify this central process, to redirect its focusing on different input data. This view has been expressed also by Cristiano Castelfranchi in the electronic discussion prior to the workshop, he has insisted on the relational nature of the notion of context (“context of” a process). Then, of course, the problem arises which factors to include in the model and which to leave out for the context. In my opinion such notion of context has not a high theoretical value, as it becomes equivalent to explanatory “trash” – everything which we cannot explain so far is context.The dynamic theory of context assumes that the cognitive system is continuously changing and there is no clear difference between changing the input and changing the system itself. It assumes also that there is one global cognitive process described as the evolution of the system over time and that all more specific or partial processes that we are considering and modelling are only abstractions which allow us to simplify our scientific endeavour. Having this in mind then there is only one single global context for all these “abstract” processes –the state of the dynamic system. This is called intrinsic context of the cognitive system and is the focus of research of the dynamic theory of context. The intrinsic context includes what others characterise as “figure”, i.e. subject’s current goals and focus of attention are part of the context and might be considered as its core or central part. The problem is how to describe this global state of the system. Before we try to answer this question we shall make another useful distinction.IMPLICIT CONTEXT, EXPLICIT CONTEXT, AND META-CONTEXTThere are at least two different meanings of explicit/implicit used in the literature and both are relevant. The first one concerns the content of the memory traces – whether they include a representation of contextual features. For example, whether subjects encode information about some specific feature of the room setting, of the experimenter, etc. when asked to remember a list of words.2 The traces may even include a meta-representation –1 In the situated approach context is considered to be the interaction between the environment and subject’s mind/body, thus there is no such separation between internal and external context (Shanon, 1990).2 This reading of explicit/implicit assumes model-based notion of context: the model assumes that room setting and experimenter’s face are irrelevant to the task of list memorising and therefore are contextual factors. However, it might well be the case that for some subjects the vary experience of participating in the experimenta representation of the context itself (i.e. to represent the context as an object) – e.g. “This law is true only in the domain of micro-particles, but not in the macro world”. This type of meta-representation is needed when subjects use several contexts within a short period of time and have to control the process of switching from one context to another. AI models tend to use the notion of context in this meta-representation meaning (McCarthy, 1991, Giunchiglia, 1993). Contextual features might also be implicitly represented, i.e. they could be inferred (reconstructed) from the content of other traces, e.g. the fact that there were a lot of broken glasses during the car crash last week might be inferred from our schema for a car crash (and might even be false in that particular case).The second reading of implicit/explicit distinction (which is more relevant to the dynamic theory of context) concerns the subject’s level of awareness of the very fact of existence of some memory trace. According to the dynamic theory of context the level of awareness is a graded (continuous) and dynamic characteristic of every memory element, i.e. there are various degrees of awareness measured by the amount of processing capacity currently associated with this particular element. In other words accessibility is a key measure of explicitness. At the lowest end a memory trace could be completely inaccessible (neither consciously, nor unconsciously) at a particular moment, then it could be only unconsciously (implicitly) available (demonstrated by priming effects, but failing to be recognised in an explicit memory task, for example), then it could be consciously available (demonstrated by a standard recall or recognition task), and finally the very fact of existence of the memory trace might be consciously available (demonstrated in a meta-cognitive “feeling of knowing” experiment). Both isolated contextual features and the context as a whole might be implicitly or explicitly available. While explicit availability allows for controlled use of contextual information, implicit availability serves a very important role in human cognition – it is a fast and cheap way of automatic control of resource allocation. If resource allocation control was performed by conscious information processing strategies only, then human cognition would be very slow and inflexible.DYNAMIC THEORY OF IMPLICIT CONTEXTAccording to the dynamic theory (Kokinov, 1995) the implicit intrinsic internal context is considered as the dynamic fuzzy set of all memory elements (mental representations or operations) accessible for mental processing at a particular instant of time. Accessibility of a memory element is modelled by the degree of its activation which is supposed to reflect its estimated relevance (the better the element is connected to the other currently active elements the more relevant it is supposed to be). Thus context is implicitly represented by the distribution of activation over the set of all memory elements. Each pattern of activation represents a specific context. As activation is graded the membership of a memory element to this context is also graded and therefore its relevance is graded as well. This results in different amount of processing resources being made available to different memory elements (in DUAL cognitive architecture (Kokinov, 1994b this is modelled by varying the speed of working of the mental operations and by varying the degree of accessibility of mental representations).This does not exclude to have explicit meta-context representations in addition. The implicit intrinsic internal context (i.e. the mental state of the cognitive system) can be self-observed and part of it (which is consciously accessible) can be explicitly represented in a local structure and afterwards referred to. However, this is always a partial representation of the actual mental state.The dynamic theory of context is being tested in two ways: (a) by psychological experiments on context effects on problem solving which have demonstrated that subjects are influenced in their problem solving activity even by seemingly irrelevant casual stimuli from the environment without always being aware of this influence (Kokinov, Yoveva, 1996, Kokinov, Hadjiilieva, Yoveva, to appear); and (b) by computer simulations of analogical problem solving (Kokinov, 1994a) replicating the priming effects shown earlier and predicting some of the data in the later conducted psychological experiments.(characterised by the experimenter, the room, the instructions, etc.) to be subjectively more important than the artificial list of words and therefore to encode this “contextual” information even in more details than the “target”. And because this subjective importance varies from person to person and from experiment to experiment, psychologists obtain controversial data on context effects (Davies & Thomson, 1988a).ACKNOWLEDGMENTSThe ideas presented here have been inspired by the extensive discussions the author had with Cristiano Castelfranchi during his stay at the Institute of Psychology at CNR in Rome, with Fausto Giunchiglia and Paolo Bouquet during the Cognitive Science Summer School in Sofia as well as by the electronic discussion in the context discussion group. This research has been partially supported by grant OHN406 “Context and Priming Effects on High-Level Cognitive Processes” from the Bulgarian National Science Foundation. REFERENCESAmy, B. (1989). Contextual Cognitive Machines. In: Tiberghien, G. (ed.) Advances in Cognitive Science, vol.2: Theory and Applications. Ellis Horwood, Chichester.Barsalou, L. (1993). Flexibility, Structure, and Linguistic Vagary in Concepts: Manifestations of a Compositional System of Perceptual Symbols. In: Collins, A., Gathercole, S., Conway, M., & Morris, P.(eds.) Theories of Memory. Erlbaum, Hillsdale, NJ.Dunker, K. (1945). On Problem Solving. Psychological Monographs, 58:5, (Whole No. 270).Gick, M. & Holyoak, K. (1980). Analogical Problem Solving. Cognitive Psychology, 12, 306-355. Giunchiglia, F. (1993). Contextual Reasoning. In: Epistemologia - Special Issue on I Linguaggi e le Machine, 16, 345-364.Giunchiglia, F. & Bouquet, P. (in press). Introduction to Contextual Reasoning. In: Kokinov. B. (ed.) Perspectives on Cognitive Science, vol. 3, NBU Press, Sofia.Davies, G. & Thomson, D. (1988a). Memory in Context: Context in Memory. John Wiley, Chichester. Davies, G. & Thomson, D. (1988b). Context in Context. In: Davies, G. & Thomson, D. (eds.) Memory in Context: Context in Memory. John Wiley, Chichester.Kintsch, W. (1988). The Role of Knowledge in Discourse Comprehension: A Construction-Integration Model.Psychological Review, 95(2), 163-182Kokinov, B. (1990). Associative Memory-Based Reasoning: Some Experimental Results. In: Proceedings of the 12th Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Lawrence Erlbaum Associates. Kokinov, B. (1994a) A Hybrid Model of Reasoning by Analogy. Chapter 5. in: K. Holyoak & J. Barnden (eds.) Analogical Connections, Advances in Connectionist and Neural Computation Theory, vol.2, Ablex Publ. Corp.Kokinov, B. (1994b) The Context-Sensitive Cognitive Architecture DUAL. In: Proceedings of the 16th Annual Conference of the Cognitive Science Society. Erlbaum, Hillsdale, NJ.Kokinov, B. (1995). A Dynamic Approach to Context Modeling. In: Brezillon, P. Abu-Hakima, S. (eds.) Working Notes of the IJCAI’95 Workshop on Modelling Context in Knowledge Representation and Reasoning. IBP, LAFORIA 95/11.Kokinov, B., Yoveva, M. (1996). Context Effects on Problem Solving. In: Proceedings of the 18th Annual Conference of the Cognitive Science Society. Erlbaum, Hillsdale, NJ.Kokinov, B., Hadjiilieva, K., & Yoveva, M. (to appear). Is a Hint Always Useful in Problem Solving? The Influence of Pragmatic Distance on Context Effects.Levandowsky, S., Kirsner, K., & Bainbridge, V. (1989). Context Effects in Implicit Memory: A Sense-Specific Account. In: Lewandowsky, S., Dunn, J., & Kirsner, K. (eds.) Implicit Memory: Theoretical Issues.Erlbaum, Hillsdale, NJ.Lockhart, R. (1988). Conceptual Specificity in Thinking and Remembering. In: Davies, G. & Thomson, D.(eds.) Memory in Context: Context in Memory. John Wiley, Chichester.Luchins, A. (1942). Mechanization in Problem Solving: The Effect of Einstellung. Psychological Monographs, 54:6, (Whole No. 248).Maier, N. (1931). Reasoning in Humans II: The Solution of a Problem and it Appearance in Consciousness.Journal of Comparative Psychology, 12, 181-194.McCarthy, J. (1991). Notes on Formalizing Context. unpublished, 1991Roediger, H. & Srinivas, K. (1993). Specificity of Operations in Perceptual Priming. In: Graf, P. & Masson, M. (eds.) Implicit Memory: New Directions in Cognition, Development, and Neuropsychology. Erlbaum, Hillsdale.Schunn, C. & Dunbar, K. (1996). Priming, Analogy, and Awareness in Complex Reasoning. Memory and Cognition, 24, 271-284.Shafir, E., Simonson, I. & Tversky, A. (1993). Reason-Based Choice. Cognition, 49, 11-36.Shanon, B. (1990). What is Context? J. for the Theory of Social Behavior, 20, 157-166.Sperber, D. & Wislon, D. (1986). Relevance. Communication and Cognition. Harvard University Press, Cambridge, MA.Tiberghien, G. (1988). Language Context and Context Language. In: Davies, G. & Thomson, D. (eds.) Memory in Context: Context in Memory. John Wiley, Chichester.。
动态语言面面观
该 工具 集 能 让 开 发 人 员在 Ｓ ＯＡ 中开
发 服 务 时 使 用 Ｐ Ｐ和 Ｊｖ 。ｚｎ 也 推 出 Ｈ ａａ ｅ ｄ 了Ｐ ＨＰ的开 发框 架 ， 些 技 术将 帮 助 软 件 这 公 司创 建 企 业级 Ｐ ＨＰ应 用 。 Ｐ ＨＰ会 变
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在 Ｊ ｖ 上产生新的语言 ａａ
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时 更 改 类 结 构 或 相 关 配 置 。 另 外 很 多 技
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到企业级 Ｗｅ ｂ应 用 ，Ｐ ＨＰ必将 成为 最 有
自然语言处理
自然语言处理2002．11．09中国科学院计算技术研究所1.综述.1.1. 绪论.1.1.1.背景,目标.1.1.1.1. 研究自然语言的动力1．语言是思维的裁体，是人际交流的重要工具。
在人类历史上以语言文字形式记载和流传的知识占到知识总量的80％以上。
就计算机的应用而言，据统计用于数学计算的仅占10％，用于过程控制的不到5％，其余85％左右都是用于语言文字的信息处理。
在这样的社会需求下，自然语言理解作为语言信息处理技术的一个高层次的重要方向，一直是人工智能界所关注的核心课题之一。
2．由于创造和使用自然语言是人类高度智能的表现，因此对自然语言理解的研究也有助于揭开人类智能的奥秘，深化我们对语言能力和思维本质的认识。
.1.1.1.2. 什么是计算语言学计算语言学（Computational Linguistics）指的是这样一门学科，它通过建立形式化的数学模型，来分析、处理自然语言，并在计算机上用程序来实现分析和处理的过程，从而达到以机器来模拟人的部分乃至全部语言能力的目的。
计算语言学（Computational Linguistics）有时也叫计量语言学（Quantitative Linguistics）, 数理语言学（Mathematical Linguistics）, 自然语言理解（Natural Language Understanding）, 自然语言处理（Natural Language Processing）, 人类语言技术（Human Language Technology）。
.1.1.1.3. 图灵测验在人工智能界，或者语言信息处理领域中，人们普遍认为可以采用著名的1950年描述的图灵试验(Turing Test )来判断计算机是否“理解”了某种自然语言。
.1.1.1.3.1.Turing模仿游戏(Imitation Game)●场景：男性被试、女性被试、观察者，3者在3个不同的房间，房间号分别为X, Y, O●规则：观察者用电传打字机与被试们通信，男性被试欺骗观察者、女性被试帮助观察者。
专四20篇完形填空专项训练(附答案)
1．Many students find the experience of attending university lectures to be a confusing and frustrating experience. The lecturer speaks for one or two hours, perhaps __1__ the talk with slides, writing up important information on the blackboard, __2__ reading material and giving out __3 __. The new student sees the other students continuously writing on notebooks and __4__ what to write. Very often the student leaves the lecture __5__ notes which do not catch the main points and __6__ become hard even for the __7__ to understand. Most institutions provide courses which __8__ new students to develop the skills they need to be __9__ listeners and note-takers.__10__ these are unavailable, there are many useful study-skills guides which __11__ learners to practice these skills __12__ . In all cases it is important to __13__ the problem __14__ actually starting your studies. It is important to __15__ that most students have difficulty in acquiring the language skills __16__ in college study. One way of __17__ these difficulties is to attend the language and study-skills classes which most institutions provide throughout the __18__ year. Another basic __19__ is to find a study partner __20__ it is possible to identify difficulties, exchange ideas and provide support.1.A.extending B.illustrating C.performing D.conducting2.A.attributing B.contributing C.distributing D.explaining3.A.assignments rmation C.content D.definition4.A.suspects B.understands C.wonders D.convinces5.A.without B.with C.on D.except6.A.what B.those C.as D.which7.A.teachers B.classmates C.partners D.students8.A.prevent B.require C.assist D.forbid9.A.effective B.passive C.relative D.expressive10.A.Because B.Though C.Whether D.If11.A.enable B.stimulate C.advocate D.prevent12.A.independently B.repeatedly C.logically D.generally13.A.evaluate B.acquaint C.tackle D.formulate14.A.before B.after C.while D.for15.A.predict B.acknowledge C.argue D.ignore16.A.to require B.required C.requiring D.are required17.A.preventing B.withstanding C.sustaining D.overcoming18.A.average B.ordinary C.normal D.academic19.A.statement B.strategy C.situation D.suggestion20.A.in that B.for which C.with whom D.such as1.【答案】B【解析】将第1，2，3题通盘考虑。
二语习得动态系统理论综述
Total.335December 2015（B）The Science Education Article Collects总第335期2015年12月（中）二语习得动态系统理论综述王一凡（山东农业大学外国语学院山东·泰安271018）中图分类号：G640文献标识码：A文章编号：1672-7894（2015）35-0141-02作者简介：王一凡（1990—），女，山东潍坊人，山东农业大学英语专业研究生在读，研究方向为二语习得。
摘要二语习得理论研究范式众多，每种范式都从不同的角度和侧面对二语习得研究做出解释，动态系统理论便是其中一个。
本文尝试对动态系统理论的理论框架、研究发展及其在二语习得领域的贡献和不足进行简单的介绍，为进一步的研究提供参考。
关键词二语习得动态系统理论综述Review of the Theory of Dynamic System on Second Lan 原guage Acquisition //Wang YifanAbstract There are many kinds of paradigms about theories're-search on second language acquisition (SLA),and each paradigm explains the SLA from different aspects and angels.The theory of dynamic system is one of them.This thesis tries to make a brief introduction on its theory framework,research development and its contribution and disadvantage in the field of SLA.Key words second language acquisition (SLA);dynamic system theory;review1引言自20世纪60年代二语习得成为一门独立的学科以来，二语习得的理论范围不断扩展，其研究领域已经大大超越了应用语言学的范畴，与社会学、心理学、教育学等多个领域息息相关。
毕业设计_英文翻译
9 Continuous-Time DynamicNeural Networks9 连续时间动态神经网络9.1 Dynamic Neural Network Structures: An Introduction9.2 Hopfield Dynamic Neural Network (DNN) and Its Implementation 9.3 Hopfield Dynamic Neural Networks (DNNs) as Gradient-like Systems9.4 Modifications of Hopfield Dynamic Neural Networks9.5 Other DNN Models9.6 Conditions for Equilibrium Points in DNN9.7 Concluding RemarksProblems9.1动态神经网络结构导论9.2动态的Hopfield神经网络（DNN）及其实现9.3动态的Hopfield神经网络（DNNS）为梯度样系统9.4修改动态的Hopfield神经网络9.5其他DNN模型9.6条件平衡点在DNN9.7结语问题As seen in the previous chapters, a neural network consists of many interconnected simple processing units, called neurons, which form the layered configurations. An individual neuron aggregates its weighed inputs and yields an output through a nonlinear activation function with a threshold. In artificial neural networks there are three types of connections: intralayer, interlayer, and recurrent connections. The intralayer connections, which are also called lateral connections or cross-layer connections, are links between neurons in the same layer of the network. The interlayer connections are links between neurons in different layers. The recurrent connections provide self-feedback links to the neurons. In interlayer connections, the signals are transformed in one of the two ways: either feedforward or feedback.就像在前面的章节中，神经网络由许多互连简单处理单元，称为神经元，形成层状结构。
《普通语言学》教案
教案课程名称：普通语言学授课对象：英、法、德、日、俄研究生授课时间：2/周授课教师：张吉生第一章：普通语言学的研究领域1.1普通语言学是研究语言的科学●普通语言学是一门研究语言的科学，研究人类语言具有共性的现象、规则、原理。
像其它系统性的研究学科一样。
普通语言学的研究不是静止的，有些观点是在变化的，对有些现象的理解、规则的制定、原理的解释可能因不同的专家学者而有所不同，主要表现在不同学派、不同流派之间的差异。
e.g.德语日语汉语ein Hut wie ein Kind; kodomo-rasii bo:si; 象小孩（一样）的一顶帽子子供のような帽子a hat like a child a child-like a hat like a child a hatNP NP NP N PP PP N PP N P N N P P Nexperiment + al ; *movement + almap, pam, ?pma, ?mpa, amp, *apm; ma, ?am; ? a, a 。
●普通语言学或对语言的科学研究不同于研究某种具体语言，如研究汉语，或研究日语、韩语、德语、法语等。
1.1.1 语言●全世界至今尚有至少六千余种语言，有些只有几十个人能讲，有些只有几个老人能讲，有的甚至只有一个人能讲。
汉语使用人口最多，英语使用地区最广。
汉语有7大方言，共约九百多方言。
按人口算，最大的是北方方言，第二是吴方言。
吴方言区内有约90多种吴方言。
●任何一种语言，无论其文明程度如何，使用人口多少，使用地域多大，都是值得研究和学习的。
（现代语言学理论的发展得益于印地安语）●语言学研究的对象包括人类用来交际的任何语言（包括哑语）和死亡语言，如拉丁语、古希腊语、古英语等。
1.1.2 普通语言学研究领域及方法普通语言学涉及研究语言的好几个相关领域，每个领域都可以从语言学理论和语言的实际使用的角度分析研究语言。
最主要的几个领域包括：描写语言学、历史语言学、比较语言学、对比语言学、功能语言学、生成语言学。
上海初三一模备考完形填空之联系上下文
【知识梳理】1．1. 题型概说完形填空考查考生语篇分析方面的能力，如阅读能力、逻辑推理及分析归纳能力、综合判断能力。
这种题型归纳起来有如下特点：（1）重点考查考生对短文的整体理解、上下文的段落衔接，情理分析及推理判断能力。
（2）针对初中学生的认知水平，一般采用故事体裁，尽量避免专业性太强的文章，文章长度一般在200个单词左右。
（3）以意义选择为主。
完形填空题侧重对语言应用能力、阅读理解能力的考查。
它考查的主要内容为：1）从词语层面：主要考词语辨析、固定搭配与习惯表达；2）从句子层面：主要考句子意义的逻辑性与合理性；3）从篇章层面：主要考篇章的连贯和句与句之间的意义关系。
完形填空要求考生不仅要会运用自己学过的词汇和语法知识妥善地处理好每个单句，理解语义，还要处理好单句之间以及整个语篇的意义关系。
完形填空题以考实词（动词、名词、形容词、副词及相关短语）为主，难点主要是如何根据上下文的意义关系正确判断所需的用词。
2. 解题步骤在做完形填空题时，通常先弄清语境，并依据上下文进行合理的分析、判断，才能做出恰当的选择。
具体可分为以下三步：（1）通览全文，了解大意答题时，应先越过空格，通读全文，理顺大意，找出信息词。
这是做好完形填空题的关键。
因为完形填空的特点是着眼于整体理解。
我们如果把短文比作环环相扣的链条，那么由于空格的设置，“链条”从第二句起有些地方就脱节了。
有些同学习惯于提笔就填或边读边填，急于求成，然而，欲速则不达，结果往往由于“只见树木不见森林”而事半功倍。
因此我们应该依据首句给的启示，通过逻辑思维，借助短文中关键词所提供的信息，越过空格，尽快把全文读完，建立语言的整体感，帮助我们了解短文大意。
（2）综合考虑，先易后难78. A. everything B. anything C. nothing D. something79. A. longer B. stricter C. lighter D. easier80. A. If B. Even if C. Now that D. As long as【答案】75-80 CABADB【分析】75.survive 从...中缓过来；存活escape逃脱；beat打败；change改变句意为如何从考试季缓过来76.process过程，句意为做笔记、听讲座是学习过程中的第一步。
中南大学人工智能习题
中南大学人工智能习题第一章绪论1-1. 什么是人工智能？试从学科和能力两方面加以说明。
1-2. 在人工智能的发展过程中，有哪些思想和思潮起了重要作用？1-3. 为什么能够用机器（计算机）模仿人的智能？1-4. 现在人工智能有哪些学派？它们的认知观是什么？1-5. 你认为应从哪些层次对认知行为进行研究？1-6. 人工智能的主要研究和应用领域是什么？其中，哪些是新的研究热点？第二章知识表示方法2-1 状态空间法、问题归约法、谓词逻辑法和语义网络法的要点是什么？它们有何本质上的联系及异同点?2-2 设有3个传教士和3个野人来到河边，打算乘一只船从右岸渡到左岸去。
该船的负载能力为两人。
在任何时候，如果野人人数超过传教士人数，那么野人就会把传教士吃掉。
他们怎样才能用这条船安全地把所有人都渡过河去?2-3 利用图2.3，用状态空间法规划一个最短的旅行路程：此旅程从城市A开始，访问其他城市不多于一次，并返回A。
选择一个状态表示，表示出所求得的状态空间的节点及弧线，标出适当的代价，并指明图中从起始节点到目标节点的最佳路径。
2-4 试说明怎样把一棵与或解树用来表达图2.28所示的电网络阻抗的计算。
单独的R、L 或C可分别用R、jωL或1/jωC来计算，这个事实用作本原问题。
后继算符应以复合并联和串联阻抗的规则为基础。
图2.282-5 试用四元数列结构表示四圆盘梵塔问题，并画出求解该问题的与或图。
2-6 把下列句子变换成子句形式：(1) ( x)｛P(x)→P(x)｝(2) x y(On(x,y)→Above(x,y))(3) x y z(Above(x,y)∧Above(y,z)→Above(x,z))(4) ～｛( x)｛P(x)→｛（y）［p(y)→p(f(x,y))］∧( y)［Q(x,y)→P(y)］｝｝｝2-7 用谓词演算公式表示下列英文句子(多用而不是省用不同谓词和项。
例如不要用单一的谓词字母来表示每个句子。
APPLICATIONS
INCREMENTAb INTERPRETATION: T H E O R Y , A N D RF, L A T I O N S H I P T O D Y N A M I C David Milward & Robin Cooper
SEMANTICS*
Centre for Cognitive Science, University of Edinburgh 2, Buccleuch Place, Edinburgh, EH8 91,W, Scot,land, davidm@
APPLICATIONS
Following the work of, for example, Marslen-Wilson (1973), .lust and Carpenter (1980) and Altma.nn al]d Steedrnan (1988), it has heroine widely accepted that semantic i11terpretation in hnman sentence processing can occur beibre sentence boundaries and even before clausal boundaries. It is less widely accepted that there is a need for incremental inteபைடு நூலகம்pretation in computational applications. In the [970s and early 1980s several compntational implementations motivated the use of' incremental in-. terpretation as a way of dealing with structural and lexical ambiguity (a survey is given in Haddock 1989). A sentence snch as the following has 4862 different syntactic parses due solely to attachment ambiguity (Stabler 1991). 1) I put the bouquet of flowers that you gave me for Mothers' Day in the vase that you gave me for my birthday on the chest of drawers that you gave me lbr Armistice Day. Although some of the parses can be ruled out using structural preferences during parsing (such as [,ate C'losure or Minimal Attachment (Frazier 1979)), ex traction of the correct set of plausible readings requires use of real world knowledge. Incremental interpretation allows on-line semantic tiltering, i.e. parses of initial fragments which have an implausible or anolnalous interpretation are rqiected, thereby preven*'.lPhis research was supported by the UK Science and Gnglneerlng l~.esearch Council, H,esearch G r a n t 1tR30718.
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ASurveyofLanguagesforSpecifyingDynamics:AKnowledgeEngineeringPerspective
PascalvanEck1JoeriEngelfriet1DieterFensel1FrankvanHarmelen1YdeVenema2MarkWillems3
AbstractDuringthelastyears,anumberofformalspecificationlanguagesforknowledge-basedsystemshasbeendeveloped.Characteristicforknowledge-basedsystemsareacomplexknowledgebaseandaninferenceenginewhichusesthisknowledgetosolveagivenproblem.Specificationlanguagesforknowledge-basedsystemshavetocoverbothaspects.Theyhavetoprovidemeanstospecifyacomplexandlargeamountofknowledgeandtheyhavetoprovidemeanstospecifythedynamicreasoningbehaviourofaknowledge-basedsystem.Thispaperfocusesonthesec-ondaspect.Forthispurpose,wesurveyexistingapproachesforspecifyingdy-namicbehaviourinrelatedareasofresearch.Infact,wehavetakenapproachesforthespecificationofinformationsystems(LanguageforConceptualModellingandTROLL),approachesforthespecificationofdatabaseupdatesandlogicprogram-ming(TransactionLogicandDynamicDatabaseLogic),andthegenericspecifi-cationframeworkofAbstractStateMachines.
Keywords:specificationlanguages,knowledge-basedsystems,dynamics,infer-encecontrol,updatelogics
1IntroductionOverthelastyearsanumberofformalspecificationlanguageshavebeendevelopedfordescribingknowledge-basedsystems(KBSs).ExamplesareDESIRE[1,2];KARL[3,4];KBSSF[5,6];(ML)2[7];MLPM[8]andTFL[9].Inthesespecificationlan-guagesonecandescribebothknowledgeaboutthedomainandknowledgeabouthowtousethisdomainknowledgeinordertosolvethetaskwhichisassignedtothesystem.Ontheonehand,theselanguagesenableaspecificationwhichabstractsfromimple-mentationdetails:theyarenotprogramminglanguages.Ontheotherhand,theyenable
vanEngelfriet@mckinsey.com,URLs:http://www.cs.vu.nl/˜patveck,dieter,frankh2InstituteforLogic,LanguageandComputation,UniversityofAmsterdam,PlantageMuidergracht24,
1018TVAmsterdam,TheNetherlands.Email:yde@wins.uva.nl,URL:http://turing.wins.uva.nl/˜yde3BolesianB.V.,Steenovenweg19,5708HNHelmond,TheNetherlands.Email:mark@bolesian.nl
1adetailedandprecisespecificationofaKBSatalevelofprecisionwhichisbeyondthescopeofspecificationsinnaturallanguages.Surveysontheselanguagescanbefoundin[10,11,12].4
Acharacteristicpropertyofthesespecificationlanguagesresultsfromthefactthattheydonotaimatapurelyfunctionalspecification.Ingeneral,mostproblemstackledwithKBSsareinherentlycomplexandintractable(seee.g.[13]and[14]).Aspecifica-tionhastodescribenotjustarealizationofthefunctionality,butonewhichtakesintoaccounttheconstraintsofthereasoningprocessandthecomplexityofthetask.Theconstraintshavetodowiththefactthatonedoesnotwanttoachievethefunctionalityintheorybutratherinpractice.Infact,alargepartofexpertknowledgeisconcernedexactlywithefficientreasoninggiventheseconstraints:itisknowledgeabouthowtoachievethedesiredfunctionality.Therefore,specificationlanguagesforKBSsalsohavetospecifycontrolovertheuseoftheknowledgeduringthereasoningprocess.Alanguagemustthereforecombinenon-functionalandfunctionalspecificationtech-niques:ontheonehand,itmustbepossibletoexpressalgorithmiccontrolovertheexecutionofsubsteps.Ontheotherhand,itmustbepossibletocharacterizesubstepsonlyfunctionallywithoutmakingcommitmentstotheiralgorithmicrealization.ThelanguagesmentionedareanimportantstepinthedirectionofprovidingmeansforspecifyingthereasoningofKBSs.Still,thereisanumberofopenquestionsinthisarea.Themostimportantproblemisthespecificationofthedynamicbehaviourofareasoningsystem.Thespecificationofknowledgeaboutthedomainseemstobewellunderstood.Mostapproachesusesomevariantoffirst-orderlogictodescribethisknowledge.Proofsystemsexistwhichcanbeusedforverificationandvalidation.Thecentralquestionishowtoformulateknowledgeabouthowtousethisknowledgeinordertosolveatask(thedynamicsofthesystem).Itiswell-agreedthatthisknowledgeshouldbedescribedinadeclarativefashion(i.e.notbywritingaseparateprograminaconventionalprogramminglanguageforeverydifferenttask).Atthemoment,theafore-mentionedlanguagesuseanumberofformalismstodescribethedynamicsofaKBS:DESIREusesameta-logictospecifycontrolofinferencesoftheobjectlogic,(ML)2andMLPMapplydynamiclogic([15,16]),KARLintegratesideasoflogicprogrammingwithdynamiclogic,andTFLusesprocessalgebrainthestyleof[17].WiththeexceptionofTFL,thesemanticsoftheselanguagesarebasedonstatesandtransitionsbetweenthesestates.(ML)2,MLPMandKARLusedynamiclogicKripkestylemodels,andDESIREusestemporallogictorepresentareasoningprocessasalinearsequenceofstates.Onthewhole,however,thesesemanticsarenotworkedoutinprecisedetailformostapproaches,anditisunclearwhethertheseformalismsprovideaptdescriptionmethodsforthedynamicsofKBSs.Anothershortcomingofmostapproachesisthattheydonotprovideanexplicitproofsystemforsupporting(semi-)automaticproofsforverification.Theseshortcomingsmotivateourefforttoinvestigatespecificationformalismsfromrelatedresearchareastoseewhethertheycanprovideinsightinthespecificationof(inparticularthedynamicpartof)KBSs.Wehaveanalyzedrelatedworkininformationsystemdevelopment,databasesandsoftwareengineering.Approacheshavebeense-lectedthatenabletheusertospecifycontrolanddynamics.Theapproacheswehave