Plans, goals and selection rules in the comprehension of computer programs

The notion of the programming plan has been proposed as a mechanism through which one can explain the nature of expertise in programming. Soloway and Ehrlich (1984) suggest that such expertise is characterized by the existence and use of programming plans. However, studies in other complex problem-solving domains, notably text editing, suggest that expertise is characterized not only by the possession of plan-related structures but also by the development of appropriate selection rules which govern the implementation of plans in appropriate situations (Card et al. 1980, Kay and Black 1984, 1986). This paper presents an experimental study which examines the role of programming plans in the context of skill development in programming. The results of this study suggest that plan-based structures cannot be used in isolation to explain novice/expert differences. Indeed, such structures appear to prevail at intermediate levels of skill. The major characteristic of expertise in programming would appear to be strongly related to the development of appropriate selection rules and to so-called program discourse rules. This in turn suggests that current views on the role of plan-based structures in expert programming performance are too limited in their conception to provide an adequate basis for a thorough analysis of the problem-solving activity in the programming domain.     

基于N-gram的哈萨克语文本校对系统的设计与实现

在啥萨克语文本非词查错方面,归纳和总结查错方法,在一定规模的哈萨克语词库的支持下,利用哈萨克语的特点,用哈萨克语词干切分程序和哈萨克语的音节规则,从文本中找出非词错误,再用最小编辑距离算法提供最有可能的候选词.在哈萨克语文本真词查错部分,根据上下文信息,采用基于N-gram的语言模型,利用文本的局部连接同现概率三元语法模型来进行真词查错,再用基于编辑距离的模式匹配方法对真词错误提供纠错建议.实验结果表明,系统的查错与纠错效率较好,实验方案是可行的.     

面向油田领域的中文真词错误自动校对方法研究

中文真词错误自动校对是自然语言理解的一项重要的基础研究课题,油田数字化过程中利用图像识别及人工录入产生的中文真词错误会直接影响后期数据综合分析准确度。对中文真词错误成因和统计语言模型进行分析,提出一种面向油田领域的中文真词错误自动校对方法。该方法首先构建通用领域和油田领域混淆集,再引入同义词集丰富知识库,对语料分词后,综合统计分析目标词与混淆词、周边词的同义词之间关系,自动校对真词错误。实验表明,提出的方法能有效校对油田领域的中文真词错误。     

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.     

Using dynamic user models in the recognition of the plans of the user

This paper is concerned with information-seeking dialogues in a restricted domain (we consider a consultation system for a Computer Science Department, delivering information about the various tasks that the users may want to perform: for example, how to access the library, get information about the courses of the Department, etc.) and presents a framework where a plan recognition and a user modeling component are integrated to cooperate in the task of identifying the user's plans and goals. The focus of the paper is centered on the techniques used for building the user model and exploiting it in the determination of the user's intentions. For this task, we use stereotypes and we propose some inference rules for expanding the user model by inferring the user's beliefs from both the sentences s/he utters and the information stored in the plan library of the system, that describes the actions in the domain. Moreover, we introduce some disambiguation rules that are applied to the information in the user model for restricting the set of ambiguous hypotheses on the user's plans and goals to the most plausible ones. This also simplifies a further clarification dialogue if it is necessary for a precise identification of the user's intentions.     

Functional Knowledge Representation Based on Problem Reduction

This paper proposes an approach for functional knowledge representation based on problem reuction,which represents the organization of problem-solving activities in two levels:reduction and reasoning.The former makes the functional plans for problem-solving while the latter constructs functional units, called handlers,for executing subproblems designated by these plans.This approach emphasizes that the representation of domain knowledge should be closely combined with(rather than separated from)its use,therefore provides a set of reasoning-level primitives to construct handlers and formulate the control strategies for executing them,As reduction-level primitives,handlers are used to construct handler-associative networks,which become the executable representation of problem-reduction graphs,in order to realize the problem-solving methods suited to domain features.Besides,handlers and their control slots can be used to focus the attention of knowledge acquisition and reasoning control.     

Bayesian Models for Keyhole Plan Recognition in an Adventure Game

We present an approach to keyhole plan recognition which uses a dynamic belief (Bayesian) network to represent features of the domain that are needed to identify users' plans and goals. The application domain is a Multi-User Dungeon adventure game with thousands of possible actions and locations. We propose several network structures which represent the relations in the domain to varying extents, and compare their predictive power for predicting a user's current goal, next action and next location. The conditional probability distributions for each network are learned during a training phase, which dynamically builds these probabilities from observations of user behaviour. This approach allows the use of incomplete, sparse and noisy data during both training and testing. We then apply simple abstraction and learning techniques in order to speed up the performance of the most promising dynamic belief networks without a significant change in the accuracy of goal predictions. Our experimental results in the application domain show a high degree of predictive accuracy. This indicates that dynamic belief networks in general show promise for predicting a variety of behaviours in domains which have similar features to those of our domain, while reduced models, obtained by means of learning and abstraction, show promise for efficient goal prediction in such domains.     

基于用户上下文的新闻服务机制研究

在新闻服务中引入用户上下文来实现个性化服务.通过定义用户上下文,利用基于事例的推理(CBR)方法,对基于用户新闻请求生成的问题事例进行推理,找到可用的事例,修改问题事例,经过新闻检索,实现个性化新闻服务,提高内容服务质量,优化检索结果,生成最大程度反映用户需求的新闻结果.     

基于统计翻译框架的蒙古文自动拼写校对方法

在以国际标准编码存储的传统蒙古文电子文本中,拼写错误十分普遍。人工校对这些错误不仅速度慢而且成本高。该文提出了一种基于统计翻译框架的传统蒙古文自动拼写校对方法,将拼写校对看作是从错误词到正确词的翻译。该文使用改进的基于短语的统计机器翻译模型来构建拼写校对模型,然后对测试文本进行校对。实验结果表明,该方法可以快速、有效地校对拼写错误,而且不依赖于特定语言的语法知识。使用该方法对包含1 026个正确词、1 102个错误词的测试集进行拼写校对,校对后文本中的正确词所占比例最高可达97.55%。     

Exploiting temporal and novel information from the user in plan recognition

This paper is concerned with the general topic of recognizing the plan of a user, to include a representation of the user's plans as part of a user model. We focus on extending the coverage of plan recognition, by allowing for additional detail in the user's plan beyond fixed specifications of possible plans in a system's library. We provide procedures for handling two distinct extensions: recognizing temporal constraints from the user and admitting novel information. We conclude by commenting on the importance of these extensions when including plans in a user model in order to enhance communication between the system and the user.     

Collaborative corrections with spelling control: Digital resources and peer assistance

The present study has explored how pairs of students deployed digital tools (spelling software) as resources in spontaneously occurring corrections of spelling errors. Drawing on the sociocultural theory of learning and ethnomethodological (Conversation Analytic) insights into social interaction, it has identified a range of consistent practices and uses of the spelling tools that were emergent in the everyday educational activities. As demonstrated, technology-assisted error corrections constituted a complex situation, where a number of socioculturally significant factors (goals of the task, properties of the software, and physical access to computer applications) shaped the trajectories of joint work. The present analysis shows in detail how the students approached the visually manifested language production errors by using two kinds of software resources, spelling lists, and a diagnostic tool. The inherent conceptual distinctions, characteristic of these tools, configured joint interpretative work and efforts to correct the errors in different ways. Recurrently, the students’ technology-based corrections were designed as autonomous, stepwise, locally improvised problem solutions, which were subsequently submitted for the evaluation of the diagnostic software. Overall, the study shows that the under-specification of the software’s instructions opened a space for the students’ creative engagement. The potentials of joint spelling software-assisted corrections for collaborative learning are discussed.

基于上下文的真词错误检查及校对方法

英文文本中的真词错误即输入的错词是和原词相似的另一个有效词。该文主要研究了对该类错误的检测。通过从所要检测的单词的上下文中提取句法和语义两个方面的特征,运用文档频率和信息增益进行特征筛选,实现了对上下文特征的有效提取。最终把判断该单词使用的正确与否看作分类问题,使用 Winnow分类算法进行训练和测试。通过5阶交叉验证,所收集的61组混淆集的平均正确率与召回率分别为96%,79.47%。     

Proficiency-Adapted Information Browsing and Filtering in Hypermedia Educational Systems

We present a framework for proficiency-adapted information browsing and filtering in educational hypermedia systems. In hyperdocuments, information is acquired by browsing through highly interconnected sets of information nodes. In order to find specific information, users follow links to nodes they judge to be relevant. In order to help users find relevant information and new learning material that match their levels of domain knowledge, we present a framework for adapting the information nodes, and the links leading to them, to the user's proficiency in the subject matter. Such a proficiency-adapted, user-centered educational environment is intended to enhance learning. We believe that learning in educational hypertext-based applications cannot be reduced to traversing a static information space. Navigating through any space, be it a physical or an information space, normally requires that users have a prior degree of proficiency in the domain knowledge. Learning is an evolving dynamic process through which users progress from a situation of unfamiliarity to one of mastery of a knowledge corpus. Therefore, we propose a model of proficiency-adapted learning and information browsing in which the presented choices (links and the textual context of links) are selected based on the user's knowledge state. Ultimately, such an adaptive course not only guides the learning process of the student, but it gradually transforms itself into a reference guide. This revised version was published online in July 2006 with corrections to the Cover Date.     

User-Tailored Planning of Mixed Initiative Information-Seeking Dialogues

Intelligent dialogue systems usually concentrate on user support at the level of the domain of discourse, following a plan-based approach. Whereas this is appropriate for collaborative planning tasks, the situation in interactive information retrieval systems is quite different: there is no inherent plan-goal hierarchy, and users are known to often opportunistically change their goals and strategies during and through interaction. We need to allow for mixed-initiative retrieval dialogues, where the system evaluates the user's individual dialogue behavior and performs situation-dependent interpretation of user goals, to determine when to take the initiative and to change the control of the dialogue, e.g., to propose (new) problem-solving strategies to the user. In this article, we present the dialogue planning component of a concept- oriented, logic-based retrieval system (MIRACLE). Users are guided through the global stages of the retrieval interaction but may depart, at any time, from this guidance and change the direction of the dialogue. When users submit ambiguous queries or enter unexpected dialogue control acts, abductive reasoning is used to generate interpretations of these user inputs in light of the dialogue history and other internal knowledge sources. Based on these interpretations, the system initiates a short dialogue offering the user suitable options and strategies for proceeding with the retrieval dialogue. Depending on the user's choice and constraints resulting from the history, the system adapts its strategy accordingly.     

Automatic correction of syntax-errors in programming languages

Summary A formal model for automatic error-correction is presented. It considers clusters of errors, using a local context to determine the corrections. The process can be embedded in left-to-right recognizers. The recognizer is used for standard syntax checking. When an error is detected, an error correction mechanism is activated which, by a limited backward then forward move, determines the possible corrections. An important aspect of this process is that it does not slow down the parsing of correct programs.Work done mainly at Cornell University, Department of Computer Science, Ithaca NY 14850. Partially supported by the National Science Foundation under grant GJ-28176.     

Generation and selection of likely interpretations during plan recognition in task-oriented consultation systems

This paper presents a mechanism which infers a user's plans from his/her utterances by directing the inference process towards the more likely interpretations of a speaker's statements among many possible interpretations. Our mechanism uses Bayesian theory of probability to assess the likelihood of an interpretation, and it complements this assessment by taking into consideration two aspects of an interpretation: its coherence and its information content. The coherence of an interpretation is determined by the relationships between the different statements in the discourse. The information content of an interpretation is a measure of how well defined the interpretation is in terms of the actions to be performed on the basis of this interpretation. This measure is used to guide the inference process towards interpretations with higher information content. The information content of an interpretation depends on the specificity and the certainty of the inferences in it, where the certainty of an inference depends on the knowledge on which the inference is based. Our mechanism has been developed for use in task-oriented consultation systems. The particular domain that we have chosen for exploration is that of travel booking.     

基于文本序列错误概率和中文拼写错误概率融合的汉语纠错算法

中文拼写纠错是一项检测和纠正文本中拼写错误的任务。大多数中文拼写错误是在语义、读音或字形上相似的字符被误用,因此常见的做法是对不同模态提取特征进行建模。但将不同特征直接融合或是利用固定权重进行求和,使得不同模态信息之间的重要性关系被忽略以及模型在识别错误时会出现偏差,阻止了模型以有效的方式学习。为此,提出了一种新的模型以改善这个问题,称为基于文本序列错误概率和中文拼写错误概率融合的汉语纠错算法。该方法使用文本序列错误概率作为动态权重、中文常见拼写错误概率作为固定权重,对语义、读音和字形信息进行了高效融合。模型能够合理控制不同模态信息流入混合模态表示,更加针对错误发生处进行学习。在SIGHAN基准上进行的实验表明,所提模型的各项评估分数在不同数据集上均有提升,这验证了该算法的可行性。     

Information Filtering Using User's Context on Browsing in Hypertext

Browsing is one of the most popular ways to gather information in database with hypertext structure. In order to support a user to browse, modeling of the user's interests is one of the most important issues. Although there are several promising methods to infer the interests from the user's browsing behavior, they assume that the interests are consistent during the browsing. However, the user's interests are often strongly dependent on the local context of the browsing. This paper describes a method to model the user's shifting interests from the browsing history. An information filtering method using the model of the interests has been implemented. We call it ‘context-sensitive filtering’. The results of an experimental evaluation, by real users' browsing for an encyclopedia in CD-ROM format, are also reported. This revised version was published online in July 2006 with corrections to the Cover Date.     

Techniques for Plan Recognition

Knowing a user's plans and goals can significantly improve the effectiveness of an interactive system. However, recognizing such goals and the user's intended plan for achieving them is not an easy task. Although much research has dealt with representing the knowledge necessary for plan inference and developing strategies that hypothesize the user's evolving plans, a number of serious problems still impede the use of plan recognition in large-scale, real-world applications. This paper describes the various approaches that have been taken to plan inference, along with techniques for dealing with ambiguity, robustness, and representation of requisite domain knowledge, and discusses areas for further research.