Qualitative Spatial Representation and Reasoning with the Region Connection Calculus

This paper surveys the work of the qualitative spatial reasoning group at the University of Leeds. The group has developed a number of logical calculi for representing and reasoning with qualitative spatial relations over regions. We motivate the use of regions as the primary spatial entity and show how a rich language can be built up from surprisingly few primitives. This language can distinguish between convex and a variety of concave shapes and there is also an extension which handles regions with uncertain boundaries. We also present a variety of reasoning techniques, both for static and dynamic situations. A number of possible application areas are briefly mentioned.     

Semantical investigations into nonmonotonic and probabilistic logics

Different forms of semantics have been proposed for conditionals of the form “Usually, if A then B”, ranging from quantitative probability distributions to qualitative approaches using plausibility orderings, possibility distributions, or conditional objects. Atomic-bound systems, also called big-stepped probabilities, allow qualitative reasoning with probabilities, aiming at bridging the gap between qualitative and quantitative argumentation and providing a model for the nonmonotonic reasoning system P. By using Goguen and Burstall’s notion of institutions for the formalization of logical systems, we elaborate precisely which formal connections exist among big-stepped probabilities, standard probabilities, and qualitative logics. Based on our investigations, we also develop two variants of conditional objects, one of them having a simpler semantics while still providing a model for system P.     

Computer-supported argumentation: A review of the state of the art

Argumentation is an important skill to learn. It is valuable not only in many professional contexts, such as the law, science, politics, and business, but also in everyday life. However, not many people are good arguers. In response to this, researchers and practitioners over the past 15–20 years have developed software tools both to support and teach argumentation. Some of these tools are used in individual fashion, to present students with the “rules” of argumentation in a particular domain and give them an opportunity to practice, while other tools are used in collaborative fashion, to facilitate communication and argumentation between multiple, and perhaps distant, participants. In this paper, we review the extensive literature on argumentation systems, both individual and collaborative, and both supportive and educational, with an eye toward particular aspects of the past work. More specifically, we review the types of argument representations that have been used, the various types of interaction design and ontologies that have been employed, and the system architecture issues that have been addressed. In addition, we discuss intelligent and automated features that have been imbued in past systems, such as automatically analyzing the quality of arguments and providing intelligent feedback to support and/or tutor argumentation. We also discuss a variety of empirical studies that have been done with argumentation systems, including, among other aspects, studies that have evaluated the effect of argument diagrams (e.g., textual versus graphical), different representations, and adaptive feedback on learning argumentation. Finally, we conclude by summarizing the “lessons learned” from this large and impressive body of work, particularly focusing on lessons for the CSCL research community and its ongoing efforts to develop computer-mediated collaborative argumentation systems.     

基于论据系统的带权定性概率推理机

定性概率推理是不确定性推理领域的一种重要方法。将定性概率推理的论据系统方法和抽象系统方法二者合而为一,在定性概率推理机(QPR)的基础上提出基于论据系统的带权定性概率推理机(WQPR)。首先扩展了带权定性概率网的定义,讨论了带权定性影响的对称性;其次将带权定性概率推理融入到论据系统中,提出WQPR推理系统,相比QPR能够在更精确的尺度进行不确定性推理,并证明了系统的正确性与完备性。     

Argument-based mixed recommenders and their application to movie suggestion

Recommender systems have become prevalent in recent years as they help users to access relevant items from the vast universe of possibilities available these days. Most existing research in this area is based purely on quantitative aspects such as indices of popularity or measures of similarity between items or users. This work introduces a novel perspective on movie recommendation that combines a basic quantitative method with a qualitative approach, resulting in a family of mixed character recommender systems. The proposed framework incorporates the use of arguments in favor or against recommendations to determine if a suggestion should be presented or not to a user. In order to accomplish this, Defeasible Logic Programming (DeLP) is adopted as the underlying formalism to model facts and rules about the recommendation domain and to compute the argumentation process. This approach has a number features that could be proven useful in recommendation settings. In particular, recommendations can account for several different aspects (e.g., the cast, the genre or the rating of a movie), considering them all together through a dialectical analysis. Moreover, the approach can stem for both content-based or collaborative filtering techniques, or mix them in any arbitrary way. Most importantly, explanations supporting each recommendation can be provided in a way that can be easily understood by the user, by means of the computed arguments. In this work the proposed approach is evaluated obtaining very positive results. This suggests a great opportunity to exploit the benefits of transparent explanations and justifications in recommendations, sometimes unrealized by quantitative methods.     

Modelling discourse in contested domains: A semiotic and cognitive framework

This paper examines the representational requirements for interactive, collaborative systems intended to support sensemaking and argumentation over contested issues. We argue that a perspective supported by semiotic and cognitively oriented discourse analyses offers both theoretical insights and motivates representational requirements for the semantics of tools for contesting meaning. We introduce our semiotic approach, highlighting its implications for discourse representation, before describing a research system (ClaiMaker) designed to support the construction of scholarly argumentation by allowing analysts to publish and contest ‘claims’ about scientific contributions. We show how ClaiMaker's representational scheme is grounded in specific assumptions concerning the nature of explicit modelling, and the evolution of meaning within a discourse community. These characteristics allow the system to represent scholarly discourse as a dynamic process, in the form of continuously evolving structures. A cognitively oriented discourse analysis then shows how the use of a small set of cognitive relational primitives in the underlying ontology opens possibilities for offering users advanced forms of computational service for analysing collectively constructed argumentation networks.     

Studying the effectiveness of an online argumentation model for improving undergraduate students' argumentation ability

This research develops a web-based model, entitled the “intuitive claim, peer-assessment, discussion, and elaborate claim argumentation training” (IPadE) model, and embeds with a Web-based Interactive Argumentation System to enhance undergraduate students' socioscientific argumentation abilities. This research adopts a quasi-experimental research design; the sample comprised 131 undergraduate students from two classes (69 in the experimental group and 62 in the control group). The socioscientific issue discussed were related to global health. This study collected and analysed quantitative and qualitative data, including the pretest and posttest of students' knowledge test scores and argumentation abilities questionnaire. The results generally confirmed the effectiveness of the IPadE model. First, in a comparison of the content knowledge and argumentation skills, the experimental group have statistically significantly improved than the control group. Second, regarding the number of reasoning modes proposed, the experimental group could propose multiple reasoning modes and reasoning levels on rebuttals increased after training.     

基于模糊层次分析的木马攻击效果评估技术研究

现有的网络攻击效果评估系统主要针对DDoS和蠕虫进行分析,缺乏对木马病毒的针对性。少数针对木马的危害度测量系统的指标值由人工标注,且都为定性值,稍欠客观性和全面性。基于该现状,从木马特性分析出发,建立针对木马病毒的层次化指标体系,设计指标值可定性也可定量,可人工标注也可系统采集。提出基于模糊层次分析的评估模型对木马攻击效果进行评估。同时,提出一种多对一的权重综合技术,对多组权重进行了合理的综合。最后,通过对比已有评估系统的评估结果,验证了所提评估技术的合理性。     

Suspension Terms as a Means for Meta-coordination in the μLog Coordination Framework

In this paper we introduce the concept of suspension terms as a reification of the suspension of coordination operations in the Log framework. Each time a coordination operation suspends, a corresponding suspension term is created. By creating primitives that can manipulate the suspension terms, we are able to introduce coordination primitives at the meta-level (e.g., dealing with success, failure, suspension). This is called meta-coordination. In this paper, we introduce the new primitives, give a formal definition of their basic semantics, and argue their expressiveness. The paper is concluded with an argumentation why first order suspension terms suffice and how higher order suspension terms can be replaced by lower order suspension terms.     

Collective intelligence as mechanism of medical diagnosis: The iPixel approach

Collective intelligence (CI) is an active field of research, which capitalizes the knowledge of human collectives in order to create, to innovate and to invent. There are two important mechanisms to implement CI: recommender and reputation systems. Recommender systems are used to provide filtered information from a large amount of elements. The recommendations are intended to provide interesting elements to users. Recommendation systems can be developed using different techniques and algorithms where the selection of these techniques depends on the area in which they will be applied. This work presents iPixel Recommender Engine, which is focused on the medical field. iPixel Recommendation Engine supports the process of differential diagnosis by recommending mammographic evaluations. Each mammogram is collectively tagged by the users’ community with a semantic sense; this feature allows iPixel acquires collective knowledge. iPixel can associate more than one feature with each mammogram. This work also presents a qualitative evaluation, where the basic features that a recommendation system should have in the medical field were obtained. Finally, a comparison was carried out with other similar recommender systems in order to know the Pixel advantages.     

Towards true linguistic modelling through optimal numerical solutions

This paper is concerned with both the problems of quantitative and qualitative modelling of complex systems by using fuzzy techniques. A unified approach for the identification and subsequent extraction of linguistic knowledge of systems using fuzzy relational models is addressed. This approach deals with the identification problem by means of optimal numerical solutions based on weighted least squares and quadratic programming formulations. The linguistic knowledge is extracted in the form of consistent fuzzy rules that describe linguistically the behaviour of the identified system. A new methodology for the simplification of the extracted rules is derived by using a pruning criterion based on the representability matrix concept introduced in previous work. Several numerical aspects concerning the proposed optimization schemes and a covering discussion about the linguistic interpretation of the resulting models are also included together with illustrative examples in the contexts of pattern classification and dynamic systems identification. The paper also provides an overview of fuzzy modelling techniques that intends to situate the relational models among other fuzzy model architectures typically adopted in the literature, highlighting their main advantages and drawbacks.     

Organization self-design of distributed production systems

The authors introduce two reorganization primitives, composition and decomposition, which change the population of agents and the distribution of knowledge in an organization. To create these primitives, they formalize organizational knowledge, which represents knowledge of potential and necessary interactions among agents in an organization. The authors develop computational organizational self-design (OSD) techniques for agents with architectures based on production systems to take advantage of the well-understood body of theory and practice. They first extend parallel production systems, where global control exists, into distributed production systems, where problems are solved by a society of agents using distributed control. Then they introduce OSD into distributed production systems to provide adaptive work allocation. Simulation results demonstrate the effectiveness of the approach in adapting to changing environmental demands. The approach affects production system design and improves the ability of build production systems that can adapt to changing real-time constraints     

Vartalaap: A distributed multicast communication system

With the increasing proliferation of computer networks and distributed systems, there is a growing number of applications using multicast communication. This paper presents the Vartalaap system developed at IIT, Bombay. Vartalaap is an hierarchical distributed system for multicast communication over a network, implemented in a hardware-independent fashion. The multicast is achieved without resorting to unnecessary broadcasting of messages over the network. Issues covered in this paper include the primitives for multicast, the multicast model and the system architecture. We discuss the implementation of Vartalaap and compare it with some other systems. We conclude with a discussion on the limitations of the current implementation and directions for future work.     

Parallel programming for computer vision

Two Unix environments developed for programming parallel computers to handle image-processing and vision applications are described. Visx is a portable environment for the development of vision applications that has been used for many years on serial computers in research. Visx was adapted to run on a multiprocessor with modest parallelism by using functional decomposition and standard operating-system capabilities to exploit the parallel hardware. Paragon is a high-level environment for multiprocessor systems that has facilities for both functional decomposition and data partitioning. It provides primitives that will work efficiently on several parallel-processing systems. Paragon's primitives can be used to build special image-processing operations, allowing one's own programming environment to be grown naturally     

A labelling framework for probabilistic argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature.     

Automated Visual Presentation: From Heterogeneous Information to Coherent Visual Discourse

Automated visual presentation systems should be able to design effective presentations for heterogeneous (quantitative and qualitative) information. They should also be able to work in static or interactive environments and capable of employing a wide range of visual media and visual techniques. In this paper, we focus on three tasks in building visual production systems: establishing a thorough understanding of the presentation-related characteristics of domain-specific information; classifying several types of visual information and capturing their distinct syntactic, semantic, and pragmatic features; and formulating a set of design principles.We define a data-analysis taxonomy to characterize heterogeneous information. In addition, we have modeled presentation context information such as audience identity to produce user-centered visual design. To utilize and manipulate visual information, we have classified it into visual objects and visual tools based on its role in the visual production process. To guide the visual design process, we have formulated a set of design principles that ensure the expressiveness and effectiveness of a design. To test and evaluate our work, we have developed a prototype system called IMPROVISE based on the research results. We use examples generated by IMPROVISE to illustrate how it constructs visual presentations.     

Verification and validation of an intelligent tutorial system

This paper presents the results of a verification and validation process for an intelligent system. The system being studied is an Intelligent Tutorial that employs fuzzy logic and multiagent systems. Software engineering techniques were used in the verification process, while the validation exploited both qualitative and quantitative techniques.