Defeasible reasoning: A discussion of some intuitions

In this article, we discuss some issues related to the intuitions of defeasible reasoning, in particular floating conclusions, reinstatement, and zombie paths. Defeasible logic serves as the formal basis for our analysis. We also make some comments on the comparison between defeasible logics and the well‐founded semantics of extended logic programs with priorities. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 545–558, 2006.     

DR-Prolog: A System for Defeasible Reasoning with Rules and Ontologies on the Semantic Web

Nonmonotonic rule systems are expected to play an important role in the layered development of the semantic Web. Defeasible reasoning is a direction in nonmonotonic reasoning that is based on the use of rules that may be defeated by other rules. It is a simple, but often more efficient approach than other nonmonotonic rule systems for reasoning with incomplete and inconsistent information. This paper reports on the implementation of a system for defeasible reasoning on the Web. The system 1) is syntactically compatible with RuleML, 2) features strict and defeasible rules, priorities, and two kinds of negation, 3) is based on a translation to logic programming with declarative semantics, 4) is flexible and adaptable to different intuitions within defeasible reasoning, and 5) can reason with rules, RDF, RDF Schema, and (parts of) OWL ontologies     

A conditional logic for defeasible beliefs

This paper provides a framework for representing beliefs by distinguishing between (i) the defeasible principles of a belief system, (ii) the propositions that are beyond reasonable doubt in a belief state, and (iii) the propositions ‘favored’ on the basis of defeasible principles and those propositions that are beyond reasonable doubt. Defeasible principles are interpreted semantically by means of a Lewis-style ranking of worlds (without the assumption that the actual world is among the ‘innermost’, or most highly ranked, worlds). The ‘favored closure’ (F-closure) of a set of defeasible principles and reasonable propositions is non-monotonic. Yet, given the concept of ‘pruning’ the default ranking relative to a set of worlds (determined by what is beyond reasonable doubt in a particular belief state) we provide a formal characterization of the conditions under which in inference to a favored conclusion on the basis of defeasible rules and reasonable propositions, is warranted. The adequacy of our representation of defeasible principles can be tested by considering a number of valid formulas that we list. We show that our concept of defeasible principle parallels but is not identical to the concept of ‘relatively high conditional probability’. An example of application of the formal language and semantics is given, and the final parts of the paper contain u     

Fuzzy Web ad selector based on Web usage mining

Internet and Web technologies are widely available, making it easier for companies to conduct business and transfer information to customers. Moreover, they speed up financial transactions efficiently, reducing the transaction costs of commercial activities that businesses would normally incur. So, Internet business has created a competitive environment, a successful company wanting to survive and gain a competitive advantage must provide an acceptable bundle of customized services that satisfy customers' needs. Despite the Internet's obvious benefits as a new communication medium its advertising gives the same advertising messages to all customers and so has suffered from poor responses. To raise a Web ad's effectiveness, we propose a Web ad selector that personalizes advertising messages for customers based on their preferences and interests. The Web ad selection system divides Web site customers with similar preferences into several segments through Web usage mining. It uses fuzzy rules that express customer segments' surfing patterns on the basis of expert advice, and recommends appropriate ads by fuzzy inference.     

A fuzzy preference‐ranking model for a quality evaluation of hospital web sites

This article presents a quality evaluation model for measuring the performance of hospital Web sites. The model is developed on the basis of a conceptual framework, which consists of seven major e‐service quality dimensions, including tangibles, reliability, responsiveness, confidence, empathy, quality of information, and integration of communication issues of Web sites. The dimensions and their associated attributes are first obtained from published articles in the health care and information technology literature and then adapted according to the suggestions of related domain experts. Two multicriteria decision‐making methods are used in the evaluation procedure. Determined Web site evaluation dimensions and their relevant attributes are weighted using the Analytic Hierarchy Process (AHP) method. Vagueness in some stages of the evaluation required the incorporation of fuzzy numbers in the assessment process. Both fuzzy and crisp data are then synthesized using the fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) ranking method. The model is applied initially to measure the performance of the Web sites of Turkish hospitals. This study should be of interest to health care and technology practitioners and researchers, as the findings shed light on the further development of performance measurements for hospital Web sites. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1181–1197, 2006.     

A fuzzy framework for efficient user-centric Web service selection

With the development of Web technologies and the increasing usage of Internet, more and more Web Services (WS) are deployed over Internet. Therefore, there will be a large number of candidate services for fulfilling a desired task. In the last decade, several WS selection approaches are proposed to cope with this challenge. In sharp contrast to the existing WS selection approaches that focus only on user-specified preferences, in this paper, we propose a flexible and effective WS selection framework, which gives users an adequate way to express their preferences using linguistic terms, and enhance the WS selection by leveraging their contexts and profiles. The satisfaction of the candidate WS is expressed by an objective score that takes into consideration no only the user-specified preferences, but also additional preferences extracted from both his/her context and profile using fuzzy inference rules, so as to improve the effectiveness of the selection. We then introduce an effective strategy that allows for priority between the two kinds of preferences, for ranking candidate services. Experimental evaluation on a real case study demonstrates the effectiveness of our proposed strategy.     

Reasoning about preferences in argumentation frameworks

The abstract nature of Dung's seminal theory of argumentation accounts for its widespread application as a general framework for various species of non-monotonic reasoning, and, more generally, reasoning in the presence of conflict. A Dung argumentation framework is instantiated by arguments and a binary conflict based attack relation, defined by some underlying logical theory. The justified arguments under different extensional semantics are then evaluated, and the claims of these arguments define the inferences of the underlying theory. To determine a unique set of justified arguments often requires a preference relation on arguments to determine the success of attacks between arguments. However, preference information is often itself defeasible, conflicting and so subject to argumentation. Hence, in this paper we extend Dung's theory to accommodate arguments that claim preferences between other arguments, thus incorporating meta-level argumentation based reasoning about preferences in the object level. We then define and study application of the full range of Dung's extensional semantics to the extended framework, and study special classes of the extended framework. The extended theory preserves the abstract nature of Dung's approach, thus aiming at a general framework for non-monotonic formalisms that accommodate defeasible reasoning about as well as with preference information. We illustrate by formalising argument based logic programming with defeasible priorities in the extended theory.     

Proof explanation for a nonmonotonic Semantic Web rules language

In this work, we present the design and implementation of a system for proof explanation in the Semantic Web, based on defeasible reasoning. Trust is a vital feature for Semantic Web. If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs. Our system produces automatically proof explanations using a popular logic programming system (XSB), by interpreting the output from the proof’s trace and converting it into a meaningful representation. It also supports an XML representation for agent communication, which is a common scenario in the Semantic Web. In this paper, we present the design and implementation of the system, a RuleML language extension for the representation of a proof explanation, and we give some examples of the system. The system in essence implements a proof layer for nonmonotonic rules on the Semantic Web.     

脏话文本语料库建设

脏话作为一种非正规的语言现象,在网络评价中已经无处不在,对网络文明造成了影响。描述了脏话文本的特点、定义及其危害,并对网络脏话文本进行了研究与分析,设计了一个机器自动判别与少量人工标注相结合的脏话语料采集方法,借助海量的真实评价文本,构造了一个较大规模的高质量的脏话语料库,初步采集了6 000多句脏话语料。然后利用一元、二元和三元特征,通过SVM与最大熵分类器对脏话的自动分类进行了实验,结果表明,两种分类器的准确率和查全率都达到97%以上。     

Aggregation operators based on indistinguishability operators

This article gives a new approach to aggregating assuming that there is an indistinguishability operator or similarity defined on the universe of discourse. The very simple idea is that when we want to aggregate two values a and b we are looking for a value λ that is as similar to a as to b or, in a more logical language, the degrees of equivalence of λ with a and b must coincide. Interesting aggregation operators on the unit interval are obtained from natural indistinguishability operators associated to t‐norms that are ordinal sums. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 857–873, 2006.     

A multi-agent recommender system for supporting device adaptivity in e-Commerce

Traditional recommender systems for e-Commerce support the customers’ activities providing them with useful suggestions about available products in Web stores. To this purpose, in an agent-based context, each customer is often associated with a customer agent that interacts with the site agent associated with the visited e-Commerce Web site. In presence of a high number of interactions between customers and Web sites, the generation of recommendations can be a heavy task for both these agents. Moreover, customers can navigate on the Web by using different devices having different characteristics that may influence customer’s preferences. In this paper we propose a new multi-agent system, called ARSEC, where each device exploited by a customer is associated with a device agent that autonomously monitors his/her behaviour. Furthermore, each customer is associated with a customer agent that collects in a global profile the information provided by his/her device agents and each e-Commerce Web site is associated with a seller agent. Based on the similarity existing among the global profiles the customers are partitioned in clusters, each one managed by a counsellor agent. Recommendations are generated in ARSEC as result of the collaboration between the seller agent and some counsellor agents associated with the customer. The usage of the device agents leads to generating recommendations taking into account the device currently used, while the fully decentralized architecture introduces a strong reduction of the time costs. Some experimental results are presented to show the significant advantages obtained by ARSEC in terms of recommendation effectiveness with respect to other well-known agent-based recommenders.     

Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems

This article presents an approach to integrate a Pareto dominance concept into a comprehensive learning particle swarm optimizer (CLPSO) to handle multiple objective optimization problems. The multiobjective comprehensive learning particle swarm optimizer (MOCLPSO) also integrates an external archive technique. Simulation results (obtained using the codes made available on the Web at http://www.ntu.edu.sg/home/EPNSugan) on six test problems show that the proposed MOCLPSO, for most problems, is able to find a much better spread of solutions and faster convergence to the true Pareto‐optimal front compared to two other multiobjective optimization evolutionary algorithms. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 209–226, 2006.     

Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Implicit ratings are more desirable for the mobile Web, but commonly used cardinal (interval, ratio) scales for representing preferences are also unsatisfactory because they may increase estimation errors. In this paper, we propose a CF-based recommendation methodology based on both implicit ratings and less ambitious ordinal scales. A mobile Web usage mining (mWUM) technique is suggested as an implicit rating approach, and a specific consensus model typically used in multi-criteria decision-making (MCDM) is employed to generate an ordinal scale-based customer profile. An experiment with the participation of real mobile Web customers shows that the proposed methodology provides better performance than existing CF algorithms in the mobile Web environment.     

Artificial argument assistants for defeasible argumentation

The present paper discusses experimental argument assistance tools. In contrast with automated reasoning tools, the objective is not to replace reasoning, but to guide the user's production of arguments. Two systems are presented, and based on . The focus is on defeasible argumentation with an eye on the law. Argument assistants for defeasible argumentation naturally correspond to a view of the application of law as dialectical theory construction. The experiments provide insights into the design of argument assistants, and show the pros and cons of different ways of representing argumentative data. The development of the argumentation theories underlying the systems has culminated in the logical system that formalizes the interpretation of prima facie justified assumptions. introduces an innovative use of conditionals expressing support and attack. This allows the expression of warrants for support and attack, making it a transparent and flexible system of defeasible argumentation.     

Integrating web service and semantic dialogue model for user models interoperability on the web

Nowadays there is a great number of Web information systems that build a model of the user and adapt their services according to the needs and preferences maintained by the user model (UM). One of the most challenging issues of this scenario is the possibility to enable different systems to cooperate in order to exchange the available information about a user. Our aim is to create rich (and scalable) communication protocols and infrastructures to enable consumers and providers of UM data to interact. Our solution for dealing with such an issue is to exploit Web standards for interoperability (i.e. Semantic Web and Web Services) for implementing simple atomic communication, and a dialogue model for implementing enhanced communication capabilities. In particular, two systems can start a semantics-enhanced Dialogue Game as a form of negotiation to clarify the meaning of the requested concepts when a shared knowledge model does not exist, and to approximate the response when the exact one is not available. We propose a distributed semantic conversation framework based on the Sesame semantic environment for the exchange of user model knowledge on the Web. Systems have to expose their user model data as a Web Service, and to exploit a public dialogue knowledge base to start the dialogue. The main advantage of the approach is to allow systems to deal with difficult situations by starting an appropriate dialogue game instead of stopping the communication as in the traditional “all-or-nothing” Web Service approach. On the basis of a preliminary evaluation, the approach has shown an improvement of the adaptation results provided by the systems we tested.     

A Feature-based Approach to Recommending Selections based on Past Preferences

The increasing availability of a large number of interactive multi-media information services means that users have a large and diverse collection of choices open to them. This diversity and choice may present navigation difficulties to users which can dissuade them from using such services. One method of assisting users to navigate through large collections is to use information filtering to extract only the information relevant to an end-user according to his/her long-term preferences. In this paper, we describe a mechanism to acquire a user's long-term preferences (user profile), and then show how the acquired profile may be used to recommend selections that may be of interest to the user. The profile is acquired on the basis of a user's habits using a Heuristic-Statistical approach, and is used to create selection indices which are then used during on-line interactions to recommend selections. Our mechanism has been incorporated into an experimental Video On Demand (VOD) service that is implemented using a client-server architecture. The profile acquisition component is incorporated into a VOD server on a multi-tasking machine, while the VOD user interface resides on a personal computer. Our mechanism for acquiring profiles and making recommendations has been quantitatively evaluated on the basis of data collected about movie preferences. This revised version was published online in July 2006 with corrections to the Cover Date.     

Modeling users through an expert system and a neural network

With the number of Internet and Web users increasing rapidly, electronic service providers are competing to satisfy and better serve customers looking for information or channels of advertisement. A wide variety of browses, specialized sites, custom made software, etc. are being offered on a regular basis. However, the user has to filter through a large number of files before finding what he/she is really looking for. This paper presents a user modeling expert system, SIGMA, based on neural networks for encapsulating Internet and Web users' habits and preferences. SIGMA is an artificial intelligence application designed to answer an Internet client needs and preferences. It analyses the user supplied demographic data and the monitored transactions then generate a tailored profile that is ultimately used to filter what information is being passed on to him/her in an effort to reduce and hopefully eliminate the time and energy expended in sifting through raw and often unwanted data.     

An intelligent Internet information delivery system to evaluate site preferences

Due to the continual growth of the popularity of the Internet, commercial as well as industrial companies have been advertising their products and services via the Web, resulting in a drastic increase in the number of Web sites. With a huge amount of information available on various Web sites, it is important that the relevant and useful information favored by individual visitors is delivered to the destinations in a timely manner. The two traditional approaches for sorting web information including search engines and hierarchical indices require specific input by the visitors who may not have any specific favorite sites in mind. In most cases, site surfers are just “window-shopping” on the Internet, looking for “exciting” things. This paper proposes the development of an Intelligent Internet Information Delivery System (IIIDS) which is characterized by its machine learning capability based on the data of site spots “movements” by the users within the Web pages and then evaluates the site preferences of the relevant users by means of fuzzy logic principle. The development of IIIDS and the test of a prototype to evaluate its feasibility are covered in this paper.     

American Sign Language on the Web

ABSTRACT

American Sign Language (ASL) is the third most common language in the United States, and an increasing number of Americans are becoming aware of this important language and the Deaf culture at its core. The growth of the Internet has provided a forum for a large number of World Wide Web sites that provide information on ASL, Deaf education, and Deaf culture. We have examined a large number of World Wide Web sites, and have selected and abstracted those sites most appropriate for researching ASL or Deaf culture.