Towards an intelligent possibilistic web information retrieval using multiagent system.

PURPOSE - The purpose of this paper is to make a scientific contribution to web information retrieval (IR). Design/methodolog y/approach – A multiagent system for web IR is proposed based on new technologies: Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). This system is based on a possibilistic qualitative approach which extends the quantitative one. FINDINGS – The paper finds that the relevance of the order of documents changes while passing from a profile to another. Even if the selected terms tend to select the relevant document, these terms are not the most frequent of the document. This criterion shows the asset of the qualitative approach of the SARIPOD system in the selection of relevant documents. The insertion of the factors of preference between query terms in the calculations of the possibility and the necessity consists in increasing the scores of possibilistic relevance of the documents containing these terms with an aim of penalizing the scores of relevance of the documents not containing them. The penalization and the increase in the scores are proportional to the capacity of the terms to discriminate between the documents of the collection. RESEARCH LIMITATIONS/IMPLICATIONS – It is planned to extend the tests of the SARIPOD system to other grammatical categories, like refining the approach for the substantives by considering for example, the verbal occurrences in names definitions, etc. Also, it is planned to carry out finer measurements of the performances of SARIPOD system by extending the tests with other types of web documents. PRACTICAL IMPLICATIONS – The system can be useful to help research students find their relevant scientific papers. It must be located in the document server of any research laboratory. ORIGINALITY/VALUE – The paper presents SARIPOD, a new qualitative possibilistic model for web IR using multiagent syste

A Possibilistic Query Translation Approach for Cross-Language Information Retrieval

International audienceIn this paper, we explore several statistical methods to find solutions to the problem of query translation ambiguity. Indeed, we propose and compare a new possibilistic approach for query translation derived from a probabilistic one, by applying a classical probability-possibility transformation of probability distributions, which introduces a certain tolerance in the selection of word translations. Finally, the best words are selected based on a similarity measure. The experiments are performed on CLEF-2003 French-English CLIR collection, which allowed us to test the effectiveness of the possibilistic approach

Contributions to artificial intelligence: the IIIA perspective

La intel·ligència artificial (IA) és un camp científic i tecnològic relativament nou dedicat a l'estudi de la intel·ligència mitjançant l'ús d'ordinadors com a eines per produir comportament intel·ligent. Inicialment, l'objectiu era essencialment científic: assolir una millor comprensió de la intel·ligència humana. Aquest objectiu ha estat, i encara és, el dels investigadors en ciència cognitiva. Dissortadament, aquest fascinant però ambiciós objectiu és encara molt lluny de ser assolit i ni tan sols podem dir que ens hi haguem acostat significativament. Afortunadament, però, la IA també persegueix un objectiu més aplicat: construir sistemes que ens resultin útils encara que la intel·ligència artificial de què estiguin dotats no tingui res a veure amb la intel·ligència humana i, per tant, aquests sistemes no ens proporcionarien necessàriament informació útil sobre la naturalesa de la intel·ligència humana. Aquest objectiu, que s'emmarca més aviat dins de l'àmbit de l'enginyeria, és actualment el que predomina entre els investigadors en IA i ja ha donat resultats impresionants, tan teòrics com aplicats, en moltíssims dominis d'aplicació. A més, avui dia, els productes i les aplicacions al voltant de la IA representen un mercat anual de desenes de milers de milions de dòlars. Aquest article resumeix les principals contribucions a la IA fetes pels investigadors de l'Institut d'Investigació en Intel·ligència Artificial del Consell Superior d'Investigacions Científiques durant els darrers cinc anys.Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive scientists. Unfortunately, such an ambitious and fascinating goal is not only far from being achieved but has yet to be satisfactorily approached. Fortunately, however, artificial intelligence also has an engineering goal: building systems that are useful to people even if the intelligence of such systems has no relation whatsoever with human intelligence, and therefore being able to build them does not necessarily provide any insight into the nature of human intelligence. This engineering goal has become the predominant one among artificial intelligence researchers and has produced impressive results, ranging from knowledge-based systems to autonomous robots, that have been applied to many different domains. Furthermore, artificial intelligence products and services today represent an annual market of tens of billions of dollars worldwide. This article summarizes the main contributions to the field of artificial intelligence made at the IIIA-CSIC (Artificial Intelligence Research Institute of the Spanish Scientific Research Council) over the last five years

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by whichorganizations can better use information and, more importantly, manageknowledge. Unlike other strategies, knowledge management (KM) is difficult todefine because it encompasses a range of concepts, management tasks,technologies, and organizational practices, all of which come under the umbrella ofthe information management. Semantic approaches allow easier and more efficienttraining, maintenance, and support knowledge. Current ICT markets are dominatedby relational databases and document-centric information technologies, proceduralalgorithmic programming paradigms, and stack architecture. A key driver of globaleconomic expansion in the coming decade is the build-out of broadbandtelecommunications and the deployment of intelligent services bundling. This paperintroduces the main characteristics of an Intelligent Knowledge ManagementSystem as a multiagent system used in a Learning Control Problem (IKMSLCP),from a semantic perspective. We describe an intelligent KM framework, allowingthe observer (a human agent) to learn from experience. This framework makes thesystem dynamic (flexible and adaptable) so it evolves, guaranteeing high levels ofstability when performing his domain problem P. To capture by the agent who learnthe control knowledge for solving a task-allocation problem, the control expertsystem uses at any time, an internal fuzzy knowledge model of the (business)process based on the last knowledge model

SOME CONCEPTUAL PROPERTIES FOR KNOWLEDGE MANAGEMENT SYSTEMS DESIGN

Knowledge Management Systems (KMS) are important tools by which organizations can better useinformation and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) isdifficult to define because it encompasses a range of concepts, management tasks, technologies, and organizationalpractices, all of which come under the umbrella of the information management. Semantic approaches alloweasier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated byrelational databases and document-centric information technologies, procedural algorithmic programmingparadigms, and stack architecture. A key driver of global economic growth in the coming decade is the build-out ofbroadband telecommunications and the deployment of intelligent services bundling. This paper introduces themain characteristics of an Intelligent Knowledge Management System as a multi-agent system used in a LearningControl Problem (IKMSLCP). We describe an intelligent KM framework, allowing the observer (a human agent)to learn from experience

Combining Coordination and Organisation Mechanisms for the Development of a Dynamic Context-aware Information System Personalised by means of Logic-based Preference Methods

The general objective of this thesis is to enhance current ICDs by developing a personalised information system stable over dynamic and open environments, by adapting the behaviour to different situations, and handle user preferences in order to effectively provide the content (by means of a composition of several information services) the user is waiting for. Thus, the system combines two different usage contexts: the adaptive behaviour, in which the system adapts to unexpected events (e.g., the sudden failure of a service selected as information source), and the information customisation, in which the system proactively personalises a list of suggestions by considering user’s context and preferences