Argument-based critics and recommenders: A qualitative perspective on user support systems

In recent years we have witnessed the wide-spread evolution of support tools that operate in association with the user to accomplish a range of computer-mediated tasks. Two examples of these tools are critics and recommenders. Critics are cooperative tools that observe the user interacting with a co...

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Detalles Bibliográficos
Autores: Chesñevar, Carlos Iván, Maguitman, Ana Gabriela, Simari, Guillermo Ricardo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2006
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/82249
Acceso en línea:http://hdl.handle.net/11336/82249
Access Level:acceso abierto
Palabra clave:Defeasible Argumentation
User Support Systems
Decision Support Systems
Recommender Systems
Critics
Practical Reasoning
https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
Descripción
Sumario:In recent years we have witnessed the wide-spread evolution of support tools that operate in association with the user to accomplish a range of computer-mediated tasks. Two examples of these tools are critics and recommenders. Critics are cooperative tools that observe the user interacting with a computer system and present reasoned opinions about a product under development. Recommender systems are tools that assist users by facilitating access to relevant items. At the same time, defeasible argumentation has evolved as a successful approach in AI to model commonsense qualitative reasoning, with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents a novel approach towards the integration of user support systems, such as critics and recommender systems, with a defeasible argumentation framework. The final goal is to enhance practical reasoning capabilities of current user support tools by incorporating argument-based qualitative inference.