Open AccessProceedings Article
Validation and Experimentation of a Tourism Recommender Agent based on a Graded BDI Model
Ana Casali,Lluís Godo,Carles Sierra +2 more
- pp 41-50
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TLDR
A validation and an experimentation of the use of graded BDI agents using this agent model are reported, showing that the results obtained by these particular recommender agents using graded attitudes improve those achieved by agents using non-graded attitudes.Abstract:
In this paper, a validation and an experimentation of the use of graded BDI agents is reported. This agent model has been proposed to specify agents capable to deal with the environment uncertainty and with graded attitudes in an efficient way. As a case study we focus on a Tourism Recommender Agent specified using this agent model. The experimentation on the case study aims at proving that this agent model is useful to develop concrete agents showing different and rich behaviours. We also show that the results obtained by these particular recommender agents using graded attitudes improve those achieved by agents using non-graded attitudes.read more
Citations
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Journal ArticleDOI
A graded BDI agent model to represent and reason about preferences
TL;DR: This research note introduces a graded BDI agent development framework, g-BDI for short, that allows to build agents as multi-context systems that reason about three fundamental and graded mental attitudes.
Journal ArticleDOI
A Tourism Recommender Agent: from theory to practice
TL;DR: This paper focuses on the implementational aspects of the multiagent system and specially on the T-Agent development, going from the theoretical agent model to a concrete agent implementation.
Dissertation
On intentional and social agents with graded attitudes
TL;DR: The central contribution of this dissertation is the proposal of a graded BDI agent model (g-BDI), specifying an architecture capable of representing and reasoning with graded mental attitudes, and shows that this model is useful to develop agents showing varied and rich behaviours.
Book ChapterDOI
g-BDI: A Graded Intensional Agent Model for Practical Reasoning
TL;DR: This paper overviews recent developments about a multi-context based agent architecture g-BDI to represent and reasoning about gradual notions of desires and intentions, including sound and complete logical formalizations and shows that the framework is expressive enough to describe how desires can lead agents to intentions and finally to actions.
Book ChapterDOI
The Representation for All Model: An Agent-Based Collaborative Method for More Meaningful Citizen Participation in Urban Planning
TL;DR: The Model is designed to greatly increase public participation in urban planning and make it more citizen-friendly, and uses an agent technology consisting of a pair of opinion-miner recommender agents which make recommendations to planners on the design of the master plan.
References
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BDI Agents: From Theory to Practice
Anand S. Rao,Michael P. Georgeff +1 more
TL;DR: This paper explores a particular type of rational agent, a BeliefDesire-Intention (BDI) agent, and integrates the theoretical foundations of BDI agents from both a quantitative decision-theoretic perspective and a symbolic reasoning perspective.
Knowledge-based recommender systems
TL;DR: Recommendations made by recommender systems can help users navigate through large information spaces of product descriptions, news articles or other items, and are an increasingly important tool in the on-line information and e-commerce burgeon.
Journal ArticleDOI
A Taxonomy of Recommender Agents on theInternet
TL;DR: A state-of-the-art taxonomy of intelligent recommender agents on the Internet and a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.
Journal ArticleDOI
Local Models Semantics, or contextual reasoning=locality+compatibility☆
TL;DR: In this article, a new semantics, called Local Models Semantics, is proposed to provide a foundation for reasoning with contexts, which captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context; however, there is compatibility among the reasoning performed in different contexts.
Beyond Recommender Systems: Helping People Help Each Other
Loren Terveen,Will Hill +1 more
TL;DR: This work presents a framework for understanding recommender systems and surveys a number of distinct approaches in terms of this framework, and suggests two main research challenges: helping people form communities of interest while respecting personal privacy and developing algorithms that combine multiple types of information to compute recommendations.