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BACKGROUND This research investigated the proposal that children with autism are impaired in processing information in its context. To date, this proposal rests almost exclusively on evidence from verbal tasks. Given evidence of visuo-spatial proficiency in autism in other areas of functioning, it is possible that the ability to use context is spared in the(More)
Many researchers have focussed their efforts in developing collaborative recommender systems. It has been proved that the use of collaboration in such systems improves its performance, but what is not known is how this collaboration is done and what is more important, how it has to be done in order to optimise the information exchange. The collaborative(More)
Trust is one of the most important social concepts that helps human agents to cope with their social environment and is present in all human interaction. Like in real world, agents should rely in some agents and mistrust in other ones to achieve a purpose. In this paper we develop a model of trust in the collaborative world as a new approach of recommender(More)
Recommender systems help users to identify particular items that best match their tastes or preferences. When we apply the agent theory to this domain, a standard centralized recommender system becomes a distributed world of recommender agents. Therefore, due to the agent's world, a new information filtering method appears: the opinion-based filtering(More)
We introduce a case-based system, BOLERO, that learns both plans and goal states. The major aim is that of improving the performance of a rule-based diagnosis system by adapting its behavior using the most recent information available about a patient. On the one hand BOLERO gets knowledge from cases in the form of diagnostic plans that are represented as(More)
The ability to express and recognise emotions is a fundamental element of human social interaction. With regard to web services, user requirements can be forgotten and forsaken when the user's emotional needs are satisfied. The question, then, is how to improve recommender systems, and make them more pleasant to the user through the perception of his/her(More)
This paper describes our approach to the next generation of open, distributed and heterogeneous recommender systems using Smart User Models (SUM). Our work focuses on integrating multiple agent-based services based on a unique representation of the user in what is called a Multi-agent Smart User Model. Intelligent agents are used in order to obtain a single(More)
When selfish industries are competing for limited shared resources, they need to coordinate their activities to handle possible conflicting situations. Moreover, this coordination should not affect the activities already planned by the industries, since this could have negative effects on their performance. Although agents may have buffers that allow them(More)
Multi-attribute resource allocation problems involves the allocation of resources on the basis of several attributes, therefore, the definition of a fairness method for this kind of auctions should be formulated from a multi-dimensional perspective. Under such point of view, fairness should take into account all the attributes involved in the allocation(More)