An Intelligent Multi-Agent Recommender System for Human Capacity Building

This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative filtering recommendation is implemented using intelligent agents. The agents work together in recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved using an agent that retrieves information autonomously using data mining techniques from websites. This manner of recommendation is scalable and adaptable. Further improvements can be made using clustering and recording user feedback.Comment: Proceedings of the 14th IEEE Mediterranean Electrotechnical Conference, 2008, pages 909 to 91

AntRS: Recommending Lists through a Multi-Objective Ant Colony System

International audienceWhen people use recommender systems, they generally expect coherent lists of items. Depending on the application domain, it can be a playlist of songs they are likely to enjoy in their favorite online music service, a set of educational resources to acquire new competencies through an intelligent tutoring system, or a sequence of exhibits to discover from an adaptive mobile museum guide. To make these lists coherent from the users' perspective, recommendations must find the best compromise between multiple objectives (best possible precision, need for diversity and novelty). We propose to achieve that goal through a multi-agent recommender system, called AntRS. We evaluated our approach with a music dataset with about 500 users and more than 13,000 sessions. The experiments show that we obtain good results as regards to precision, novelty and coverage in comparison with typical state-of-the-art single and multi-objective algorithms

Personalized Decentralized Communication

Search engines, portals and topic-centered web sites are all attempts to create more or less personalized web-services. However, no single service can in general fulfill all needs of a particular user, so users have to search and maintain personal profiles at several locations. We propose an architecture where each person has his own information management environment where all personalization is made locally. Information is exchanged with other’s if it’s of mutual interest that the information is published or received. We assume that users are self-interested, but that there is some overlap in their interests. Our recent work has focused on decentralized dissemination of information, specifically what we call decentralized recommender systems. We are investigating the behavior of such systems and have also done some preliminary work on the users’ information environment

Intelligent Agents - a Tool for Modeling Intermediation and Negotiation Processes

Many contemporary problems as encountered in society and economy require advanced capabilities for evaluation of situations and alternatives and decision making, most of the time requiring intervention of human agents, experts in negotiation and intermediation. Moreover, many problems require the application of standard procedures and activities to carry out typical socio-economic processes (for example by employing standard auctions for procurement or supply of goods or convenient intermediation to access resources and information). This paper focuses on enhancing knowledge about intermediation and negotiation processes in order to improve quality of services and optimize performances of business agents, using new computational methods that combine formal methods with intelligent agents paradigm. Taking into account their modularity and extensibility, agent systems allow facile, standardized and seamless integration of negotiation protocols and strategies by employing declarative and formal representations specific to computer science.Business processes, Intelligent Agents, Intermediation and Negotiation, Formal Models.

Design issues for agent-based resource locator systems

While knowledge is viewed by many as an asset, it is often difficult to locate particularitems within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work presented complements current research into both expertise finders and recommender systems. The essential issues for the proposed design are scalability, together ith the ability to learn and adapt to changing resources. As knowledge is often implicit within electronic resources, and therefore difficult to locate, we have proposed the use of ontologies, to extract the semantics and infer meaning to obtain the results required. We explore the use of communities of practice, applying ontology-based networks, and e-mail message exchanges to aid the resource discovery process