Amalthaea--information filtering and discovery using a multiagent evolving system

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (p. 73-76).Alexandros G. Moukas.M.S

Engene: A genetic algorithm classifier for content-based recommender systems that does not require continuous user feedback

We present Engene, a genetic algorithm based classifier which is designed for use in content-based recommender systems. Once bootstrapped Engene does not need any human feedback. Although it is primarily used as an online classifier, in this paper we present its use as a one-class document batch classifier and compare its performance against that of a one-elms k-NN classifier

Learner course recommendation in e-learning based on swarm intelligence

Se dan unas recomendaciones en la enseñanza asistida por ordenador (e-learning) basada en la inteligencia colectiva.This paper analyses aspects about the recommendation process in distributedinformation systems. It extracts similarities and differences between recommendations in estores and the recommendations applied to an e-learning environment. It also explains the phenomena of self-organization and cooperative emergence in complex systems coupled with bio-inspired algorithms to improve knowledge discovery and association rules. Finally, the present recommendation is applied to e-learning by proposing recommendation by emergence in a multi.agent system architecture

Agents in decentralised information ecosystems: the DIET approach

The complexity of the current global information infrastructure requires novel means of understanding and exploiting the dynamics of information. One means may be through the concept of an information ecosystem. An information ecosystem is analo gous to a natural ecosystem in which there are flo ws of materials and energy analo gous to information flow between many interacting individuals. This paper describes a multi-agent platform, DIET (Decentralised Information Ecosystem Technologies) that can be used to implement open, robust, adaptive and scalable ecosystem-inspired systems. We describe the design principles of the DIET software architecture, and present a simple example application based upon it. We go on to consider how the DIET system can be used to develop information brokering agents, and how these can contribute to the implementation of economic interactions between agents, as well as identifying some open questions relating to research in these areas. In this way we show the capacity of the DIET system to support applications using information agents.Future and Emerging Technologies arm of the IST Programme of the European Union, under the FET Proactive Initiative – Universal Information Ecosystems (FET, 1999), through project DIET (IST -1999-10088), BTexaCT Intelligent Systems Laboratory for stimulating discussion and comment

PADMA: PArallel Data Mining Agents for scalable text classification

This paper introduces PADMA (PArallel Data Mining Agents), a parallel agent based system for scalable text classification. PADMA contains modules for (1) parallel data accessing operations, (2) parallel hierarchical clustering, and (3) web-based data visualization. This paper introduces the general architecture of PADMA and presents a detailed description of its different modules

Modelling Web Services in the agent-oriented modelling language and environment CAMLE

A particular difficulty in the development of Web Services applications is caused by the lack of communications between developers from different vendors. This paper investigates how modelling can help to solve the problem using the caste-centric agent-oriented modelling language and environment CAMLE, and illustrates the method by an example of online auction service. The use of CAMLE in model consistency check and specification generation is also discussed. Software engineers are enabled to specify not only the service provider's functionality and behaviour, but also the requirements and restrictions on service requesters' behaviours