Amalthaea: An Evolving Multi-Agent Information Filtering and Discovery System for the WWW

Amalthaea is an evolving, multi-agent ecosystem for personalized filtering, discovery, and monitoring of information sites. Amalthaea's primary application domain is the World Wide Web and its main purpose is to assist its users in finding interesting information. Two different categories of agents are introduced in the system: filtering agents that model and monitor the interests of the user and discovery agents that model the information sources.A market-like ecosystem where the agents evolve, compete, and collaborate is presented: agents that are useful to the user or other agents reproduce, while low-performing agents are destroyed. Results from various experiments with different system configurations and varying ratios of user interests versus agents in the system are presented. Finally issues like fine-tuning the initial parameters of the system and establishing and maintaining equilibria in the ecosystem are discussed.

[1]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[2]  Michael L. Best An ecology of text: Using text retrieval to study alife on the net , 1997 .

[3]  Filippo Menczer,et al.  Artificial Life Applied to Adaptive Information Agents , 1995 .

[4]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[5]  Paul E. Baclace Competitive agents for information filtering , 1992, CACM.

[6]  Adrian O'Riordan,et al.  An intelligent agent for high-precision text filtering , 1995, CIKM '95.

[7]  Timothy W. Finin,et al.  A semantics approach for KQML—a general purpose communication language for software agents , 1994, CIKM '94.

[8]  S. Clearwater Market-based control: a paradigm for distributed resource allocation , 1996 .

[9]  R. W. Mitchell,et al.  A Comparative-Developmental Approach to Understanding Imitation , 1987 .

[10]  Katia P. Sycara,et al.  Designing behaviors for information agents , 1997, AGENTS '97.

[11]  Katia P. Sycara,et al.  Distributed Intelligent Agents , 1996, IEEE Expert.

[12]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[13]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[14]  Thorsten Joachims,et al.  WebWatcher : A Learning Apprentice for the World Wide Web , 1995 .

[15]  Richard K. Belew,et al.  Evolution, Learning, and Culture: Computational Metaphors for Adaptive Algorithms , 1990, Complex Syst..

[16]  Pattie Maes,et al.  Evolving agents for personalized information filtering , 1993, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications.

[17]  Yoav Shoham,et al.  Learning Information Retrieval Agents: Experiments with Automated Web Browsing , 1995 .

[18]  Katia P. Sycara,et al.  Coordination of Multiple Intelligent Software Agents , 1996, Int. J. Cooperative Inf. Syst..

[19]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[20]  Oren Etzioni,et al.  Moving Up the Information Food Chain: Deploying Softbots on the World Wide Web , 1996, AI Mag..

[21]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[22]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[23]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[24]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[25]  Nicholas J. Belkin,et al.  Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.

[26]  Thomas Wagner,et al.  MACRON: An Architecture for Multi-agent Cooperative Information Gathering , 1995, CIKM 1995.

[27]  CroftW. Bruce,et al.  Information filtering and information retrieval , 1992 .

[28]  Alexandros Moukas Amalthaea Information Discovery and Filtering Using a Multiagent Evolving Ecosystem , 1997, Appl. Artif. Intell..