A semantic web architecture for advocate agents to determine preferences and facilitate decision making

ABSTRACT

The world-wide-web (WWW) today consists of distinct, isolated islands of data and metadata. In the near future we expect the availability of a critical mass of data and metadata for use by intelligent agents that act on behalf of human users. These agents would identify, propose and capture new opportunities to assist human users in satisfying their goals, by traversing and acting on this semantically rich and abundant information. We envision a new class of agents, their networks and their communities that exist for the sole purpose of serving as their human "master's" Advocates - Advocate Agents. Advocate Agents learn a human's goals and preferences, collaborate with other agents, mine semantic content, identify new opportunities for action, propose them and finally transact them, while always keeping the human "in-the-loop." This paper discusses this class of distributed, intelligent, Advocate Agents, their potential uses, and proposed architectures and techniques that provide a conceptual framework for these networked agent societies to collaborate in the achievement of their human user's goals.

References

1. Simon, H. A., Rational Decision Making in Business Organizations. The American Economic Review, 1979. <b>69</b>(4): p. 493--513.
2. Gigerenzer, G. and P. M. Todd, Simple Heuristics That Make Us Smart. 1999: Oxford University Press.
3. Maes, P., Agents that reduce work and information overload. Communications of the ACM, 1994b. <b>37</b>(7): p. 30--40.
4. Kumar, R., et al., Recommendation Systems: A Probabilistic Analysis. JCSS, 2001. <b>63</b>(1): p. 42--61.
5. Schafer, J. B., J. A. Konstan, and J. Riedl, E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, 2001. <b>5</b>(1): p. 115--153.
6. Varian, H. R. and P. Resnick, CACM Special Issue on Recommender Systems. Communications of the ACM, 1997. <b>40.</b>
7. Adomavicius, G. and A. Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. 2005.
8. Xiao, B. and I. Benbasat, Consumer Decision Support Systems for E-Commerce: Design and Adoption of Product Recommendation Agents. MIS Quarterly, 2007. <b>31</b>(1): p. 317--209.
9. Adomavicius, G. and A. Tuzhilin, An Architecture of e-Butler: A Consumer-centric Online Personalization System. International Journal of Computational Intelligence and Applications, 2002. <b>2</b>(3): p. 1--15.
10. Wellman, M. P., E. H. Durfee, and W. P. Birmigham, The digital library as a community of information agents. Expert, IEEE {see also IEEE Intelligent Systems and Their Applications}, 1996. <b>11</b>(3).
11. Shneiderman, B., Direct manipulation: A step beyond programming languages. 1981.
12. Maes, P., Social interface agents: Acquiring competence by learning from users and other agents. Software Agents---Papers from the 1994 Spring Symposium, Technical Report SS-94-03, Etzioni, O., Ed, 1994a: p. 71--78.
13. Rhodes, B. J. and P. Maes, Just-in-time information retrieval agents. IBM Systems Journal, 2000. <b>39</b>(3&4): p. 685-.
14. Berners-Lee, T., J. Hendler, and O. Lassila, The Semantic Web. Scientific American Magazine 2001.
15. Haeubl, G. and V. Trifts, Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science, 2000. <b>19</b>(1): p. 4--21.
16. Jeffrey, D. and G. Sanjay, MapReduce: simplified data processing on large clusters. Commun. ACM, 2008. <b>51</b>(1): p. 107--113.
17. Witten, I. H. and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. 2000: Morgan Kaufmann.
18. Segaran, T., Programming Collective Intelligence: Building Smart Web 2.0 Applications. 2007: O'Reilly Media, Inc.
19. Anderson, C., The Long Tail: Why the Future of Business is Selling Less of More. 2006: Hyperion.
20. Richardson, L., S. Ruby, and D. H. Hansson, RESTful Web Services. 2007: O'Reilly Media, Inc.
21. Bellifemine, F., A. Poggi, and G. Rimassa, Developing Multi-agent Systems with JADE. Intelligent Agents VII: Agent Theories Architectures and Languages: 7th International Workshop, ATAL 2000, Boston, MA, USA, July 7--9, 2000: Proceedings, 2001.
22. Chappell, D., Enterprise Service Bus. 2004: O'Reilly Media, Inc.
23. Varun, G. and T. C. T. James, E-commerce and the information market. Commun. ACM, 2001. <b>44</b>(4): p. 79--86.
24. Hagel, J. and M. Singer, Net worth. 1999: Harvard Business School Press Boston.
25. Billsus, D. and M. J. Pazzani, Learning collaborative information filters. Proceedings of the Fifteenth International Conference on Machine Learning. 1998. 46--54.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Distributed artificial intelligence

      • Intelligent agents

      • Multi-agent systems

• Social and professional topics

  • Computing / technology policy

• Software and its engineering

  • Software organization and properties

    • Software system structures

      • Software architectures