Abstract
Due to advances in technology, diverse and voluminous information is becoming available to decision makers. This presents the potential for improved decision support, but poses challenges in terms of building tools to support users in accessing, filtering, evaluating, and fusing information from heterogeneous information sources. Most reported research on intelligent information agents to date has dealt with a user interacting with a single agent that has general knowledge and is capable of performing a variety of user delegated information finding tasks (e.g., Etzioni and Weld, 1994). For each information query, the agent is responsible for accessing different information sources and integrating the results. We believe that, given the current computational state of the art, a centralized agent approach has many limitations: (1) a single general agent would need an enormous amount of knowledge to be able to deal effectively with user information requests that cover a variety of tasks, (2) a centralized information agent constitutes a processing bottleneck and a ‘single point of failure,’ (3) unless the agent has beyond the state of the art learning capabilities, it would need considerable reprogramming to deal with the appearance of new agents and information sources in the environment, (4) because of the complexity of the information fmding and filtering task, and the large amount of information, the required processing would overwhelm a single agent. For these reasons and because of the characteristics of the Internet environment, we employ a distributed collaborative collection of agents for information gathering.
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References
Decker, K. S. (1995) Environment Centered Analysis and Design of Co-ordination Mechanisms. PhD thesis, University of Massachusetts.
Decker, K. S., Lesser, V. R. (1993) Quantitative modeling of complex computational task environments. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, 217–224, Washington, July.
Decker, K. S., Lesser, V. R. (1995) Designing a family of coordination algorithms. In: Proceedings of the First International Conference on Multi-Agent Systems, 73–80, San Francisco, June, AAAI Press. Longer version available as UMass CS-TR 94–14.
Etzioni, O., Weld, D. (1994) A softbot-based interface to the Internet. Communications of the ACM, 37(7), July.
Garvey, A., Decker, K. S., Lesser, V. R. (1994) A negotiation-based interface between a real-time scheduler and a decision-maker. In: AAAI Workshop on Models of Conflict Management, Seattle. Also UMASS CS TR-94–08.
Garvey, A., Humphrey, M., Lesser, V. R. (1993) Task interdependencies in design-to-time real-time scheduling. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, 580–585, Washington, July.
Joh, G., Lee, C. (1992) Stock price response to accounting information in oligopoly. Journal of Business,65(3), 451–472, July.
Kambhampati, S., Hendler, J. A. (1992) A validation-structure-based theory of plan modification and reuse. Artificial Intelligence, 55 (2–3), 193–258.
Markowitz, H. (1991) Portfolio Selection: Efficient Diversification of Investments. B. Blackwell, Cambridge, MA, Second Edition.
Michalski, R., Tecuci, G. (1994) Machine Learning: A multistrategy Approach, Vol IV. Morgan Kaufmann Publishers, San Mateo, CA.
Miyashita, K., Sycara, K. (1995) Cabins: a framework of knowledge acquisition and iterative revision for schedule improvement and reactive repair. Artificial Intelligence, 76 (1–2).
Moore, A. W. (1993) Prioritized sweeping: reinforcement learning with less data and less real time. Machine Learning, October.
Simmons, R. (1994) Structured control for autonomous robots. IEEE Trans. on Robotics and Automation, 10(1), February.
Sutton, R. S. (1988) Learning to predict by the methods of temporal differences. Machine Learning, 3, 9–44.
Sycara, K. (1989a) Argumentation: planning other agents’ plans. In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), Detroit, Mich.
Sycara, K. (1989b) Multi-agent compromise via negotiation. In: M. Huhns, L. Gasser (Eds.) Distributed Artificial Intelligence, Vol 2. Pitman.
Sycara, K., Zeng, D. (1995) Task-based multi-agent coordination for information gathering, in: C. Knoblock, A. Levy (Eds.) Working Notes of the AAAI Spring Symposium Series on Information Gathering from Distributed, Heterogeneous Environments, Stanford, CA, March. AAAI.
Sycara, K., Zeng, D., Miyashita, K. (1995) Using case-based reasoning to acquire user scheduling preferences that change over time. In: Proceedings of the Eleventh IEEE Conference on Artificial Intelligence Applications (CAIA’95), Los Angeles, February, IEEE.
Trippi, R., Turban, E. (Eds.) (1990) Investment Management: Decision Support and Expert Systems. Van Nostrand Reinhold, New York.
Veloso, M. M. (1992) Learning by Analogical Reasoning in General Problem Solving. PhD thesis, Carnegie Mellon University.
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© 1998 Springer-Verlag Berlin Heidelberg
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Sycara, K.P., Zeng, D., Decker, K. (1998). Intelligent Agents in Portfolio Management. In: Jennings, N.R., Wooldridge, M.J. (eds) Agent Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03678-5_14
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DOI: https://doi.org/10.1007/978-3-662-03678-5_14
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