Skip to main content

Intelligent Agents in Portfolio Management

  • Chapter
Agent Technology

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Decker, K. S. (1995) Environment Centered Analysis and Design of Co-ordination Mechanisms. PhD thesis, University of Massachusetts.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Etzioni, O., Weld, D. (1994) A softbot-based interface to the Internet. Communications of the ACM, 37(7), July.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Joh, G., Lee, C. (1992) Stock price response to accounting information in oligopoly. Journal of Business,65(3), 451–472, July.

    Google Scholar 

  • Kambhampati, S., Hendler, J. A. (1992) A validation-structure-based theory of plan modification and reuse. Artificial Intelligence, 55 (2–3), 193–258.

    Article  Google Scholar 

  • Markowitz, H. (1991) Portfolio Selection: Efficient Diversification of Investments. B. Blackwell, Cambridge, MA, Second Edition.

    Google Scholar 

  • Michalski, R., Tecuci, G. (1994) Machine Learning: A multistrategy Approach, Vol IV. Morgan Kaufmann Publishers, San Mateo, CA.

    Google Scholar 

  • 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).

    Google Scholar 

  • Moore, A. W. (1993) Prioritized sweeping: reinforcement learning with less data and less real time. Machine Learning, October.

    Google Scholar 

  • Simmons, R. (1994) Structured control for autonomous robots. IEEE Trans. on Robotics and Automation, 10(1), February.

    Google Scholar 

  • Sutton, R. S. (1988) Learning to predict by the methods of temporal differences. Machine Learning, 3, 9–44.

    Google Scholar 

  • Sycara, K. (1989a) Argumentation: planning other agents’ plans. In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), Detroit, Mich.

    Google Scholar 

  • Sycara, K. (1989b) Multi-agent compromise via negotiation. In: M. Huhns, L. Gasser (Eds.) Distributed Artificial Intelligence, Vol 2. Pitman.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Trippi, R., Turban, E. (Eds.) (1990) Investment Management: Decision Support and Expert Systems. Van Nostrand Reinhold, New York.

    Google Scholar 

  • Veloso, M. M. (1992) Learning by Analogical Reasoning in General Problem Solving. PhD thesis, Carnegie Mellon University.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-03678-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08344-0

  • Online ISBN: 978-3-662-03678-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics