Skip to main content

Opportunistic Motivated Learning Agents

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

Included in the following conference series:

  • 1748 Accesses

Abstract

This paper presents an extension of the Motivated Learning model that includes environment masking, and opportunistic behavior of the motivated learning agent. Environment masking improves an agent’s ability to learn by helping to filter out distractions, and the addition of a more complex environment increases the simulation’s realism. If conditions call for it opportunistic behavior allows an agent to deviate from the dominant task to perform a less important but rewarding action. Numerical simulations were performed using Matlab and the implementation of a graphical simulation based on the OGRE engine is in progress. Simulation results show good performance and numerical stability of the attained solution.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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.

Similar content being viewed by others

References

  1. Starzyk, J.A., Prasad, D.K.: A Computational Model of Machine Consciousness. International Journal of Machine Consciousness (2011) (to appear)

    Google Scholar 

  2. Starzyk, J.A., Graham, J.T., Raif, P., Tan, A.-H.: Motivated Learning for Autonomous Robots Development. Cognitive Science Research (2011) (to appear)

    Google Scholar 

  3. Rao, A.S., Georgeff, M.P.: BDI Agents: From Theory to Practice. In: Lesser, V. (ed.) Proceedings of the 1st International Conference on Multi-Agent Systems (ICMAS), pp. 312–319. MIT Press (1995)

    Google Scholar 

  4. Dastani, M., Dignum, F., Meyer, J.-J.: Autonomy, and Agent Deliberation. In: Proc. of the 1st International Workshop on Computational Autonomy (Autonomy 2003) (2003)

    Google Scholar 

  5. Wooldridge, M.: Reasoning about Rational Agents. In: Intelligent Robots and Autonomous Agents. The MIT Press, Cambridge (2000)

    Google Scholar 

  6. Sutton, R.S.: Temporal Credit Assignment in Reinforcement Learning. PhD thesis, University of Massachusetts, Amherst, MA (1984)

    Google Scholar 

  7. Fu, W.-T., Anderson, J.R.: Solving the Credit Assignment Problem: Explicit and Implicit Learning with Internal and External State Information. In: Proceedings of the 28th Annual Conference of the Cognitive Science Society. LEA, Hillsdale (2006)

    Google Scholar 

  8. iCub, RobotCub – An Open Framework for Research in Embodied Cognition (2004), http://www.robotcub.org/

  9. Starzyk, J.A.: Motivation in Embodied Intelligence. In: Frontiers in Robotics, Automation and Control, pp. 83–110. I-Tech Education and Publishing (October 2008)

    Google Scholar 

  10. NeoAxis – all-purpose, modern 3D graphics engine for 3D simulations, visualizations and games, http://www.neoaxis.com/

  11. OGRE – Open Source 3D Graphics Engine, http://www.ogre3d.org

  12. Starzyk, J.A.: Mental Saccades in Control of Cognitive Process. In: Int. Joint Conf. on Neural Networks, San Jose, CA, July 31-August 5 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Graham, J., Starzyk, J.A., Jachyra, D. (2012). Opportunistic Motivated Learning Agents. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29350-4_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics