Advertisement

Observation, Communication and Intelligence in Agent-Based Systems

  • Nader Chmait
  • David L. Dowe
  • David G. Green
  • Yuan-Fang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9205)

Abstract

The intelligence of multiagent systems is known to depend on the communication and observation abilities of its agents. However it is not clear which factor has the greater influence. By following an information-theoretical approach, this study quantifies and analyzes the impact of these two factors on the intelligence of multiagent systems. Using machine intelligence tests, we evaluate and compare the performance of collaborative agents across different communication and observation abilities of measurable entropies. Results show that the effectiveness of multiagent systems with low observation/perception abilities can be significantly improved by using high communication entropies within the agents in the system. We also identify circumstances where these assumptions fail, and analyze the dependency between the studied factors.

Keywords

Multiagent System Direct Communication Communication Range Communication Mode Intelligence Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bettencourt, L.M.A.: The Rules of Information Aggregation and Emergence of Collective Intelligent Behavior. Topics in Cognitive Science 1(4), 598–620 (2009). http://dx.doi.org/10.1111/j.1756-8765.2009.01047.x CrossRefGoogle Scholar
  2. 2.
    Chmait, N., Dowe, D.L., Green, D.G., Li, Y.F., Insa-Cabrera, J.: Measuring universal intelligence in agent-based systems using the anytime intelligence test. Tech. Rep. 2015/279, Faculty of Information Technology, Clayton, Monash University (2015). http://www.csse.monash.edu.au/publications/2015/tr-2015-279-full.pdf
  3. 3.
    Dowe, D.L., Hernández-Orallo, J., Das, P.K.: Compression and intelligence: social environments and communication. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds.) AGI 2011. LNCS, vol. 6830, pp. 204–211. Springer, Heidelberg (2011). http://dx.doi.org/10.1007/978-3-642-22887-2_21
  4. 4.
    Fallenstein, B., Soares, N.: Problems of self-reference in self-improving space-time embedded intelligence. In: Goertzel, B., Orseau, L., Snaider, J. (eds.) AGI 2014. LNCS, vol. 8598, pp. 21–32. Springer, Heidelberg (2014). http://dx.doi.org/10.1007/978-3-319-09274-4_3
  5. 5.
    Franklin, S., Graesser, A.: Is it an agent, or just a program?: A taxonomy for autonomous agents. In: Müller, J.P., Wooldridge, M.J., Jennings, N.R. (eds.) ECAI-WS 1996 and ATAL 1996. LNCS, vol. 1193, pp. 21–35. Springer, Heidelberg (1997). http://dx.doi.org/10.1007/BFb0013570
  6. 6.
    Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artif. Intell. 174(18), 1508–1539 (2010). http://dx.doi.org/10.1016/j.artint.2010.09.006 CrossRefzbMATHGoogle Scholar
  7. 7.
    Insa-Cabrera, J., Benacloch-Ayuso, J.-L., Hernández-Orallo, J.: On measuring social intelligence: experiments on competition and cooperation. In: Bach, J., Goertzel, B., Iklé, M. (eds.) AGI 2012. LNCS, vol. 7716, pp. 126–135. Springer, Heidelberg (2012). http://dx.doi.org/10.1007/978-3-642-35506-6_14
  8. 8.
    Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)CrossRefGoogle Scholar
  9. 9.
    Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11(3), 387–434 (2005). http://dx.doi.org/10.1007/s10458-005-2631-2 CrossRefGoogle Scholar
  10. 10.
    Shannon, C.: A mathematical theory of communication. Bell System Technical Journal 27(3), 379–423 (1948)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Weyns, D., Steegmans, E., Holvoet, T.: Towards active perception in situated multiagent systems. Applied Artificial Intelligence 18(9–10), 867–883 (2004). http://dx.doi.org/10.1080/08839510490509063 CrossRefGoogle Scholar
  12. 12.
    Wooldridge, M., Jennings, N.R.: Intelligent agents: Theory and practice. The Knowledge Engineering Review 10(2), 115–152 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nader Chmait
    • 1
  • David L. Dowe
    • 1
  • David G. Green
    • 1
  • Yuan-Fang Li
    • 1
  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia

Personalised recommendations