Inducing Desirable Behaviour through an Incentives Infrastructure

  • Roberto Centeno
  • Holger Billhardt
  • Sascha Ossowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6251)


In open multiagent systems, where agents may join/leave the system at runtime, participants can be heterogeneous, self-interested and may have been built with different architectures and languages. Therefore, in such a type of systems, we cannot assure that agents populating them will behave according to the objectives of the system. To address this problem, organisational abstractions, such as roles and norms, have been proposed as a promising solution. Norms are often coupled with penalties and rewards to deter agents from violating the rules of the system. But, what happens if a current population of agents does not care about these penalties/rewards. To deal with this problem, we propose an incentives infrastructure that allows to estimate agents’ preferences, and can modify the consequences of actions in a way that agents have incentives to act in a certain manner. Employing this infrastructure, a desirable behaviour can be induced in the agents to fulfil the preferences of the system.


Utility Function Action Space Multiagent System Incentive Mechanism Selector Module 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Esteva, M., Rodriguez, J., Sierra, C., Garcia, P., Arcos, J.: On the formal specification of electronic institutions. In: Sierra, C., Dignum, F.P.M. (eds.) AgentLink 2000. LNCS (LNAI), vol. 1991, pp. 126–147. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Dignum, V., Vazquez-Salceda, J., Dignum, F.: OMNI: Introducing social structure, norms and ontologies into agent organizations. In: Bordini, R.H., Dastani, M.M., Dix, J., El Fallah Seghrouchni, A. (eds.) PROMAS 2004. LNCS (LNAI), vol. 3346, pp. 181–198. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    DeLoach, S., Oyenan, W., Matson, E.: A capabilities-based theory of artificial organizations. J. Autonomous Agents and Multiagent Systems 16, 13–56 (2008)CrossRefGoogle Scholar
  4. 4.
    Esteva, M., Rosell, B., Rodríguez-Aguilar, J., Arcos, J.: AMELI: An agent-based middleware for electronic institutions. In: Proc. of AAMAS, vol. 1, pp. 236–243 (2004)Google Scholar
  5. 5.
    Centeno, R., Billhardt, H., Hermoso, R., Ossowski, S.: Organising mas: A formal model based on organisational mechanisms. In: Proc. of SAC, pp. 740–746 (2009)Google Scholar
  6. 6.
    Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)zbMATHGoogle Scholar
  7. 7.
    Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press, Cambridge (1993)CrossRefzbMATHGoogle Scholar
  8. 8.
    Boutilier, C., Patrascu, R., Poupart, P., Schuurmans, D.: Regret-based utility elicitation in constraint-based decision problems. In: Proc. of IJCAI, pp. 929–934 (2005)Google Scholar
  9. 9.
    Watkins, C.: Learning from Delayed Rewards. PhD thesis, King’s College, Cambridge, UK (1989)Google Scholar
  10. 10.
    Zhang, H., Parkes, D.: Value-based policy teaching with active indirect elicitation. In: Proc. of AAAI, pp. 208–214. AAAI Press, Menlo Park (2008)Google Scholar
  11. 11.
    Dufton, L., Larson, K.: Multiagent policy teaching. In: Proc. of AAMAS 2009 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Roberto Centeno
    • 1
  • Holger Billhardt
    • 1
  • Sascha Ossowski
    • 1
  1. 1.Centre for Intelligent Information Technologies (CETINIA)University Rey Juan CarlosSpain

Personalised recommendations