Inducing Desirable Behaviour through an Incentives Infrastructure
- 471 Downloads
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.
KeywordsUtility Function Action Space Multiagent System Incentive Mechanism Selector Module
Unable to display preview. Download preview PDF.
- 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
- 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.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
- 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.Watkins, C.: Learning from Delayed Rewards. PhD thesis, King’s College, Cambridge, UK (1989)Google Scholar
- 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.Dufton, L., Larson, K.: Multiagent policy teaching. In: Proc. of AAMAS 2009 (2009)Google Scholar