Experiments with Multiple BDI Agents with Dynamic Learning Capabilities
In this paper we show how multiple BDI agents, enhanced with temporal difference learning capabilities, learn their utility function, while they are concurrently exploring an uncertain environment. We focus on the programming aspects of the agents using the Jason agent-oriented programming language. We also provide experimental results showing the behavior of multiple agents acting in a Markovian grid environment. We consider agents with the perception function affected by the intermittent faults and Gaussian noise, as well as agents for which their action function is not always successful.
KeywordsBDI agent Reinforcement learning Agent-oriented programming
- 2.Bădică, A., Bădică, C., Ivanović, M., Mitrović, D.: An approach of temporal difference learning using agent-oriented programming. In: Proceedings of the 20th International Conference on Control Systems and Computer Science (CSCS 2015), pp. 735–742. IEEE, (2015). 10.1109/CSCS.2015.71
- 3.Bellifemine, F.L., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE, ser. Wiley Series in Agent Technology. John Wiley & Sons Ltd (2007)Google Scholar
- 4.Bordini, R.H., Hũbner, J.F., Wooldridge, M.: Programming Multi-Agent Systems in AgentSpeak using Jason, ser. Wiley Series in Agent Technology. Wiley (2007)Google Scholar
- 6.Jason: a Java-based interpreter for an extended version of AgentSpeak. http://jason.sourceforge.net//. Accessed February, 2016
- 7.Jennings, N.R., Wooldridge, M.: Applications of intelligent agents. In: Jennings, N.R., Wooldridge, M.J. (eds.) Agent Technology, pp. 3–28. Heidelberg (1998). http://dl.acm.org/citation.cfm?id=277789.277799