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From Programming Agents to Educating Agents – A Jason-Based Framework for Integrating Learning in the Development of Cognitive Agents

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Engineering Multi-Agent Systems (EMAS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12058))

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Abstract

Recent advances and successes of machine learning techniques are paving the way to what is referred as Software 2.0 era and cognitive computing, in which traditional programming and software development is meant to be replaced by such techniques for many applications. If we consider agent-oriented programming, we believe that such developments trigger new interesting scenarios blending cognitive architecture such as the BDI one and techniques like Reinforcement Learning (RL) even more deeply compared to what has been proposed so far in the literature. In that perspective, we aim at exploring the integration of cognitive agent-oriented programming based on BDI with learning techniques so as to systematically exploit them in the agent development stage. The approach should support the design of BDI agents in which some plans can be explicitly programmed and others instead can be learned by the agent during the development/engineering stage. In that view, the development of an agent is metaphorically similar to an education process, in which first an agent is created with a set of basic programmed plans and then grow up in order to learn plans to achieve the goals for which the agent is meant to be designed. This paper presents and discusses this medium-term view, introducing a first model for a BDI agent programming framework integrating RL, a first implementation based on Jason programming language/platform and sketching a roadmap for this research line.

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Notes

  1. 1.

    hard in this case stands for hard-coded.

  2. 2.

    https://github.com/MichaelBosello/jacamo-rl.

  3. 3.

    We thank the reviewers for this suggestion.

References

  1. Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/. Software available from tensorflow.org

  2. Airiau, S., Padgham, L., Sardina, S., Sen, S.: Enhancing the adaptation of BDI agents using learning techniques. Int. J. Agent Technol. Syst. 1(2), 1–18 (2009)

    Article  Google Scholar 

  3. Andrew McAfee, E.B.: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company, New York (2014)

    Google Scholar 

  4. Badica, A., Badica, C., Ivanovic, M., Mitrovic, D.: An approach of temporal difference learning using agent-oriented programming. In: 20th International Conference on Control Systems and Computer Science, pp. 735–742, May 2015

    Google Scholar 

  5. Badica, C., Becheru, A., Felton, S.: Integration of jason reinforcement learning agents into an interactive application. In: 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 361–368, September 2017

    Google Scholar 

  6. Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. SMC-13(5), 834–846 (1983)

    Google Scholar 

  7. Bordini, R.H., Hübner, J.F., Vieira, R.: Jason and the golden fleece of agent-oriented programming. In: Bordini, R.H., et al. (eds.) Multi-Agent Programming: Languages, Platforms and Applications, pp. 3–37. Springer, Boston (2005). https://doi.org/10.1007/0-387-26350-0_1

    Chapter  Google Scholar 

  8. Bordini, R.H., Hübner, J.F., Wooldridge, M.: Programming Multi-Agent Systems in AgentSpeak Using Jason (Wiley Series in Agent Technology). Wiley, Hoboken (2007)

    Book  Google Scholar 

  9. Botvinick, M., Niv, Y., C Barto, A.: Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition 113, 262–280 (2008)

    Google Scholar 

  10. Brockman, G., et al.: OpenAI Gym (2016)

    Google Scholar 

  11. Brys, T., Harutyunyan, A., Suay, H.B., Chernova, S., Taylor, M.E., Nowé, A.: Reinforcement learning from demonstration through shaping. In: Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI 2015), pp. 3352–3358. AAAI Press (2015)

    Google Scholar 

  12. Feliú, J.L.S.: Use of Reinforcement Learning (RL) for plan generation in Belief-Desire-Intention (BDI) agent systems. University of Rhode Island (2013)

    Google Scholar 

  13. Ford, M.: Architects of Intelligence: The Truth About AI from the People Building It. Packt Publishing, Birmingham (2018)

    Google Scholar 

  14. Gerrish, S.: How Smart Machines Think. MIT Press, Cambridge (2018)

    Google Scholar 

  15. Guerra-Hernández, A., El Fallah-Seghrouchni, A., Soldano, H.: Learning in BDI multi-agent systems. In: Dix, J., Leite, J. (eds.) CLIMA 2004. LNCS (LNAI), vol. 3259, pp. 218–233. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30200-1_12

    Chapter  MATH  Google Scholar 

  16. Karim, S., Sonenberg, L., Tan, A.-H.: A hybrid architecture combining reactive plan execution and reactive learning. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 200–211. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-36668-3_23

    Chapter  Google Scholar 

  17. Kelly, J.E.: Computing, cognition and the future of knowing (2015). IBM Research and Solutions, white paper

    Google Scholar 

  18. Meijer, E.: Behind every great deep learning framework is an even greater programming languages concept (2018). Invited Talk at the 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

    Google Scholar 

  19. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518 (2015)

    Google Scholar 

  20. Moore, A.: Efficient memory-based learning for robot control. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA, March 1991

    Google Scholar 

  21. Norling, E.: Folk psychology for human modelling: extending the BDI paradigm. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), pp. 202–209. IEEE Computer Society, Washington, DC (2004)

    Google Scholar 

  22. Parnas, D.L.: The real risks of artificial intelligence. Commun. ACM 60(10), 27–31 (2017)

    Article  Google Scholar 

  23. Rao, A.S.: AgentSpeak(L): BDI agents speak out in a logical computable language. In: Van de Velde, W., Perram, J.W. (eds.) MAAMAW 1996. LNCS, vol. 1038, pp. 42–55. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0031845

    Chapter  Google Scholar 

  24. Rao, A.S., Georgeff, M.P.: BDI agents: from theory to practice. In: Proceedings of the First International Conference on Multi-Agent Systems (ICMAS 1995), pp. 312–319 (1995)

    Google Scholar 

  25. Ricci, A., Piunti, M., Viroli, M.: Environment programming in multi-agent systems: an artifact-based perspective. Auton. Agents Multi-Agent Syst. 23(2), 158–192 (2011)

    Article  Google Scholar 

  26. Guadarrama, S., et al.: TF-Agents: A library for reinforcement learning in tensorflow (2018). https://github.com/tensorflow/agents

  27. Shoham, Y.: Agent-oriented programming. Artif. Intell. 60(1), 51–92 (1993)

    Article  MathSciNet  Google Scholar 

  28. Singh, D., Hindriks, K.V.: Learning to improve agent behaviours in goal. In: Dastani, M., Hübner, J.F., Logan, B. (eds.) Programming Multi-Agent Systems, pp. 158–173. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  29. Singh, D., Sardina, S., Padgham, L., James, G.: Integrating learning into a BDI agent for environments with changing dynamics. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 2525–2530. AAAI Press (2011)

    Google Scholar 

  30. Sutton, R.S., Barto, A.G.: Reinforcement Learning : An Introduction. The MIT Press, Cambridge (2018)

    Google Scholar 

  31. Tan, A.H., Ong, Y.S., Tapanuj, A.: A hybrid agent architecture integrating desire, intention and reinforcement learning. Expert Syst. Appl. 38(7), 8477–8487 (2011)

    Article  Google Scholar 

  32. Tanz, J.: The end of code. Wired (2016)

    Google Scholar 

  33. Weiß, G.: Adaptation and learning in multi-agent systems: some remarks and a bibliography. In: Weiß, G., Sen, S. (eds.) IJCAI 1995. LNCS, vol. 1042, pp. 1–21. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-60923-7_16

    Chapter  Google Scholar 

  34. Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3), 229–256 (1992)

    MathSciNet  MATH  Google Scholar 

  35. Wooldridge, M.: Introduction to Multi-Agent Systems. Wiley, Hoboken (2009)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Alma Mater Studiorum – Università di Bologna, Cesena Campus, Cesena, Italy

    Michael Bosello & Alessandro Ricci

Authors
  1. Michael Bosello
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  2. Alessandro Ricci
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Correspondence to Alessandro Ricci .

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Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Louise A. Dennis

  2. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil

    Rafael H. Bordini

  3. University of York, Toronto, ON, Canada

    Yves Lespérance

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Bosello, M., Ricci, A. (2020). From Programming Agents to Educating Agents – A Jason-Based Framework for Integrating Learning in the Development of Cognitive Agents. In: Dennis, L., Bordini, R., Lespérance, Y. (eds) Engineering Multi-Agent Systems. EMAS 2019. Lecture Notes in Computer Science(), vol 12058. Springer, Cham. https://doi.org/10.1007/978-3-030-51417-4_9

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  • DOI: https://doi.org/10.1007/978-3-030-51417-4_9

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  • Online ISBN: 978-3-030-51417-4

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