A Learning Model for Intelligent Agents Applied to Poultry Farming

Richardson Ribeiro, Marcelo Teixeira, André L. Wirth, André P. Borges, Fabrício Enembreck

2015

Abstract

This paper proposes a learning model for taking-decision problems using intelligent agents technologies combined with instance-based machine learning techniques. Our learning model is applied to a real case to support the daily decisions of a poultry farmer. The agent of the system is used to generate action policies, in order to control a set of factors in the daily activities, such as food-meat conversion, amount of food to be consumed, time to rest, weight gain, comfort temperature, water and energy to be consumed, etc. The perception of the agent is ensured by a set of sensors scattered by the physical structure of the poultry. The principal role of the agent is to perform a set of actions in a way to consider aspects such as productivity and profitability without compromising bird welfare. Experimental results have shown that, for the decision-taking process in poultry farming, our model is sound, advantageous and can substantially improve the agent actions in comparison with equivalent decision when taken by a human specialist.

References

  1. Aamodt, A. and Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications, 7(1):39-59.
  2. Abdel-Aziz, A., Strickert, M., and Hüllermeier, E. (2014). Learning solution similarity in preference-based cbr. In Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, ICCBR 7814, pages 17-31.
  3. Aha, D. W., Kibler, D., and Albert, M. K. A. (1991). Instance-based learning algorithms. Machine Learning, 6(1):37-66.
  4. Amores, J. (2013). Multiple instance classification: Review, taxonomy and comparative study. Artificial Intelligence, 201:81-105.
  5. Andrieu, C., de Freitas, N., Doucet, A., and Jordan, M. I. (2003). An introduction to mcmc for machine learning. Machine learning, 50(1):5-43.
  6. Arowolo, H., Weaver, W. D. Jr. and Amosa, B., and Faleye, E. (2012). An expert system for management of poultry diseases. International Proceedings of Computer Science & Information Tech., 47:113-117.
  7. Au, T.-C., Zhang, S., and Stone, P. (2014). Semiautonomous intersection management. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 7814, pages 1451-1452.
  8. Bachrach, Y., Savani, R., and Shah, N. (2014). Cooperative max games and agent failures. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 7814, pages 29-36.
  9. Bakst, M. R., Akuffo, V., Nicholson, D., and French, N. (2012). Comparison of blastoderm traits from 2 lines of broilers before and after egg storage and incubation. Poultry Science, 91(10):2645-2648.
  10. Castelfranchi, C. (1997). To be or not to be an agent. In Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages, ECAI 7896, pages 37-39.
  11. Charles, T. B. and Stuart, H. O. (2011). Commercial poultry farming. Biotech Books, 6 th edition.
  12. Closter, A. M., Van As, P., Elferink, M. G., Crooijmanns, R. P. M. A., Groenen, M. A. M., Vereijken, A. L. J., Van Arendonk, J. A. M., and Bovenhuis, H. (2012). Genetic correlation between heart ratio and body weight as a function of ascites frequency in broilers split up into sex and health status. Southern journal of agricultural economics, 91(3):556-564.
  13. Cobo, L. C., Isbell, C. L., and Thomaz, A. L. (2013). Object focused q-learning for autonomous agents. In Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 7813, pages 1061-1068.
  14. Enembreck, F. and Barthès, J.-P. (2005). Ela - a new approach for learning agents. Autonomous Agents and Multi-Agent Systems, 10(3):215-248.
  15. Ferket, P. R. and Gernat, A. G. (2006). Factors that affect feed intake of meat birds: A review. International Journal of Poultry, 10(5):905-911.
  16. Fontana, E. A., Weaver, W. D. J., Watkins, B. A., and Denbow, D. M. (1992). Effect of early feed restriction on growth, feed conversion, and mortality in broiler chickens. Poultry Science, 71(8):1296-1305.
  17. Jaidee, U., Mun˜oz-Avila, H., and Aha, D. W. (2013). Casebased goal-driven coordination of multiple learning agents. In International Conference on Case-Based Reasoning, ICCBR 7813, pages 164-178.
  18. Jiang, S., Zhang, J., and Ong, Y.-S. (2014). A pheromonebased traffic management model for vehicle re-routing and traffic light control. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 7814, pages 1479-1480.
  19. Leake, D. and McSherry, D. (2005). Introduction to the special issue on explanation in case-based reasoning. Artificial Intelligence Review, 24(2):103-108.
  20. Lee, D., Lyu, S., Wang, R., Weng, C., and Chen, B. (2011). Exhibit differential functions of various antibiotic growth promoters in broiler growth, immune response and gastrointestinal physiology. International Journal of Poultry Science, 10(3):216-220.
  21. Maes, P. (1995). Artificial life meets entertainment: Lifelike autonomous agents. Communications of the ACM, 38(11):108-114.
  22. Maseleno, A. and Hasan, M. M. (2012). Poultry diseases expert system using dempster-shafer theory and web mapping. International Journal of Advanced Research in Artificial Intelligence, 1(3):44-48.
  23. McSherry, D. (2014). An algorithm for conversational casebased reasoning in classification tasks. In Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, ICCBR 7814, pages 289-304.
  24. Northcutt, J. K. and Jones, D. R. (2004). A survey of water use and common industry practices in commercial broiler processing facilities. Jounal Applied Poultry Research, 13(1):48-54.
  25. Ribeiro, R. and Enembreck, F. (2013). A sociologically inspired heuristic for optimization algorithms: A case study on ant systems. Expert Systems With Applications, 40(5):1814-1826.
  26. Ribeiro, R., Favarim, F., Barbosa, M. A. C., Borges, A. P., Dordal, O. B., Koerich, A. L., and Enembreck, F. (2012). Unified algorithm to improve reinforcement learning in dynamic environments - an instance-based approach. In International Conference on Enterprise Information Systems, ICEIS 7812, pages 229-238.
  27. Ribeiro, R., Ronszcka, A. F., Barbosa, M. A. C., Favarim, F., and Enembreck, F. (2013). Updating strategies of policies for coordinating agent swarm in dynamic environments. In International Conference on Enterprise Information Systems, ICEIS 7813, pages 345-356.
  28. Schmisseur, E. and Pankratz, J. (1989). Xlayer: an expert system providing management advice to commercial layer managers. Southern journal of agricultural economics, 2(21):183-193.
  29. Shariatmadari, F. (2012). Plans of feeding broiler chickens. World's Poultry Science Journal, 68(1):21-30.
  30. Tavarez, M. A., Boler, D. D., Bess, K. N., Zhao, J., Yan, F., Dilger, A. C., McKeith, F. K., and Killefer, J. (2011). Effect of antioxidant inclusion and oil quality on broiler performance, meat quality, and lipid oxidation. Poultry Science, 90(4):922-930.
  31. Teixeira, M., Malik, R., Cury, J., and de Queiroz, M. (2014). Supervisory control of des with extended finite-state machines and variable abstraction. Automatic Control, IEEE Transactions on, 60(1):118-129.
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Paper Citation


in Harvard Style

Ribeiro R., Teixeira M., L. Wirth A., P. Borges A. and Enembreck F. (2015). A Learning Model for Intelligent Agents Applied to Poultry Farming . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 495-503. DOI: 10.5220/0005373604950503


in Bibtex Style

@conference{iceis15,
author={Richardson Ribeiro and Marcelo Teixeira and André L. Wirth and André P. Borges and Fabrício Enembreck},
title={A Learning Model for Intelligent Agents Applied to Poultry Farming},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={495-503},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005373604950503},
isbn={978-989-758-096-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Learning Model for Intelligent Agents Applied to Poultry Farming
SN - 978-989-758-096-3
AU - Ribeiro R.
AU - Teixeira M.
AU - L. Wirth A.
AU - P. Borges A.
AU - Enembreck F.
PY - 2015
SP - 495
EP - 503
DO - 10.5220/0005373604950503