Preference Elicitation for DCOPs

  • Atena M. Tabakhi
  • Tiep Le
  • Ferdinando Fioretto
  • William Yeoh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10416)

Abstract

Distributed Constraint Optimization Problems (DCOPs) offer a powerful approach for the description and resolution of cooperative multi-agent problems. In this model, a group of agents coordinate their actions to optimize a global objective function, taking into account their preferences or constraints. A core limitation of this model is the assumption that the preferences of all agents or the costs of all constraints are specified a priori. Unfortunately, this assumption does not hold in a number of application domains where preferences or constraints must be elicited from the users. One of such domains is the Smart Home Device Scheduling (SHDS) problem. Motivated by this limitation, we make the following contributions in this paper: (1) We propose a general model for preference elicitation in DCOPs; (2) We propose several heuristics to elicit preferences in DCOPs; and (3) We empirically evaluate the effect of these heuristics on random binary DCOPs as well as SHDS problems.

Keywords

Distributed Constraint Optimization Smart homes Preference elicitation 

References

  1. 1.
    Abdennadher, S., Schlenker, H.: Nurse scheduling using constraint logic programming. In: Proceedings of the Conference on Innovative Applications of Artificial Intelligence (IAAI), pp. 838–843 (1999)Google Scholar
  2. 2.
    Anderson, B., Lin, S., Newing, A., Bahaj, A., James, P.: Electricity consumption and household characteristics: implications for census-taking in a smart metered future. Comput. Environ. Urban Syst. 63, 58–67 (2017)CrossRefGoogle Scholar
  3. 3.
    Bacchus, F., Grove, A.J.: Utility independence in a qualitative decision theory. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 542–552 (1996)Google Scholar
  4. 4.
    Boutilier, C.: A POMDP formulation of preference elicitation problems. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), pp. 239–246 (2002)Google Scholar
  5. 5.
    Boutilier, C., Patrascu, R., Poupart, P., Schuurmans, D.: Regret-based utility elicitation in constraint-based decision problems. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 929–934 (2005)Google Scholar
  6. 6.
    Braziunas, D.: Computational approaches to preference elicitation. Technical report (2006)Google Scholar
  7. 7.
    Braziunas, D., Boutilier, C.: Local utility elicitation in GAI models. In: Proceedings of the Conference in Uncertainty in Artificial Intelligence (UAI), pp. 42–49 (2005)Google Scholar
  8. 8.
    Erdös, P., Rényi, A.: On random graphs, I. Publ. Math. (Debr.) 6, 290–297 (1959)MATHGoogle Scholar
  9. 9.
    Farinelli, A., Rogers, A., Petcu, A., Jennings, N.: Decentralised coordination of low-power embedded devices using the Max-Sum algorithm. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 639–646 (2008)Google Scholar
  10. 10.
    Fioretto, F., Pontelli, E., Yeoh, W.: Distributed constraint optimization problems and applications: a survey. CoRR, abs/1602.06347 (2016)Google Scholar
  11. 11.
    Fioretto, F. Yeoh, W., Pontelli, E.: A dynamic programming-based MCMC framework for solving DCOPs with GPUs. In: Proceedings of Principles and Practice of Constraint Programming (CP), pp. 813–831 (2016)Google Scholar
  12. 12.
    Fioretto, F., Yeoh, W., Pontelli, E.: Multi-variable agent decomposition for DCOPs. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2016)Google Scholar
  13. 13.
    Fioretto, F., Yeoh, W., Pontelli, E.: A multiagent system approach to scheduling devices in smart homes. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 981–989 (2017)Google Scholar
  14. 14.
    Fioretto, F., Yeoh, W., Pontelli, E., Ma, Y., Ranade, S.: A DCOP approach to the economic dispatch with demand response. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 981–989 (2017)Google Scholar
  15. 15.
    Gelain, M., Pini, M.S., Rossi, F., Venable, K.B., Walsh, T.: Elicitation strategies for fuzzy constraint problems with missing preferences: algorithms and experimental studies. In: Stuckey, P.J. (ed.) CP 2008. LNCS, vol. 5202, pp. 402–417. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85958-1_27 CrossRefGoogle Scholar
  16. 16.
    Goldsmith, J., Junker, U.: Preference handling for artificial intelligence. AI Mag. 29(4), 9–12 (2008)CrossRefGoogle Scholar
  17. 17.
    Hatano, D., Hirayama, K.: DeQED: an efficient divide-and-coordinate algorithm for DCOP. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 566–572 (2013)Google Scholar
  18. 18.
    Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1–2), 99–134 (1998)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Proceedings of the Workshop on Data Mining Applications in Sustainability, pp. 59–62 (2011)Google Scholar
  20. 20.
    Larrosa, J.: Node and arc consistency in weighted CSP. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 48–53 (2002)Google Scholar
  21. 21.
    Le, T., Fioretto, F., Yeoh, W., Son, T.C., Pontelli, E.: ER-DCOPs: a framework for distributed constraint optimization with uncertainty in constraint utilities. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2016)Google Scholar
  22. 22.
    Le, T., Son, T.C., Pontelli, E., Yeoh, W.: Solving distributed constraint optimization problems with logic programming. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2015)Google Scholar
  23. 23.
    Maheswaran, R., Pearce, J., Tambe, M.: Distributed algorithms for DCOP: a graphical game-based approach. In: Proceedings of the International Conference on Parallel and Distributed Computing Systems (PDCS), pp. 432–439 (2004)Google Scholar
  24. 24.
    Miller, S., Ramchurn, S., Rogers, A.: Optimal decentralised dispatch of embedded generation in the smart grid. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 281–288 (2012)Google Scholar
  25. 25.
    Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I.: Internet of things: vision, applications and research challenges. Ad Hoc Netw. 10(7), 1497–1516 (2012)CrossRefGoogle Scholar
  26. 26.
    Modi, P.: Distributed constraint optimization for multiagent systems. Ph.D. thesis, University of Southern California, Los Angeles (United States) (2003)Google Scholar
  27. 27.
    Modi, P., Shen, W.-M., Tambe, M., Yokoo, M.: ADOPT: asynchronous distributed constraint optimization with quality guarantees. Artif. Intell. 161(1–2), 149–180 (2005)MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Netzer, A., Grubshtein, A., Meisels, A.: Concurrent forward bounding for distributed constraint optimization problems. Artif. Intell. 193, 186–216 (2012)MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Nguyen, D.T., Yeoh, W., Lau, H.C., Gibbs, D.: A memory-bounded sampling-based DCOP algorithm. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 167–174 (2013)Google Scholar
  30. 30.
    Nguyen, D.T., Yeoh, W., Lau, H.C., Zilberstein, S., Zhang, C.: Decentralized multi-agent reinforcement learning in average-reward dynamic DCOPs. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 1447–1455 (2014)Google Scholar
  31. 31.
    Ottens, B., Dimitrakakis, C., Faltings, B.: DUCT: an upper confidence bound approach to distributed constraint optimization problems. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 528–534 (2012)Google Scholar
  32. 32.
    Paatero, J.V., Lund, P.D.: A model for generating household electricity load profiles. Int. J. Energy Res. 30(5), 273–290 (2006)CrossRefGoogle Scholar
  33. 33.
    Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1413–1420 (2005)Google Scholar
  34. 34.
    Rodrigues, L., Magatao, L.: Enhancing supply chain decisions using constraint programming: a case study. In: Proceedings of the Mexican International Conference on Artificial Intelligence (MICAI), pp. 1110–1121 (2007)Google Scholar
  35. 35.
    Rossi, F., Venable, K.B., Walsh, T.: Preferences in constraint satisfaction and optimization. AI Mag. 29(4), 58–68 (2008)CrossRefGoogle Scholar
  36. 36.
    Shapiro, L.G., Haralick, R.M.: Structural descriptions and inexact matching. IEEE Trans. Pattern Anal. Mach. Intell. 5, 504–519 (1981)CrossRefGoogle Scholar
  37. 37.
    Stranders, R., Delle Fave, F., Rogers, A., Jennings, N.: DCOPs and bandits: exploration and exploitation in decentralised coordination. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 289–297 (2012)Google Scholar
  38. 38.
    Stuckey, P.J., Becket, R., Brand, S., Brown, M., Feydy, T., Fischer, J., de la Banda, M.G., Marriott, K., Wallace, M.: The evolving world of MiniZinc. In: Constraint Modelling and Reformulation, pp. 156–170 (2007)Google Scholar
  39. 39.
    Taylor, M., Jain, M., Tandon, P., Yokoo, M., Tambe, M.: Distributed on-line multi-agent optimization under uncertainty: balancing exploration and exploitation. Adv. Complex Syst. 14(03), 471–528 (2011)CrossRefGoogle Scholar
  40. 40.
    Ueda, S., Iwasaki, A., Yokoo, M.: Coalition structure generation based on distributed constraint optimization. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 197–203 (2010)Google Scholar
  41. 41.
    Vinyals, M., Rodríguez-Aguilar, J., Cerquides, J.: Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law. J. Auton. Agents Multi-Agent Syst. 22(3), 439–464 (2011)CrossRefGoogle Scholar
  42. 42.
    Wang, T., Boutilier, C.: Incremental utility elicitation with the minimax regret decision criterion. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 309–318 (2003)Google Scholar
  43. 43.
    Wu, F., Jennings, N.: Regret-based multi-agent coordination with uncertain task rewards. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 1492–1499 (2014)Google Scholar
  44. 44.
    Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm. J. Artif. Intell. Res. 38, 85–133 (2010)MATHGoogle Scholar
  45. 45.
    Yeoh, W., Yokoo, M.: Distributed problem solving. AI Mag. 33(3), 53–65 (2012)CrossRefGoogle Scholar
  46. 46.
    Zivan, R., Okamoto, S., Peled, H.: Explorative anytime local search for distributed constraint optimization. Artif. Intell. 212, 1–26 (2014)MathSciNetCrossRefMATHGoogle Scholar
  47. 47.
    Zivan, R., Yedidsion, H., Okamoto, S., Glinton, R., Sycara, K.: Distributed constraint optimization for teams of mobile sensing agents. J. Auton. Agents Multi-Agent Syst. 29(3), 495–536 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Atena M. Tabakhi
    • 1
  • Tiep Le
    • 2
  • Ferdinando Fioretto
    • 3
  • William Yeoh
    • 1
  1. 1.Department of Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA
  2. 2.Department of Computer ScienceNew Mexico State UniversityLas CrucesUSA
  3. 3.Department of Industrial and Operations EngineeringUniversity of MichiganAnn ArborUSA

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