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Fusion of Game Theory and Big Data for AI Applications

  • Praveen ParuchuriEmail author
  • Sujit Gujar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11297)

Abstract

With the increasing reach of the Internet, more and more people and their devices are coming online which has resulted in the fact that, a significant amount of our time and a significant number of tasks are getting performed online. As the world moves faster towards more automation and as concepts such as IoT catch up, a lot more (data generation) devices are getting added online without needing the involvement of human agents. The result of all this is that there will be lots (and lots) of information generated in a variety of contexts, in a variety of formats at a variety of rates. Big data analytics therefore becomes (and is already) a vital topic to gain insights or understand the trends encoded in the large datasets. For example, the worldwide Big Data market revenues for software and services are projected to increase from 42 Billion USD in 2018 to 103 Billion in 2027. However, in the real-world it may not be enough to just perform analysis, but many times there may be a need to operationalize the insights to obtain strategic advantages. Game theory being a mathematical tool to analyze strategic interactions between rational decision-makers, in this paper, we study the usage of Game Theory to obtain strategic advantages in different settings involving usage of large amounts of data. The goal is to provide an overview of the use of game theory in different applications that rely extensively on big data. In particular, we present case studies of four different Artificial Intelligence (AI) applications namely Information Markets, Security systems, Trading agents and Internet Advertising and present details for how game theory helps to tackle them. Each of these applications has been studied in detail in the game theory literature, and different algorithms and techniques have been developed to address the different challenges posed by them.

References

  1. 1.
    Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002)CrossRefGoogle Scholar
  2. 2.
    Babaioff, M., Kleinberg, R.D., Slivkins, A.: Truthful mechanisms with implicit payment computation. In: Eleventh ACM Conference on Electronic Commerce, pp. 43–52. ACM (2010)Google Scholar
  3. 3.
    Babaioff, M., Sharma, Y., Slivkins, A.: Characterizing truthful multi-armed bandit mechanisms: extended abstract. In: Tenth ACM Conference on Electronic Commerce, pp. 79–88. ACM (2009)Google Scholar
  4. 4.
    Basilico, N., Gatti, N., Amigoni., F.: Leader-follower strategies for robotic patrolling in environments with arbitrary topologies. In: AAMAS, pp. 57–64 (2009)Google Scholar
  5. 5.
    Brown, G., Carlyle, M., Salmeron, J., Wood., K.: Defending critical infrastructure. Interfaces 36(6), 530–544 (2006)CrossRefGoogle Scholar
  6. 6.
    Brown, M., Sinha, A., Schlenker, A., Tambe., M.: One size does not fit all: a game-theoretic approach for dynamically and effectively screening for threats. In: AAAI (2016)Google Scholar
  7. 7.
    Budhiraja, A., Reddy, P.K.: An improved approach for long tail advertising in sponsored search. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10178, pp. 169–184. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55699-4_11CrossRefGoogle Scholar
  8. 8.
    Cheng, H., Cantú-Paz, E.: Personalized click prediction in sponsored search. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 351–360. ACM (2010)Google Scholar
  9. 9.
    Chowdhury, M.M.P., Kiekintveld, C., Son, T.C., Yeoh, W.: Bidding strategy for periodic double auctions using Monte Carlo tree search. In: AAMAS, pp. 1897–1899 (2018)Google Scholar
  10. 10.
    Columbus., L.: 10 charts that will change your perspective of big data’s growth (2018). https://www.forbes.com/sites/louiscolumbus/2018/05/23/10-charts-that-will-change-your-perspective-of-big-datas-growth/#3c7b5c0d2926. Accessed Oct 2018
  11. 11.
    Conitzer, V., Sandholm., T.: Computing the optimal strategy to commit to. In: Proceedings of ACM EC, pp. 82–90 (2006)Google Scholar
  12. 12.
    Cowgill, B., Wolfers, J., Zitzewitz, E.: Using prediction markets to track information flows: evidence from google. In: Das, S., Ostrovsky, M., Pennock, D., Szymanksi, B. (eds.) AMMA 2009. LNICST, vol. 14, p. 3. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-03821-1_2CrossRefGoogle Scholar
  13. 13.
    Cuevas, J.S., Rodriguez-Gonzalez, A.Y., Cote, E.M.D.: Fixed-price tariff generation using reinforcement learning. In: Modern Approaches to Agent-based Complex Automated Negotiation, pp. 121–136 (2017)Google Scholar
  14. 14.
    Devanur, N.R., Kakade, S.M.: The price of truthfulness for pay-per-click auctions. In: Tenth ACM Conference on Electronic Commerce, pp. 99–106 (2009)Google Scholar
  15. 15.
    Faltings, B., Li, J.J., Jurca, R.: Incentive mechanisms for community sensing. IEEE Trans. Comput. 63(1), 115–128 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Faltings, B., Radanovic, G.: Game theory for data science: eliciting truthful information. Synth. Lect. Artif. Intell. Mach. Learn. 11(2), 1–151 (2017)CrossRefGoogle Scholar
  17. 17.
    Fang, F., Nguyen., T.: Green security games: apply game theory to addressing green security challenges. In: ACM SIGecom Exchanges (2016)Google Scholar
  18. 18.
    Fang, F., Stone, P., Tambe., M.: When security games go green: designing defender strategies to prevent poaching and illegal fishing. In: IJCAI (2015)Google Scholar
  19. 19.
    Garg, D., Narahari, Y., Gujar, S.: Foundations of mechanism design: a tutorial part 1-key concepts and classical results. Sadhana 33(2), 83–130 (2008)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Garg, D., Narahari, Y., Gujar, S.: Foundations of mechanism design: a tutorial part 2-advanced concepts and results. Sadhana 33(2), 131–174 (2008)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Gatti, N., Lazaric, A., Trovò, F.: A truthful learning mechanism for contextual multi-slot sponsored search auctions with externalities. In: Thirteenth ACM Conference on Electronic Commerce, pp. 605–622 (2012)Google Scholar
  22. 22.
    Ghalme, G., Jain, S., Gujar, S., Narahari, Y.: Thompson sampling based mechanisms for stochastic multi-armed bandit problems. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 87–95. International Foundation for Autonomous Agents and Multiagent Systems (2017)Google Scholar
  23. 23.
    Ghose, A., Yang, S.: An empirical analysis of sponsored search performance in search engine advertising. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 241–250. ACM (2008)Google Scholar
  24. 24.
    Hanson, R.: Logarithmic markets coring rules for modular combinatorial information aggregation. J. Prediction Markets 1(1), 3–15 (2012)Google Scholar
  25. 25.
    Hillard, D., Schroedl, S., Manavoglu, E., Raghavan, H., Leggetter, C.: Improving ad relevance in sponsored search. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 361–370 (2010)Google Scholar
  26. 26.
    Joshi, A., Motwani, R.: Keyword generation for search engine advertising. In: Sixth IEEE International Conference on Data Mining Workshops ICDM Workshops 2006, pp. 490–496. IEEE (2006)Google Scholar
  27. 27.
    Jutzeler, A., Li, J.J., Faltings, B.: A region-based model for estimating urban air pollution. In: Proceedings of the 28th Conference on Artificial Intelligence (AAAI), pp. 424–430 (2014)Google Scholar
  28. 28.
    Ketter, W., Collins, J., Reddy., P.: Power tac: a competitive economic simulation of the smart grid. Energy Econ. 39, 262–270 (2013)CrossRefGoogle Scholar
  29. 29.
    Ketter, W., Collins, J., Weerdt, M.: The 2018 power trading agent competition (2017)Google Scholar
  30. 30.
    Liefers, B., Hoogland, J., Poutré, H.L.: A successful broker agent for Power TAC. In: Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets, pp. 99–113 (2014)Google Scholar
  31. 31.
    Mansour, Y., Slivkins, A., Syrgkanis, V.: Bayesian incentive-compatible bandit exploration. In: Proceedings of the Sixteenth ACM Conference on Economics and Computation, pp. 565–582. ACM (2015)Google Scholar
  32. 32.
    Moore, G.E.: Cramming more components onto integrated circuits. Electron. Magaz. (1965). Accessed Oct 2018Google Scholar
  33. 33.
    Narahari, Y.: Game theory and mechanism design. World Scientific 4 (2014)Google Scholar
  34. 34.
    Nash, J.: Non-cooperative games. Ann. Math. 54, 286–295 (1951)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Özdemir, S., Unland, R.: AgentUDE17: a genetic algorithm to optimize the parameters of an electricity tariff in a smart grid environment. In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection, pp. 224–236 (2018)CrossRefGoogle Scholar
  36. 36.
    Panda, S., Vorobeychik., Y.: Stackelberg games for vaccine design. In: AAMAS, pp. 1391–1399 (2015)Google Scholar
  37. 37.
    Paruchuri, P., Pearce, J.P., Marecki, J., Tambe, M., Ordonez, F., Kraus., S.: Playing games for security: an efficient exact algorithm for solving bayesian stackelberg games. AAMAS 2, 895–902 (2008)Google Scholar
  38. 38.
    Paruchuri, P., Pearce, J.P., Tambe, M., Ordonez, F., Kraus., S.: An efficient heuristic approach for security against multiple adversaries. In: AAMAS (2007)Google Scholar
  39. 39.
    Pita, J., et al.: Deployed armor protection: the application of a game theoretic model for security at the los angeles international airport. In: AAMAS Industry Track, pp. 125–132 (2008)Google Scholar
  40. 40.
    Prelec, D.: A bayesian truth serum for subjective data. Science 306(5695), 462–466 (2004)CrossRefGoogle Scholar
  41. 41.
    Radanovic, G., Faltings, B.: Incentive schemes for participatory sensing. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 1081–1089. International Foundation for Autonomous Agents and Multiagent Systems (2015)Google Scholar
  42. 42.
    Reddy, P.P., Veloso, M.M.: Strategy learning for autonomous agents in smart grid markets. In: IJCAI, pp. 1446–1451 (2011)Google Scholar
  43. 43.
    Riley, B.: Minimum truth serums with optional predictions. In: Proceedings of the 4th Workshop on Social Computing and User Generated Content (SC14) (2014)Google Scholar
  44. 44.
    Ritterman, J., Osborne, M., Klein, E.: Using prediction markets and twitter to predict a swine flu pandemic. In: 1st International Workshop on Mining Social Media, vol. 9, pp. 9–17 (2009)Google Scholar
  45. 45.
    Rosenfeld, A., Maksimov, O., Kraus, S.: Optimizing traffic enforcement: from the lab to the roads. In: Rass, S., An, B., Kiekintveld, C., Fang, F., Schauer, S. (eds.) Decision and Game Theory for Security. GameSec 2017. LNCS, vol. 10575. pp. 3–20. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68711-7_1Google Scholar
  46. 46.
    Rúbio, T.R.P.M., Queiroz, J., Cardoso, H.L., Rocha, A.P., Oliveira, E.: TugaTAC broker: a fuzzy logic adaptive reasoning agent for energy trading. In: Rovatsos, M., Vouros, G., Julian, V. (eds.) EUMAS/AT -2015. LNCS (LNAI), vol. 9571, pp. 188–202. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-33509-4_16CrossRefGoogle Scholar
  47. 47.
    Saleh, M.S., Althaibani, A., Esa, Y., Mhandi, Y., Mohamed., A.A.: Impact of clustering microgrids on their stability and resilience during blackouts. In: ICSGCE, p. 195–200 (2015)Google Scholar
  48. 48.
    Sengupta, S., et al.: A game theoretic approach to strategy generation for moving target defense in web applications. In: AAMAS, pp. 178–186 (2017)Google Scholar
  49. 49.
    Sharma, A.D., Gujar, S., Narahari, Y.: Truthful multi-armed bandit mechanisms for multi-slot sponsored search auctions. Curr. Sci. 103, 1064–1077 (2012)Google Scholar
  50. 50.
    Shieh, E.A., et al.: Protect: an application of computational game theory for the security of the ports of the united states. In: AAAI (2012)Google Scholar
  51. 51.
    da Silva Morais, T.: Survey on frameworks for distributed computing: hadoop, spark and storm. In: 10th Doctoral Symposium in Informatics Engineering-DSIE (2015)Google Scholar
  52. 52.
    Sinha, A., Fang, F., An, B., Kiekintveld, C., Tambe., M.: Stackelberg security games: looking beyond a decade of success. In: IJCAI, pp. 5494–5501 (2018)Google Scholar
  53. 53.
    Sinha, A., Nguyen, T.H., Kar, D., Brown, M., Tambe, M., Jiang., A.X.: From physical security to cybersecurity. J. Cybersecur. 1, 19–35 (2015)Google Scholar
  54. 54.
    Stengel, B.V., Zamir., S.: Leadership with commitment to mixed strategies. Technical Report LSE-CDAM (2004)Google Scholar
  55. 55.
    Tambe., M.: Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned. Cambridge University Press, Cambridge (2011)Google Scholar
  56. 56.
    Tsai, J., Kiekintveld, C., Ordonez, F., Tambe, M., Rathi., S.: Iris-a tool for strategic security allocation in transportation networks. In: AAMAS Industry Track (2009)Google Scholar
  57. 57.
    Urieli, D., Stone, P.: TacTex’13: a champion adaptive power trading agent. In: AAAI, pp. 465–471 (2014)Google Scholar
  58. 58.
    Urieli, D., Stone, P.: Autonomous electricity trading using time-of-use tariffs in a competitive market. In: AAAI (2016)Google Scholar
  59. 59.
    Vorobeychik., Y.: Adversarial ai. IJCAI, pp. 4094–4099 (2016)Google Scholar
  60. 60.
    Witkowski, J., Parkes, D.C.: Peer prediction with private beliefs. In: Proceedings of the 1st Workshop on Social Computing and User Generated Content (SC 2011) (2011)Google Scholar
  61. 61.
    Witkowski, J., Parkes, D.C.: Peer prediction without a common prior. In: Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 964–981. ACM (2012)Google Scholar
  62. 62.
    Xiao.: Impact of clustering microgrids on their stability and resilience during blackouts. In: CSGCE, pp. 195–200 (2015)Google Scholar
  63. 63.
    Yang, Y., Hao, J., Sun, M., Wang, Z., Fan, C., Strbac, G.: Recurrent deep multiagent Q-learning for autonomous brokers in smart grid. In: IJCAI, pp. 569–575 (2018)Google Scholar
  64. 64.
    Zoeter, O.: On a form of advertiser cheating in sponsored search and a dynamic-VCG solution. In: Proceedings of TROA (2008)Google Scholar

Copyright information

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Authors and Affiliations

  1. 1.Machine Learning LabIIIT HyderabadHyderabadIndia

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