Abstract
Task delegation lies at the heart of the service economy, and is a fundamental aspect of many agent marketplaces. Research in computational trust considers which agent a task should be delegated to for execution given the agent’s past behaviour. However, such work does not consider the effects of the agent delegating the task onwards, forming a chain of delegations before the task is finally executed (as occurs in many human outsourcing scenarios). In this paper we consider such delegation chains, and empirically demonstrate that existing trust based approaches do not handle these situations as well. We then introduce a new algorithm based on quitting games to cater for recursive delegation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, S., Goyal, N.: Analysis of Thompson sampling for the multi-armed bandit problem. In: Conference on Learning Theory, pp. 39–1 (2012)
Auer, P., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47, 235–256 (2002)
Brezzi, M., Lai, T.L.: Optimal learning and experimentation in bandit problems. J. Econ. Dyn. Control. 27(1), 87–108 (2002)
Burnett, C., Oren, N.: Sub-delegation and trust. In: AAMAS, pp. 1359–1360. IFAAMAS (2012)
Chapelle, O., Li, L.: An empirical evaluation of Thompson sampling. In: Advances in Neural Information Processing Systems, pp. 2249–2257 (2011)
Franke, S., Mehlitz, P., Pilecka, M.: Optimality conditions for the simple convex bilevel programming problem in banach spaces. Optimization 67(2), 237–268 (2018)
Gittins, J., Glazebrook, K., Weber, R.: Multi-Armed Bandit Allocation Indices. Wiley, Hoboken (2011)
Gutin, E., Farias, V.: Optimistic Gittins indices. In: Advances in Neural Information Processing Systems, pp. 3153–3161 (2016)
He, X., Zhou, Y., Chen, Z.: Evolutionary bilevel optimization based on covariance matrix adaptation. IEEE Trans. Evol. Comput. (2018)
Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58(301), 13–30 (1963)
Koulouriotis, D.E., Xanthopoulos, A.: Reinforcement learning and evolutionary algorithms for non-stationary multi-armed bandit problems. Appl. Math. Comput. 196(2), 913–922 (2008)
Kulkarni, T.D., Narasimhan, K., Saeedi, A., Tenenbaum, J.: Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. In: Advances in Neural Information Processing Systems, pp. 3675–3683 (2016)
Sen, S., Ridgway, A., Ripley, M.: Adaptive budgeted bandit algorithms for trust development in a supply-chain. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, pp. 137–144. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2015). http://dl.acm.org/citation.cfm?id=2772879.2772900
Skibski, O., Michalak, T.P., Rahwan, T., Wooldridge, M.: Algorithms for the shapley and myerson values in graph-restricted games. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 197–204. International Foundation for Autonomous Agents and Multiagent Systems (2014)
Solan, E., Vieille, N.: Quitting games. Math. Oper. Res. 26(2), 265–285 (2001)
Solan, E., Vieille, N.: Quitting games-an example. Int. J. Game Theory 31(3), 365–381 (2003)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2011)
Vezhnevets, A.S., et al.: Feudal networks for hierarchical reinforcement learning. arXiv preprint arXiv:1703.01161 (2017)
Welch, P.D.: The statistical analysis of simulation results. In: The Computer Performance Modeling Handbook, vol. 22, pp. 268–328 (1983)
Zhang, H., Zenios, S.: A dynamic principal-agent model with hidden information: sequential optimality through truthful state revelation. Oper. Res. 56(3), 681–696 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Afanador, J., Baptista, M., Oren, N. (2019). An Adversarial Algorithm for Delegation. In: Lujak, M. (eds) Agreement Technologies. AT 2018. Lecture Notes in Computer Science(), vol 11327. Springer, Cham. https://doi.org/10.1007/978-3-030-17294-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-17294-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17293-0
Online ISBN: 978-3-030-17294-7
eBook Packages: Computer ScienceComputer Science (R0)