A Better-response Strategy for Self-interested Planning Agents

[EN] When self-interested agents plan individually, interactions that prevent them from executing their actions as planned may arise. In these coordination problems, game-theoretic planning can be used to enhance the agents¿ strategic behavior considering the interactions as part of the agents¿ utility. In this work, we define a general-sum game in which interactions such as conflicts and congestions are reflected in the agents¿ utility. We propose a better-response planning strategy that guarantees convergence to an equilibrium joint plan by imposing a tax to agents involved in conflicts. We apply our approach to a real-world problem in which agents are Electric Autonomous Vehicles (EAVs). The EAVs intend to find a joint plan that ensures their individual goals are achievable in a transportation scenario where congestion and conflicting situations may arise. Although the task is computationally hard, as we theoretically prove, the experimental results show that our approach outperforms similar approaches in both performance and solution quality.This work is supported by the GLASS project TIN2014-55637-C2-2-R of the Spanish MINECO and the Prometeo project II/2013/019 funded by the Valencian Government.Jordán, J.; Torreño Lerma, A.; De Weerdt, M.; Onaindia De La Rivaherrera, E. (2018). A Better-response Strategy for Self-interested Planning Agents. Applied Intelligence. 48(4):1020-1040. https://doi.org/10.1007/s10489-017-1046-5S10201040484Aghighi M, Bäckström C (2016) A multi-parameter complexity analysis of cost-optimal and net-benefit planning. In: Proceedings of the Twenty-Sixth International Conference on International Conference on Automated Planning and Scheduling. AAAI Press, London, pp 2–10Bercher P, Mattmüller R (2008) A planning graph heuristic for forward-chaining adversarial planning. In: ECAI, vol 8, pp 921–922Brafman RI, Domshlak C, Engel Y, Tennenholtz M (2009) Planning games. In: IJCAI 2009, Proceedings of the 21st international joint conference on artificial intelligence, pp 73–78Bylander T (1994) The computational complexity of propositional strips planning. Artif Intell 69(1):165–204Chen X, Deng X (2006) Settling the complexity of two-player nash equilibrium. In: 47th annual IEEE symposium on foundations of computer science, 2006. FOCS’06. IEEE, pp 261–272Chien S, Sinclair A (2011) Convergence to approximate nash equilibria in congestion games. Games and Economic Behavior 71(2):315–327de Cote EM, Chapman A, Sykulski AM, Jennings N (2010) Automated planning in repeated adversarial games. In: 26th conference on uncertainty in artificial intelligence (UAI 2010), pp 376–383Dunne PE, Kraus S, Manisterski E, Wooldridge M (2010) Solving coalitional resource games. Artif Intell 174(1):20–50Fabrikant A, Papadimitriou C, Talwar K (2004) The complexity of pure nash equilibria. In: Proceedings of the thirty-sixth annual ACM symposium on theory of computing, STOC ’04, pp 604–612Friedman JW, Mezzetti C (2001) Learning in games by random sampling. J Econ Theory 98(1):55–84Ghallab M, Nau D, Traverso P (2004) Automated planning: theory & practice. ElsevierGoemans M, Mirrokni V, Vetta A (2005) Sink equilibria and convergence. In: Proceedings of the 46th annual IEEE symposium on foundations of computer science, FOCS ’05, pp 142–154Hadad M, Kraus S, Hartman IBA, Rosenfeld A (2013) Group planning with time constraints. Ann Math Artif Intell 69(3):243–291Hart S, Mansour Y (2010) How long to equilibrium? the communication complexity of uncoupled equilibrium procedures. Games and Economic Behavior 69(1):107–126Helmert M (2003) Complexity results for standard benchmark domains in planning. Artif Intell 143(2):219–262Helmert M (2006) The fast downward planning system. J Artif Intell Res 26(1):191–246Jennings N, Faratin P, Lomuscio A, Parsons S, Wooldrige M, Sierra C (2001) Automated negotiation: prospects, methods and challenges. Group Decis Negot 10(2):199–215Johnson DS, Papadimtriou CH, Yannakakis M (1988) How easy is local search? J Comput Syst Sci 37 (1):79–100Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling, ICAPSJordán J, Onaindía E (2015) Game-theoretic approach for non-cooperative planning. In: 29th AAAI conference on artificial intelligence (AAAI-15), pp 1357–1363McDermott D, Ghallab M, Howe A, Knoblock C, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL: the planning domain definition language. Yale Center for Computational Vision and Control, New HavenMilchtaich I (1996) Congestion games with player-specific payoff functions. Games and Economic Behavior 13(1):111–124Monderer D, Shapley LS (1996) Potential games. Games and Economic Behavior 14(1):124–143Nigro N, Welch D, Peace J (2015) Strategic planning to implement publicly available ev charching stations: a guide for business and policy makers. Tech rep, Center for Climate and Energy SolutionsNisan N, Ronen A (2007) Computationally feasible vcg mechanisms. J Artif Intell Res 29(1):19–47Nisan N, Roughgarden T, Tardos E, Vazirani VV (2007) Algorithmic game theory. Cambridge University Press, New YorkPapadimitriou CH (1994) On the complexity of the parity argument and other inefficient proofs of existence. J Comput Syst Sci 48(3):498–532Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177Rosenthal RW (1973) A class of games possessing pure-strategy nash equilibria. Int J Game Theory 2(1):65–67Shoham Y, Leyton-Brown K (2009) Multiagent systems: algorithmic, game-theoretic, and logical foundations. Cambridge University PressTorreño A, Onaindia E, Sapena Ó (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178Torreño A, Onaindia E, Sapena Ó (2014) FMAP: distributed cooperative multi-agent planning. Appl Intell 41(2):606– 626Torreño A, Sapena Ó, Onaindia E (2015) Global heuristics for distributed cooperative multi-agent planning. In: ICAPS 2015. 25th international conference on automated planning and scheduling. AAAI Press, pp 225–233Von Neumann J, Morgenstern O (2007) Theory of games and economic behavior. Princeton University Pressde Weerdt M, Bos A, Tonino H, Witteveen C (2003) A resource logic for multi-agent plan merging. Ann Math Artif Intell 37(1):93–130Wooldridge M, Endriss U, Kraus S, Lang J (2013) Incentive engineering for boolean games. Artif Intell 195:418–43

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates

The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. The resulting algorithm configuration (AC) problem has attracted much attention from the machine learning community. However, the proper evaluation of new AC procedures is hindered by two key hurdles. First, AC benchmarks are hard to set up. Second and even more significantly, they are computationally expensive: a single run of an AC procedure involves many costly runs of the target algorithm whose performance is to be optimized in a given AC benchmark scenario. One common workaround is to optimize cheap-to-evaluate artificial benchmark functions (e.g., Branin) instead of actual algorithms; however, these have different properties than realistic AC problems. Here, we propose an alternative benchmarking approach that is similarly cheap to evaluate but much closer to the original AC problem: replacing expensive benchmarks by surrogate benchmarks constructed from AC benchmarks. These surrogate benchmarks approximate the response surface corresponding to true target algorithm performance using a regression model, and the original and surrogate benchmark share the same (hyper-)parameter space. In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures. We show that our surrogate benchmarks capture overall important characteristics of the AC scenarios, such as high- and low-performing regions, from which they were derived, while being much easier to use and orders of magnitude cheaper to evaluate