T. Baarslag, Exploring the Strategy Space of Negotiating Agents: A Framework for Bidding, Learning and Accepting in Automated Negotiation, 2016.

T. Baarslag, J. Mark, . Hendrikx, V. Koen, and C. Hindriks, Learning

T. Baarslag and K. V. Hindriks, Accepting optimally in automated negotiation with incomplete information, AAMAS '13, pp.715-722, 2013.

T. Baarslag, V. Koen, C. Hindriks, and . Jonker, A tit for tat negotiation strategy for realtime bilateral negotiation, Complex Automated Negotiations: Theories, Models, and Software Competitions, vol.435, pp.229-233

C. Cameron, E. Browne, D. Powley, S. M. Whitehouse, . Lucas et al., A survey of Monte Carlo tree search methods, IEEE Transactions on Computational Intelligence and AI in games, vol.4, issue.1, pp.1-43, 2012.

A. Couëtoux, Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems, 2013.

F. Fang, Y. Xin, Y. Xia, and X. Haitao, An opponent's negotiation behavior model to facilitate buyer-seller negotiations in supply chain management, 2008 International Symposium on Electronic Commerce and Security, 2008.

P. Faratin, R. Nicholas, C. Jennings, and . Sierra, Negotiation decision functions for autonomous agents, Robotics and Autonomous Systems, vol.24, issue.3-4, pp.159-182, 1998.

H. Finnsson, Generalized Monte Carlo tree search extensions for general game playing, Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI'12, pp.1550-1556, 2012.

N. Fukuta, T. Ito, and M. Zhang, Katsuhide Fujita, and Valentin Robu, editors. Recent Advances in Agent-based Complex Automated Negotiation, Studies in Computational Intelligence, vol.638, 2016.

S. Gelly and D. Silver, Monte-carlo tree search and rapid action value estimation in computer go, Artificial Intelligence, vol.175, issue.11, pp.1856-1875, 2011.

A. G. Robert-h-guttman, P. Moukas, and . Maes, Agent-mediated electronic commerce: a survey, The Knowledge Engineering Review, vol.13, issue.02, pp.147-159, 1998.

P. David, A. Helmbold, and . Parker-wood, All-moves-as-first heuristics in monte-carlo go, pp.605-610, 2009.

K. Hindriks and D. Tykhonov, Opponent modelling in automated multi-issue negotiation using bayesian learning, Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol.1, pp.331-338, 2008.

L. Kocsis and C. Szepesvári, Bandit based Monte Carlo planning, Machine Learning: ECML 2006: 17th European Conference on Machine Learning, pp.282-293, 2006.

R. Lin, S. Kraus, T. Baarslag, D. Tykhonov, K. Hindriks et al., Genius: an integrated environment for supporting the design of generic automated negotiators, Computational Intelligence, vol.30, issue.1, pp.48-70, 2014.

F. John and . Nash, The bargaining problem, Econometrica: Journal of the Econometric Society, pp.155-162, 1950.

E. Carl, C. Rasmussen, and . Williams, Gaussian processes for machine learning, 2006.

A. Rubinstein, Perfect equilibrium in a bargaining model, Econometrica: Journal of the Econometric Society, pp.97-109, 1982.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre et al., Mastering the game of go with deep neural networks and tree search, Nature, vol.529, issue.7587, pp.484-489, 2016.

R. Colin, V. Williams, . Robu, H. Enrico, N. Gerding et al., Using gaussian processes to optimise concession in complex negotiations against unknown opponents, IJ-CAI'11, pp.432-438, 2011.