IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, VOL. 4, NO. 1, MARCHA Survey of Monte Carlo Tree Search Methods Cameron Browne, Member, IEEE, Edward Powley, Member, IEEE, Daniel Whitehouse, Memb

First Page		Document Content
Date: 2012-06-11 20:40:44 Graph theory Search algorithms Game theory Computer chess Combinatorial game theory Minimax Reinforcement learning Expectiminimax tree Negamax Mathematics Game artificial intelligence Artificial intelligence		IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, VOL. 4, NO. 1, MARCHA Survey of Monte Carlo Tree Search Methods Cameron Browne, Member, IEEE, Edward Powley, Member, IEEE, Daniel Whitehouse, Memb Add to Reading List Source URL: www.doc.ic.ac.uk Download Document from Source Website File Size: 2,35 MB Share Document on Facebook

	Minimax Differential Dynamic Programming: An Application to Robust Biped Walking Jun Morimoto Human Information Science Labs, Department 3, ATR International DocID: 1vqMk - View Document
	2015 European Control Conference (ECC) July 15-17, 2015. Linz, Austria Approximate Closed-Loop Minimax Model Predictive Operation Control of Microgrids Christian A. Hans, Vladislav Nenchev, J¨org Raisch and Carsten Rein DocID: 1uMON - View Document
	Minimax Theory and its Applications Volume), No. 2, 265–283 Bounded Solutions to Nonlinear Problems in RN Involving the Fractional Laplacian Depending on Parameters DocID: 1unZ8 - View Document
	Online Convex Optimization: From Gambling to Minimax Theorems by Playing Repeated Games Tim van Erven DocID: 1u2Qx - View Document
	CS364A: Algorithmic Game Theory Lecture #18: From External Regret to Swap Regret and the Minimax Theorem∗ Tim Roughgarden† November 20, 2013 DocID: 1tNkR - View Document