18 results with keyword: 'exploiting game decompositions in monte carlo tree search'
The new MT-MCTS approach opens different research tracks: the develop- ment of a selection policy efficient for the different types of compound games, the support of the specific
N/A
Figure 2: The Total Number of Employees in the Insurance Sector in Turkey, 2001-2009 Figure 3: Turkey Market Size by Value of Life and Non-Life Insurance Market in USD Million, on
N/A
Using this simulation strategy the MCTS program plays at the same level as the αβ program MIA, the best LOA playing entity in the world.. 3.5.3 Deterministic
N/A
In this article we introduce a new MCTS variant, called MCTS-Solver, which has been designed to prove the game-theoretical value of a node in a search tree.. This is an important
N/A
Key words: artificial intelligence (AI), search, planning, machine learning, Monte Carlo tree search (MCTS), reinforcement learning, temporal-difference (TD) learning, upper
N/A
Monte Carlo tree search (MCTS) [1] over the years has become one of the well known algorithms used in game playing.. This algorithm has shown its strength by playing games with
N/A
Naming: when the lane of player Black contains k+1 squares (inclduing starting square and goal), and the lane of player White has m+1 squares, we call the game DSR--k-vs-m, which
N/A
The effects of strategy fusion can manifest in different ways. First, strategy fusion may arise since a deterministic solver may make different decisions in each of the states within
N/A
The main contribution of the paper is the Mosaic AutoML platform, adapting and extend- ing the Monte-Carlo Tree Search setting to tackle the structured optimization problem of
N/A
As the number of MCTS iterations in- creases, the memory usage of the algorithm is bounded only by the (combinatorially large) size of the game tree.. Sev- eral methods have
N/A
Monte Carlo tree search (MCTS) is a probabilistic algorithm that uses lightweight random simulations to selectively grow a game tree.. MCTS has experienced a lot of success in do-
N/A
The study was guided by the following research objectives; To analyze the effect customer based Innovations on operational performance, To assess how Market
N/A
20.01.2015 | Fachbereich Informatik | DKE: Seminar zu maschinellem Lernen | Robert Pinsler | 7!. Finding an
N/A
Monte-Carlo Tree Search is a best-first search algorithm that relies on random simulations to estimate position values. MCTS collects the results of these random simulations in a
N/A
AE: Adverse event; AIS: Abbreviated injury severity score; ANZIC-RC: Australian and New Zealand Intensive Care Research Centre; APACHE II: Acute Physiology and Chronic Health
N/A
Neumann and in monte carlo method lecture notes taken by simulation involves placement of tree search program play a square.. Asymptotic analysis technique used in monte carlo
N/A
We improve the performance of our player in the early game with an opening book computed through self play.. To assess the performance of our heuristics, we have performed a number
N/A