A Problem Case for UCT

Browne, Cameron

doi:10.1109/tciaig.2012.2220138

Cited by 6 publications

(12 citation statements)

References 6 publications

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“…According to Ramanujan, Sabharwal, and Selman (2010) this is due to trap states existing in Chess where a Min-Max Alpha-Beta agent is able to identify a shallow trap (a mate in three for example) while MCTS agents fail to estimate such a state correctly. Others have noted similar issues in tactical board positions in Hex and even in some Go board states (Yoshimoto et al, 2006;Browne, 2013). The results of our work are promising in that there may be a way to use MCTS'strategic strength while incorporating the tactical strength of Alpha-Beta to shore up MCTS' weakness in tactical games (cf.…”

Section: Introductionsupporting

confidence: 58%

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Epaminondas: Exploring Combat Tactics

King¹,

Peterson²

2014

ICG

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Epaminondas is a two-person, zero-sum strategy game that combines long-term strategic play with highly tactical move sequences. The game has two unique features that make it stand out from other games. The first feature is the creation of phalanxes, which are groups of pieces that can move as a whole unit. As the number of pieces in a phalanx increases, the mobility and capturing power of the phalanx also increases. The second feature differs from many other strategy games: when a player makes a crossing, a winning move in the game, the second player has an opportunity to respond. This paper presents strategies and heuristics used in a Min-Max Alpha-Beta agent that plays at a novice level. Furthermore, it defines the state-space and game-tree complexities for Epaminondas. Finally, a new version of MCTS is implemented that uses the Alpha-Beta heuristic function during node selection to guide MCTS to more promising areas of the search tree. Additionally, in an effort to overcome the MCTS tactical weakness, the MCTS player implements the Alpha-Beta search once the game reaches 15 turns. Results show that the added heuristic value and the switch to Alpha-Beta for endgame play, positively impact the performance of MCTS, surpassing novice Alpha-Beta win ratios at certain time intervals.

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Section: Introductionsupporting

confidence: 58%

“…The results of our work are promising in that there may be a way to use MCTS'strategic strength while incorporating the tactical strength of Alpha-Beta to shore up MCTS' weakness in tactical games (cf. Browne, 2013;Ramanujan et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Epaminondas: Exploring Combat Tactics

King¹,

Peterson²

2014

ICG

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“…Indeed, they were created by Piet Hein, one of the inventors of Hex and first appeared in the Danish newspaper Politken [14,20] during World War II [13]. 3 The remaining puzzle is a 5 × 5 position proposed by Cameron Browne, it arose during standard play and offers a significant challenge for the Monte Carlo Tree Search (MCTS) algorithm and the associated RAVE enhancement [5]. This is noteworthy because MCTS is the foundational algorithm behind the top artificial players for numerous games including Hex and Go [6].…”

Section: Hexmentioning

confidence: 99%

“…In our experiments, we chose four state-of-the-art QBF solvers, including the top three solvers of the latest QBF Competition 4 and three preprocessors, as indicated in Table 2a. 5 All software was called with default command line parameters. The experiments have been running on a i7-7820X CPU @ 3.60 GHz with 8 cores, 24 GB RAM.…”

Section: Setup Of Experimentsmentioning

confidence: 99%

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Positional Games and QBF: The Corrective Encoding

Mayer-Eichberger

Saffidine

2020

Theory and Applications of Satisfiability Testing – SAT 2020

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Positional games are a mathematical class of two-player games comprising Tic-tac-toe and its generalizations. We propose a novel encoding of these games into Quantified Boolean Formulas (QBFs) such that a game instance admits a winning strategy for first player if and only if the corresponding formula is true. Our approach improves over previous QBF encodings of games in multiple ways. First, it is generic and lets us encode other positional games, such as Hex. Second, structural properties of positional games together with a careful treatment of illegal moves let us generate more compact instances that can be solved faster by state-of-the-art QBF solvers. We establish the latter fact through extensive experiments. Finally, the compactness of our new encoding makes it feasible to translate realistic game problems. We identify a few such problems of historical significance and put them forward to the QBF community as milestones of increasing difficulty.

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Monte Carlo Tree Search: a review of recent modifications and applications

Świechowski¹,

et al. 2022

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Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games. However, in more complex games (e.g. those with a high branching factor or real-time ones) as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey was published in 2012. Contributions that appeared since its release are of particular interest for this review.

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A Problem Case for UCT

Cited by 6 publications

References 6 publications

Epaminondas: Exploring Combat Tactics

Epaminondas: Exploring Combat Tactics

Positional Games and QBF: The Corrective Encoding

Monte Carlo Tree Search: a review of recent modifications and applications

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