Advances in Computer Games: 16th International Conference, ACG 2019, Macao, China, August 11–13, 2019, Revised Selected Papers

@article{Cazenave2020AdvancesIC,
  title={Advances in Computer Games: 16th International Conference, ACG 2019, Macao, China, August 11–13, 2019, Revised Selected Papers},
  author={Tristan Cazenave and Jaap van den Herik and Abdallah Saffidine and I-Chen Wu and Elisa Bertino},
  journal={Advances in Computer Games},
  year={2020},
  url={https://api.semanticscholar.org/CorpusID:229326379}
}
This paper proposes that “advice” are moves selected by an adviser and proposes a mechanism that makes a player search again when the player’s move is different from advice and demonstrates that game AIs can improve their strength with advice.
View on Springer
link.springer.com

188 References

Automatic Design of Balanced Board Games

This paper describes a first attempt at using AI techniques to design balanced board games like checkers and Go by modifying the rules of the game, not just the rule parameters.

A platform for turn-based strategy games, with a comparison of Monte-Carlo algorithms

This paper analyzes the game components found in most strategy games, and proposes a set of simple rules that could be used as a standard game for research on turn-based strategy games.

UCT Enhancements in Chinese Checkers Using an Endgame Database

This paper assesses the performance of UCT-based AIs and the effectiveness of augmenting them with a lookup table containing evaluations of games states in the game of Chinese Checkers, and serves as an accurate heuristic throughout the game.

Three types of forward pruning techniques to apply the alpha beta algorithm to turn-based strategy games

Three forward-pruning techniques were introduced to enable us to apply alpha beta search (as a minimax search variant) to turn-based strategy games to address Monte-Carlo tree search challenges.

A Gamut of Games

The past successes, current projects, and future research directions for AI using computer games as a research test bed are reviewed.

Best Reply Search for Multiplayer Games

A new algorithm, called best reply search (BRS), for deterministic multiplayer games with perfect information, where only the opponent with the strongest counter move is allowed to make a move.

Monte Carlo tree search based algorithms for dynamic difficulty adjustment

This work proposes four new DDA Artificially Intelligent agents: Reactive Outcome Sensitive Action Selection, Proactive OSAS, and their "True" variants, which provide the player with an level of difficulty tailored to their skill in real-time by altering the action selection policy and the heuristic playout evaluation of Monte Carlo Tree Search.

A Comparison of Algorithms for Multi-player Games

Quantitative results derived from playing max n and the paranoid algorithm against each other on various multi-player game domains are presented, showing that paranoid widely outperforms max n in Chinese checkers, by a lesser amount in Hearts and that they are evenly matched in Spades.

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

This paper generalizes the AlphaZero approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games, and convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.

Playing Hanabi Near-Optimally

This study shows that the game of Hanabi, a multi-player cooperative card game in which a player sees the cards of the other players but not his own cards, can be played near-optimally by the computer.
...