Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning

arXiv.org Machine Learning

The game of Chinese Checkers is a challenging traditional board game of perfect information that differs from other traditional games in two main aspects: first, unlike Chess, all checkers remain indefinitely in the game and hence the branching factor of the search tree does not decrease as the game progresses; second, unlike Go, there are also no upper bounds on the depth of the search tree since repetitions and backward movements are allowed. Therefore, even in a restricted game instance, the state-space of the game can still be unbounded, making it challenging for a computer program to excel. In this work, we present an approach that effectively combines the use of heuristics, Monte Carlo tree search, and deep reinforcement learning for building a Chinese Checkers agent without the use of any human game-play data. Experiment results show that our agent is competent under different scenarios and reaches the level of experienced human players.


Reinforcement Renaissance

Communications of the ACM

Based in San Francisco, Marina Krakovsky is the author of The Middleman Economy: How Brokers, Agents, Dealers, and Everyday Matchmakers Create Value and Profit (Palgrave Macmillan, 2015). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and full citation on the first page. Copyright for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or fee. Request permission to publish from permissions@acm.org or fax (212) 869-0481. The Digital Library is published by the Association for Computing Machinery.



AI has beaten us at Go. So what next for humanity?

#artificialintelligence

In the next few days, humanity's ego is likely to take another hit when the world champion of the ancient Chinese game Go is beaten by a computer. Currently Lee Sedol – the Roger Federer of Go – has lost two matches to Google's AlphaGo program in their best-of-five series. If AlphaGo wins just one more of the remaining three matches, humanity will again be vanquished. Back in 1979, the newly crowned world champion of backgammon, Luigi Villa, lost to the BKG 9.8 program seven games to one in a challenge match in Monte Carlo. In 1994, the Chinook program was declared "Man-Machine World Champion" at checkers in a match against the legendary world champion Marion Tinsley after six drawn games.


Towards Strategic Kriegspiel Play with Opponent Modeling

AAAI Conferences

Kriesgpiel, or partially observable chess, is appealing to the AI community due to its similarity to real-world applications in which a decision maker is not a lone agent changing the environment. This paper applies the framework of Interactive POMDPs to design a competent Kriegspiel player. The novel element, compared to the existing approaches, is to model the opponent as a competent player and to predict his likely moves. The moves of our own player can then be computed based on these predictions. The problem is challenging because, first, there are many possible world states the agent has to keep track of.