A Hybrid Approach for the Fighting Game AI Challenge: Balancing Case Analysis and Monte Carlo Tree Search for the Ultimate Performance in Unknown Environment

We attempt to produce a game-winning heuristic for the mathematically incomplete game Ultimate Tic Tac Toe (UTT). There are several game AI that use Monte Carlo Tree Search to decide moves, however, heuristics offer a faster and computationally cheaper alternative. The mathematical analysis of UTT has not been actively pursued, so we attempt to prove a posteriori. We have decided on a few strategies for playing, and assign different strategies to each player. We play several automated games of UTT, and statistically analyse which games end quickest, and use that data to find optimal strategies for playing. This can be used to produce game heuristics for more complicated games, and produce insight about strategies. The first objective is to specify a framework that can compare heuristics for UTT, and decide an optimal strategy for both players. The second objective is to test the framework with a large amount of data, and produce demonstrable results for UTT. Lastly, to aid further research in this topic, we release our dataset into the public domain.

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Monte Carlo Tree Search as an intelligent search tool in structural design problems

Engineering With Computers ◽

10.1007/s00366-021-01338-2 ◽

2021 ◽

Author(s):

Leonardo Rossi ◽

Mark H. M. Winands ◽

Christoph Butenweg

Keyword(s):

Monte Carlo ◽

Structural Engineering ◽

Single Agent ◽

Hybrid Approach ◽

Tree Search ◽

Technical Problems ◽

Monte Carlo Tree Search ◽

Intelligent Search ◽

Search Tool ◽

World Champion

AbstractMonte Carlo Tree Search (MCTS) is a search technique that in the last decade emerged as a major breakthrough for Artificial Intelligence applications regarding board- and video-games. In 2016, AlphaGo, an MCTS-based software agent, outperformed the human world champion of the board game Go. This game was for long considered almost infeasible for machines, due to its immense search space and the need for a long-term strategy. Since this historical success, MCTS is considered as an effective new approach for many other scientific and technical problems. Interestingly, civil structural engineering, as a discipline, offers many tasks whose solution may benefit from intelligent search and in particular from adopting MCTS as a search tool. In this work, we show how MCTS can be adapted to search for suitable solutions of a structural engineering design problem. The problem consists of choosing the load-bearing elements in a reference reinforced concrete structure, so to achieve a set of specific dynamic characteristics. In the paper, we report the results obtained by applying both a plain and a hybrid version of single-agent MCTS. The hybrid approach consists of an integration of both MCTS and classic Genetic Algorithm (GA), the latter also serving as a term of comparison for the results. The study’s outcomes may open new perspectives for the adoption of MCTS as a design tool for civil engineers.

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