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Agent Based Gameplaying System
Trutman, Michal ; Zbořil, František (referee) ; Král, Jiří (advisor)
This thesis deals with general game playing agent systems. On the contrary with common agents, which are designed only for a specified task or a game, general game playing agents have to be able to play basically any arbitrary game described in a formal declarative language. The biggest challenge is that the game rules are not known beforehand, which makes it impossible to use some optimizations or to make a good heuristic function. The thesis consists of a theoretical and a practical part. The first part introduces the field of general game playing agents, defines the Game Description Language and covers construction of heuristic evaluation functions and their integration within the Monte Carlo tree search algorithm. In the practical part, a general method of creating a new heuristic function is presented, which is later integrated into a proper agent, which is compared then with other systems.
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General Game Playing and Deepstack
Schlindenbuch, Hynek ; Gemrot, Jakub (advisor) ; Majerech, Vladan (referee)
General game playing is an area of artificial intelligence which focuses on creating agents capable of playing many games from some class. The agents receive the rules just before the match and therefore cannot be specialized for each game. Deepstack is the first artificial intelligence to beat professional human players in heads-up no-limit Texas hold'em poker. While it is specialized for poker, at its core is a general algorithm for playing two-player zero-sum games with imperfect information - continual resolving. In this thesis we introduce a general version of continual resolving and compare its performance against Online Outcome Sampling Monte Carlo Counterfactual Regret Minimization in several games.
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Agent Based Gameplaying System
Trutman, Michal ; Zbořil, František (referee) ; Král, Jiří (advisor)
This thesis deals with general game playing agent systems. On the contrary with common agents, which are designed only for a specified task or a game, general game playing agents have to be able to play basically any arbitrary game described in a formal declarative language. The biggest challenge is that the game rules are not known beforehand, which makes it impossible to use some optimizations or to make a good heuristic function. The thesis consists of a theoretical and a practical part. The first part introduces the field of general game playing agents, defines the Game Description Language and covers construction of heuristic evaluation functions and their integration within the Monte Carlo tree search algorithm. In the practical part, a general method of creating a new heuristic function is presented, which is later integrated into a proper agent, which is compared then with other systems.
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