34,011 research outputs found
Agent Based Gameplaying System
Tato práce se zabývá universálními agentními systémy pro hraní her. Oproti běžným agentům, kteří jsou určeni pouze pro určitý druh činnosti nebo konkrétní hru, universální agent musí být schopen hrát prakticky libovolnou hru popsanou ve formálním deklarativním jazyce. Výzvou je především to, že pravidla hry nejsou předem známa, což znemožňuje použití některých optimalizací nebo vytvoření dobré heuristické funkce. Práce je rozdělena na teoretickou a praktickou část. První část představuje oblast univerzálních herních agentů, definuje jazyk GDL pro popis pravidel her a zabývá se vytvářením heuristických funkcí a jejich aplikací v algoritmu Monte Carlo stromové hledání. V praktické části je představen obecný způsob, jak vytvořit novou heuristickou funkci, která je poté integrována do vlastního herního agenta a ten je pak porovnán s dalšími existujícími systémy.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.
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
Shallow decision-making analysis in General Video Game Playing
The General Video Game AI competitions have been the testing ground for
several techniques for game playing, such as evolutionary computation
techniques, tree search algorithms, hyper heuristic based or knowledge based
algorithms. So far the metrics used to evaluate the performance of agents have
been win ratio, game score and length of games. In this paper we provide a
wider set of metrics and a comparison method for evaluating and comparing
agents. The metrics and the comparison method give shallow introspection into
the agent's decision making process and they can be applied to any agent
regardless of its algorithmic nature. In this work, the metrics and the
comparison method are used to measure the impact of the terms that compose a
tree policy of an MCTS based agent, comparing with several baseline agents. The
results clearly show how promising such general approach is and how it can be
useful to understand the behaviour of an AI agent, in particular, how the
comparison with baseline agents can help understanding the shape of the agent
decision landscape. The presented metrics and comparison method represent a
step toward to more descriptive ways of logging and analysing agent's
behaviours
Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups
Monte Carlo Tree Search (MCTS) has improved the performance of game engines
in domains such as Go, Hex, and general game playing. MCTS has been shown to
outperform classic alpha-beta search in games where good heuristic evaluations
are difficult to obtain. In recent years, combining ideas from traditional
minimax search in MCTS has been shown to be advantageous in some domains, such
as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new
way to use heuristic evaluations to guide the MCTS search by storing the two
sources of information, estimated win rates and heuristic evaluations,
separately. Rather than using the heuristic evaluations to replace the
playouts, our technique backs them up implicitly during the MCTS simulations.
These minimax values are then used to guide future simulations. We show that
using implicit minimax backups leads to stronger play performance in Kalah,
Breakthrough, and Lines of Action.Comment: 24 pages, 7 figures, 9 tables, expanded version of paper presented at
IEEE Conference on Computational Intelligence and Games (CIG) 2014 conferenc
- …