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Computer Go

Temas  -> Programación  -> Inteligencia Artificial

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Computer Go, el gran problema duro de la inteligencia artificial.


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Strategic Evaluation in Complex Domains (1998)
In some complex domains, like the game of Go, evaluating a position is not simple. In other games, like Chess for example, material balance gives good and fast to compute insight on the value of a position. In Go all the stones have the same value, so material balance is not a good heuristic. To evaluate a Go position, a computer needs a lot of knowledge and much more time.
A small Go board Study of metric and dimensional (2002)
The difficulty to write successful 19x19 go programs lies not only in the combinatorial complexity of go but also in the complexity of designing a good evaluation function containing a lot of knowledge.
Development and Evaluation of Strategic Plans (1997)
At the strategic level, a Go program has to manage uncertainty because of the difficulty to correctly evaluate middle game positions (strength of groups, battles). It has to be cautious not to rely on too many uncertain assumptions, otherwise its opponent will find a weakness in the plan. When faced with multiple choices for achieving a given strategic goal, we provide a method for assessing the least hazardous plan (a plan is a subtree of goals that leads to the success of the root goal).
Global and Local Game Tree Search (2000)
Minimax search has been used with great success to solve a number of games including Gomoku and Nine Men's Morris, and to reach a performance approaching or surpassing the best human players in other well-known games such as checkers, Othello and chess. All these highperformance game-playing programs use global search methods, which evaluate complete game positions. Local search is an alternative approach.
Applying Adversarial Planning Techniques to Go (2001)
Approaches to computer game playing based on alpha-beta search of the tree of possible move sequences combined with a position evaluation function have been successful for many games, notably Chess. Such approaches are less successful for games with large search spaces and complex positions, such as Go, and we are led to seek alternatives.
Metaprogramming domain specific metaprograms (1999)
When a metaprogram automatically creates rules, some created rules are useless because they can never apply. Some metarules, that we call impossibility metarules, are used to remove useless rules.
Multiobjective Heuristic State-Space Planning (2001)
Modern domain-independent heuristic planners evaluate their plans on the basis of their length only. However, in real-world problems there are other criteria that also play an important role, such as resource consumption, profit, safety, etc. This paper extends the GRT planner, an efficient domain-independent heuristic state-space planner, with the ability to consider multiple criteria.
The INDIGO program (1995)
This paper is now to give a ten pages description of this program. In the first part we describe INDIGO. In particular, we show how an interaction model between groups is useful to static evaluation of life and death of not fully cercled groups. Also, we show how fuzzy mathematical morphology is useful to static evaluation of territories. In the second part, before conclusion, we show the results and we discuss about strong and weak points of our approach.
The Zen Way to Go
The more I read from the Zen masters, the more connections I see between its legacy and the goban. Ergo, here is a collection of quotations from the Zen tradition which strike me as being directly relevant to Go.
Pursuing abstract goals in the game of Go (2001)
Reasoning and planning at dierent levels of abstraction is an important skill in the game of Go, for both human and computer players.
Machine Learning, Game Play, and Go (1991)
The game of go is an ideal problem domain for exploring machine learning: it is easy to define and there are many human experts, yet existing programs have failed to emulate their level of play to date.
The Move Decision Strategy of Indigo
This paper describes the move decision strategy of Indigo. By using the example of Indigo, the paper shows that the move decision process of a Go program can be very different from the processes used in other games with lower complexity than the complexity of Go, even if the basic modules are conventional (move generator, evaluation function and tree search). Indigo uses them in a specific way, adapted to computer Go.
Studies in Human and Computer Go: Assessing the Game of Go as a Research Domain for Cognitive Scienc
This thesis assesses the game of Go as a research domain for Cognitive Science by investigating some of the research issues within the domain of Go, and in particular, by assessing Go as a research domain for both Artificial Intelligence and Cognitive Psychology.
On Some Combinatorial Games Connected with Go (1993)
The two-person strategy game of Go has the feature that, with a simple set of rules, situations called kos allowing infinitely long play often arise.
Incremental Updating of Objects in INDIGO (1997)
This paper shows the incremental updating that is used in the Go playing program Indigo. Due to the size of the board and time constraints, incremental mechanisms are relevant to update data. The evaluation of a position includes the construction of a taxonomy of objects which are linked by a lot dependencies.
Automatic Acquisition of Tactical Go Rules (1996)
Gogol is a rule-based computer Go program. It uses a lot of reliable tactical rules. Tactical rules are rules about simple goals such as connecting and making an eye. Gogol uses a simplified game theory to represent the degree of achievement of the goals
Partial Order Bounding: A new Approach to Evaluation in Game Tree Search
In computer game-playing, the established method for constructing an evaluation function uses a scalar value computed as a weighted sum of features. This paper advocates the use of partial order evaluation, and describes an ecient new search method called partial order bounding (POB).
Admissible Moves in Two-player Games (2002)
Some games have abstract properties that can be used to design admissible heuristics on moves. These admissible heuristics are useful to speed up search.
Metarules to Improve Tactical Go Knowledge (2002)
Three main problems arise with automatically generated rules databases. They are too large to fit in memory, they can take a lot of time to generate, and it takes time to match many rules on a board.
Mathematical morphology applied to computer go
This paper shows how mathematical morphological operators can be applied to computer go. On one hand, mathematical morphology is a very powerful tool within image processing community. On the other hand, the Zobrist's model is well-known within the computer go community for its "influence" recognition. We present a model, derived from the closing operator of mathematical morphology and from the Zobrist's model.

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Todos los enlaces

Atari Go
Go Problems
Play Atari-Go
Applet para jugar AtariGo, con código fuente.
An Eye Shape library for Computer Go
The shape library is available as a Java applet that you can play against, and generally use to study tsumego.
Modern Game Insight
It is comprised of a number of systematic structured sgf files.
According to the subject, each file includes a couple of current lines of a certain opening pattern and more interesting variations.
Most of them are taken from actual professional games. Some figures have comments and assessments by players, observers, or authoritative reviewers.
Mail Archivo de Computer Go
An Introduction to the Computer Go Field and Associated Internet Resources
Zobryst Keys
A means of enabling position comparison
Computer Go Programming Papers
List of Links to Papers or Publication
Computer Go en AGA


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