Some Practical Techniques for Global Search in Go

Chen, Keh-Hsun

doi:10.3233/icg-2000-23202

Cited by 13 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This heuristic has no effect if there is no group that matches this characteristic. H 11 : Clears the largest set (n ! 2) in Q, provided that clearing any other set does not result in any group in Q 0 larger than the largest in Q (the one we are clearing).…”

Section: Low-level Heuristicsmentioning

confidence: 99%

An educational software tool to teach hyper‐heuristics to engineering students based on the bubble breaker puzzle

Salcedo‐Sanz

Jiménez-Fernández

Matías-Román

et al. 2014

Comp Applic In Engineering

View full text Add to dashboard Cite

This paper presents an educational software tool to teach Artificial Intelligence (AI) techniques, specifically Hyper‐heuristics, to Engineering students. This tool is based on the “Bubble Breaker” puzzle, an addictive game consisting in an M × M matrix of colored bubbles. These balls, when forming sets of two or more same colored balls, can be popped and cleared out. Thus, this puzzle can be solved by setting many different low‐level heuristics and applying a global search procedure (i.e., evolutionary algorithm) that conforms a robust hyper‐heuristic technique. The hyper‐heuristic decides what low‐level heuristics are the best, and the sequential way in which they have to be applied to gain the highest score. This approach has proven an interesting method to teach AI techniques, since simple heuristics, evolutionary algorithms, and its combination are studied in an increasing manner. © 2014 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:277–285, 2015; View this article online at http://wileyonlinelibrary.com/journal/cae; DOI

show abstract

Section: Low-level Heuristicsmentioning

confidence: 99%

An educational software tool to teach hyper‐heuristics to engineering students based on the bubble breaker puzzle

Salcedo‐Sanz

Jiménez-Fernández

Matías-Román

et al. 2014

Comp Applic In Engineering

View full text Add to dashboard Cite

show abstract

“…Following the general trend provided by [Chen 1990[Chen , 2000 or [Mueller 2002], our paper provides the example of the move decision strategy of Indigo [Bouzy 1995a[Bouzy , 1995b[Bouzy , 1999[Bouzy , 2002a. Although [Chen 2001a[Chen , 2001b classifies Indigo into the "global selective search" category, (which was in keeping with our answer), this paper will show that Indigo could also have been classified into the "static analysis" category or into the "incentive/temperature approximation" category as well.…”

Section: Figure 1 the Classical Modelmentioning

confidence: 99%

THE MOVE-DECISION STRATEGY OF Indigo

Bouzy¹

2003

ICG

View full text Add to dashboard Cite

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, which may be of interest for researchers on other mind games as complex as Go. The evaluation function can be "quick", "slow" or "strategic". It may or may not include local tree search. The move generation brings about different kinds of moves : "urgent" moves, "life and death" moves and "calm" moves. Urgent moves are statically qualified with a global urgency. A two-player quiescence search verifies that the urgent move does not decrease the position evaluation. Calm moves are used within two-player selective global search at a very low depth. Besides, Indigo also uses single-agent search to refine the strategic importance of goals. Lastly, Indigo chooses either the calm move, the life and death move or the urgent move to be the global move.

show abstract

“…Global selective search [3][4][5] is an effective approach to deal with the intrinsic difficulties of Go. Many Go programs use this strategy for move selection decisions [7].…”

Section: Introductionmentioning

confidence: 99%