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Benson's Algorithm (Go)
In the game Go, Benson's algorithm (named after David B. Benson) can be used to determine the stones which are safe from capture no matter how many turns in a row the opposing player gets, i.e. ''unconditionally alive''. Algorithm Without loss of generality, we describe Benson's algorithm for the Black player. Let ''X'' be the set of all Black chains and ''R'' be the set of all Black-enclosed regions of ''X''. Then Benson's algorithm requires iteratively applying the following two steps until neither is able to remove any more chains or regions: # Remove from ''X'' all Black chains with less than two vital Black-enclosed regions in ''R'', where a Black-enclosed region is ''vital'' to a Black chain in ''X'' if all its empty intersections are also liberties of the chain. # Remove from ''R'' all Black-enclosed regions with a surrounding stone in a chain not in ''X''. The final set X is the set of all unconditionally alive Black chains. Applicability Most strong Computer Go p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Benson's Algorithm
Benson's algorithm, named after Harold Benson, is a method for solving multi-objective linear programming problems and vector linear programs. This works by finding the "efficient extreme points in the outcome set". The primary concept in Benson's algorithm is to evaluate the upper image of the vector optimization problem by cutting planes. Idea of algorithm Consider a vector linear program :\min_C Px \; \text A x \geq b for P \in \mathbb^, A \in \mathbb^, b \in \mathbb^m and a polyhedral convex ordering cone C having nonempty interior and containing no lines. The feasible set is S=\. In particular, Benson's algorithm finds the extreme points of the set P + C, which is called upper image. In case of C=\mathbb^q_+:=\, one obtains the special case of a multi-objective linear program (multiobjective optimization Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto opti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linear Programming
Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear function#As a polynomial function, linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization). More formally, linear programming is a technique for the mathematical optimization, optimization of a linear objective function, subject to linear equality and linear inequality Constraint (mathematics), constraints. Its feasible region is a convex polytope, which is a set defined as the intersection (mathematics), intersection of finitely many Half-space (geometry), half spaces, each of which is defined by a linear inequality. Its objective function is a real number, real-valued affine function, affine (linear) function defined on this polyhedron. A linear programming algorithm finds a point in the polytope where ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multi-objective Optimization
Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives. For a nontrivial multi-objective optimization problem, no single solutio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Go (game)
Go is an abstract strategy board game for two players in which the aim is to surround more territory than the opponent. The game was invented in China more than 2,500 years ago and is believed to be the oldest board game continuously played to the present day. A 2016 survey by the International Go Federation's 75 member nations found that there are over 46 million people worldwide who know how to play Go and over 20 million current players, the majority of whom live in East Asia. The playing pieces are called stones. One player uses the white stones and the other, black. The players take turns placing the stones on the vacant intersections (''points'') of a board. Once placed on the board, stones may not be moved, but stones are removed from the board if the stone (or group of stones) is surrounded by opposing stones on all orthogonally adjacent points, in which case the stone or group is ''captured''. The game proceeds until neither player wishes to make another move. Wh ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Without Loss Of Generality
''Without loss of generality'' (often abbreviated to WOLOG, WLOG or w.l.o.g.; less commonly stated as ''without any loss of generality'' or ''with no loss of generality'') is a frequently used expression in mathematics. The term is used to indicate the assumption that follows is chosen arbitrarily, narrowing the premise to a particular case, but does not affect the validity of the proof in general. The other cases are sufficiently similar to the one presented that proving them follows by essentially the same logic. As a result, once a proof is given for the particular case, it is trivial to adapt it to prove the conclusion in all other cases. In many scenarios, the use of "without loss of generality" is made possible by the presence of symmetry. For example, if some property ''P''(''x'',''y'') of real numbers is known to be symmetric in ''x'' and ''y'', namely that ''P''(''x'',''y'') is equivalent to ''P''(''y'',''x''), then in proving that ''P''(''x'',''y'') holds for every ''x'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Go
Computer Go is the field of artificial intelligence (AI) dedicated to creating a computer program that plays the traditional board game Go. The field is sharply divided into two eras. Before 2015, the programs of the era were weak. The best efforts of the 1980s and 1990s produced only AIs that could be defeated by beginners, and AIs of the early 2000s were intermediate level at best. Professionals could defeat these programs even given handicaps of 10+ stones in favor of the AI. Many of the algorithms such as alpha-beta minimax that performed well as AIs for checkers and chess fell apart on Go's 19x19 board, as there were too many branching possibilities to consider. Creation of a human professional quality program with the techniques and hardware of the time was out of reach. Some AI researchers speculated that the problem was unsolvable without creation of human-like AI. The application of Monte Carlo tree search to Go algorithms provided a notable improvement in the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Go And Mathematics
The game of Go is one of the most popular games in the world. As a result of its elegant and simple rules, the game has long been an inspiration for mathematical research. Shen Kuo, an 11th century Chinese scholar, estimated in his ''Dream Pool Essays'' that the number of possible board positions is around 10172. In more recent years, research of the game by John H. Conway led to the invention of the surreal numbers and contributed to development of combinatorial game theory (with Go Infinitesimals being a specific example of its use in Go). Computational complexity Generalized Go is played on ''n'' × ''n'' boards, and the computational complexity of determining the winner in a given position of generalized Go depends crucially on the ko rules. Go is “almost” in PSPACE, since in normal play, moves are not reversible, and it is only through capture that there is the possibility of the repeating patterns necessary for a harder complexity. Without ko Without ko, Go is PSPACE ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Go Strategy And Tactics
The game of Go has simple rules that can be learned very quickly but, as with chess and similar board games, complex strategies may be deployed by experienced players. Go opening theory The whole board opening is called Fuseki. An important principle to follow in early play is "corner, side, center." In other words, the corners are the easiest places to take territory, because two sides of the board can be used as boundaries. Once the corners are occupied, the next most valuable points are along the side, aiming to use the edge as a territorial boundary. Capturing territory in the middle, where it must be surrounded on all four sides, is extremely difficult. The same is true for founding a living group: Easiest in the corner, most difficult in the center. The first moves are usually played on or near the 4-4 star points in the corners, because in those places it is easiest to gain territory or influence. (In order to be totally secure alone, a corner stone must be placed on t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
God's Algorithm
God's algorithm is a notion originating in discussions of ways to solve the Rubik's Cube puzzle, but which can also be applied to other combinatorial puzzles and mathematical games. It refers to any algorithm which produces a solution having the fewest possible moves. The allusion to the Deity is based on an assumption that only an omniscient being would know an optimal step from any given configuration. Scope Definition The notion applies to puzzles that can assume a finite number of "configurations", with a relatively small, well-defined arsenal of "moves" that may be applicable to configurations and then lead to a new configuration. Solving the puzzle means to reach a designated "final configuration", a singular configuration, or one of a collection of configurations. To solve the puzzle a sequence of moves is applied, starting from some arbitrary initial configuration. Solution An algorithm can be considered to solve such a puzzle if it takes as input an arbitrary initi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
