Can Deep Blue™ make us happy? Reflections on human and artificial expertise

Sadly, progress in AI has confirmed earlier conclusions, reached using formal domains, about the strict limits of human information processing and has also shown that these limits are only partly remedied by intuition. More positively, AI offers mankind a unique avenue to circumvent its cognitive limits: (1) by acting as a prosthesis extending processing capacity and size of the knowledge base; (2) by offering tools for studying our own cognition; and (3) as a consequence of the previous item, by developing tools that increase the quality and quantity of our own thinking. These ideas are illustrated with chess expertise

Decision-making and strategic thinking through analogies

When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? Consider the chess game: how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud may evolve

On The Foundations of Digital Games

Computers have lead to a revolution in the games we play, and, following this, an interest for computer-based games has been sparked in research communities. However, this easily leads to the perception of a one-way direction of influence between that the field of game research and computer science. This historical investigation points towards a deep and intertwined relationship between research on games and the development of computers, giving a richer picture of both fields. While doing so, an overview of early game research is presented and an argument made that the distinction between digital games and non-digital games may be counter-productive to game research as a whole

Distinguishing humans from computers in the game of go: a complex network approach

We compare complex networks built from the game of go and obtained from databases of human-played games with those obtained from computer-played games. Our investigations show that statistical features of the human-based networks and the computer-based networks differ, and that these differences can be statistically significant on a relatively small number of games using specific estimators. We show that the deterministic or stochastic nature of the computer algorithm playing the game can also be distinguished from these quantities. This can be seen as tool to implement a Turing-like test for go simulators.Comment: 7 pages, 6 figure

Analizador de posiciones del tablero del Go

Throughout humankind's history games have been a defining part of it. For humans, games are a unique phenomenon. They are a challenge established within a defined set of abstract rules, an example of what humans desire: overcoming obstacles and going forward. These challenges are not required for survival, solving and mastering them are its own rewards. A great amount of games have been created during our history. Games are extremely varied, difficult, sophisticated, simple. The world nowadays has a lot of games to offer and one of its main categories is tabletop games. Again, most of them have been created and popularized throughout history. One of the better known games is Go. Go's main feature is being fairly simple in terms of rules which can be explained within a short time. However, it may take a whole life for a normal person to master these rules to their maximum extent. This makes it very interesting and peculiar because it is one of the most difficult games ever created. It is a very complex game given its age, and after 2000 years it is still being studied. In chess, another tabletop game, automatic players are able to play at a grandmaster level by using techniques based on the exhaustive exploration of possible moves, like min-max search with alpha-beta pruning, with the help of carefully designed evaluation functions. This approach is significantly less useful with Go. Go has too many possibilities in terms of movements so the approach taken in Chess gives far less advantage with Go. Go is a complex game for humans to play, but it is even harder to play for computers. Despite its age there are ongoing investigations about Go's computer analysis. No optimal strategy has been found yet for Go. It is one of the games that has not been solved yet and it is considered one of the most difficult to handle. Any effort made in this area is important because of its complexity. Any valuable addition will push further investigation forward. The motivation of this project is to improve our knowledge of the computational analysis of Go. Following the reasoning made before, a Go tool will be developed. This tool will be able to analyze any given Go board using an algorithm known as Monte-Carlo Tree Search. Some playing agents used in Go competitions use this algorithm. However these agents use MCTS only to play, so this project will take a different take on this algorithm. MCTS will be used to retrieve information about influence and other different features of a Go board. Furthermore, the tool developed will be used in order to further analyze information retrieved with machine learning techniques.Ingeniería Informátic

Depth, balancing, and limits of the Elo model

-Much work has been devoted to the computational complexity of games. However, they are not necessarily relevant for estimating the complexity in human terms. Therefore, human-centered measures have been proposed, e.g. the depth. This paper discusses the depth of various games, extends it to a continuous measure. We provide new depth results and present tool (given-first-move, pie rule, size extension) for increasing it. We also use these measures for analyzing games and opening moves in Y, NoGo, Killall Go, and the effect of pie rules

Sequencing Chess

We analyze the structure of the state space of chess by means of transition path sampling Monte Carlo simulation. Based on the typical number of moves required to transpose a given configuration of chess pieces into another, we conclude that the state space consists of several pockets between which transitions are rare. Skilled players explore an even smaller subset of positions that populate some of these pockets only very sparsely. These results suggest that the usual measures to estimate both, the size of the state space and the size of the tree of legal moves, are not unique indicators of the complexity of the game, but that topological considerations are equally important