A Dynamical Systems Approach for Static Evaluation in Go

In the paper arguments are given why the concept of static evaluation has the potential to be a useful extension to Monte Carlo tree search. A new concept of modeling static evaluation through a dynamical system is introduced and strengths and weaknesses are discussed. The general suitability of this approach is demonstrated.Comment: IEEE Transactions on Computational Intelligence and AI in Games, vol 3 (2011), no

I Am Error

I Am Error is a platform study of the Nintendo Family Computer (or Famicom), a videogame console first released in Japan in July 1983 and later exported to the rest of the world as the Nintendo Entertainment System (or NES). The book investigates the underlying computational architecture of the console and its effects on the creative works (e.g. videogames) produced for the platform. I Am Error advances the concept of platform as a shifting configuration of hardware and software that extends even beyond its ‘native’ material construction. The book provides a deep technical understanding of how the platform was programmed and engineered, from code to silicon, including the design decisions that shaped both the expressive capabilities of the machine and the perception of videogames in general. The book also considers the platform beyond the console proper, including cartridges, controllers, peripherals, packaging, marketing, licensing, and play environments. Likewise, it analyzes the NES’s extension and afterlife in emulation and hacking, birthing new genres of creative expression such as ROM hacks and tool-assisted speed runs. I Am Error considers videogames and their platforms to be important objects of cultural expression, alongside cinema, dance, painting, theater and other media. It joins the discussion taking place in similar burgeoning disciplines—code studies, game studies, computational theory—that engage digital media with critical rigor and descriptive depth. But platform studies is not simply a technical discussion—it also keeps a keen eye on the cultural, social, and economic forces that influence videogames. No platform exists in a vacuum: circuits, code, and console alike are shaped by the currents of history, politics, economics, and culture—just as those currents are shaped in kind

Bandit algorithms for searching large spaces

Bandit games consist of single-state environments in which an agent must sequentially choose actions to take, for which rewards are given. The objective being to maximise the cumulated reward, the agent naturally seeks to build a model of the relationship between actions and rewards. The agent must both choose uncertain actions in order to improve its model (exploration), and actions that are believed to yield high rewards according to the model (exploitation). The choice of an action to take is called a play of an arm of the bandit, and the total number of plays may or may not be known in advance. Algorithms designed to handle the exploration-exploitation dilemma were initially motivated by problems with rather small numbers of actions. But the ideas they were based on have been extended to cases where the number of actions to choose from is much larger than the maximum possible number of plays. Several problems fall into this setting, such as information retrieval with relevance feedback, where the system must learn what a user is looking for while serving relevant documents often enough, but also global optimisation, where the search for an optimum is done by selecting where to acquire potentially expensive samples of a target function. All have in common the search of large spaces. In this thesis, we focus on an algorithm based on the Gaussian Processes probabilistic model, often used in Bayesian optimisation, and the Upper Confidence Bound action-selection heuristic that is popular in bandit algorithms. In addition to demonstrating the advantages of the GP-UCB algorithm on an image retrieval problem, we show how it can be adapted in order to search tree-structured spaces. We provide an efficient implementation, theoretical guarantees on the algorithm's performance, and empirical evidence that it handles large branching factors better than previous bandit-based algorithms, on synthetic trees

Modelling Uncertainty in the Game of Go

Go is an ancient oriental game whose complexity has defeated attempts to automate it. We suggest using probability in a Bayesian sense to model the uncertainty arising from the vast complexity of the game tree. We present a simple conditional Markov random field model for predicting the pointwise territory outcome of a game. The topology of the model reflects the spatial structure of the Go board. We describe a version of the Swendsen-Wang process for sampling from the model during learning and apply loopy belief propagation for rapid inference and prediction. The model is trained on several hundred records of professional games. Our experimental results indicate that the model successfully learns to predict territory despite its simplicity