Understanding Sampling Style Adversarial Search Methods
نویسندگان
چکیده
UCT has recently emerged as an exciting new adversarial reasoning technique based on cleverly balancing exploration and exploitation in a Monte-Carlo sampling setting. It has been particularly successful in the game of Go but the reasons for its success are not well understood and attempts to replicate its success in other domains such as Chess have failed. We provide an in-depth analysis of the potential of UCT in domain-independent settings, in cases where heuristic values are available, and the effect of enhancing random playouts to more informed playouts between two weak minimax players. To provide further insights, we develop synthetic game tree instances and discuss interesting properties of UCT, both empirically and analytically.
منابع مشابه
Generative Image Modeling Using Style and Structure Adversarial Networks
Current generative frameworks use end-to-end learning and generate images by sampling from uniform noise distribution. However, these approaches ignore the most basic principle of image formation: images are product of: (a) Structure: the underlying 3D model; (b) Style: the texture mapped onto structure. In this paper, we factorize the image generation process and propose Style and Structure Ge...
متن کاملOn the Behavior of UCT in Synthetic Search Spaces
UCT and Minimax are two of the most prominent tree-search based adversarial reasoning strategies for a variety of challenging domains, such as Chess and Go. Their complementary strengths in different domains have been the motivation for several works attempting to achieve a better understanding of their vastly different behavior. Rather than using complex games as a testbed for deriving indirec...
متن کاملApa Style Manuscript 1 Running Head: Apa Style Manuscript Sampling the Web as Training Data for Text Classification
Data acquisition is a major concern in text classification. The excessive human efforts required by conventional methods to build up quality training collection might not always be available to research workers. In this paper, we look into possibilities to automatically collect training data by sampling the Web with a set of given class names. The basic idea is to populate appropriate keywords ...
متن کاملOn Adversarial Search Spaces and Sampling-Based Planning
Upper Confidence bounds applied to Trees (UCT), a banditbased Monte-Carlo sampling algorithm for planning, has recently been the subject of great interest in adversarial reasoning. UCT has been shown to outperform traditional minimax based approaches in several challenging domains such as Go and Kriegspiel, although minimax search still prevails in other domains such as Chess. This work provide...
متن کاملChinese Typeface Transformation with Hierarchical Adversarial Network
In this paper, we explore automated typeface generation through image style transfer which has shown great promise in natural image generation. Existing style transfer methods for natural images generally assume that the source and target images share similar high-frequency features. However, this assumption is no longer true in typeface transformation. Inspired by the recent advancement in Gen...
متن کامل