1,989 research outputs found
Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups
Monte Carlo Tree Search (MCTS) has improved the performance of game engines
in domains such as Go, Hex, and general game playing. MCTS has been shown to
outperform classic alpha-beta search in games where good heuristic evaluations
are difficult to obtain. In recent years, combining ideas from traditional
minimax search in MCTS has been shown to be advantageous in some domains, such
as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new
way to use heuristic evaluations to guide the MCTS search by storing the two
sources of information, estimated win rates and heuristic evaluations,
separately. Rather than using the heuristic evaluations to replace the
playouts, our technique backs them up implicitly during the MCTS simulations.
These minimax values are then used to guide future simulations. We show that
using implicit minimax backups leads to stronger play performance in Kalah,
Breakthrough, and Lines of Action.Comment: 24 pages, 7 figures, 9 tables, expanded version of paper presented at
IEEE Conference on Computational Intelligence and Games (CIG) 2014 conferenc
Project management â the contrast between classical and modern approaches
A project is a discrete effort to compress a set of activities to achieve a specific scope for obtaining a final product or service. The actual experience shows that using modern management of projects lead to a better control over resources and better relations customer. Subject of this paper it is "Project Management – The contrast between classical and modern approaches”. Choosing the theme of this article is not random, it continuing series of articles published for strengthen of scientific research in the Doctorate studies that I followed since 2005
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Insightful artificial intelligence
Funder: Leverhulme Trust; Id: http://dx.doi.org/10.13039/501100000275In March 2016, DeepMind's computer programme AlphaGo surprised the world by defeating the worldâchampion Go player, Lee Sedol. AlphaGo exhibits a novel, surprising and valuable style of play and has been recognised as âcreativeâ by the artificial intelligence (AI) and Go communities. This article examines whether AlphaGo engages in creative problem solving according to the standards of comparative psychology. I argue that AlphaGo displays one important aspect of creative problem solving (namely mental scenario building in the form of Monte Carlo tree search), while lacking another (domain generality). This analysis has consequences for how we think about creativity in humans and AI
Numerical methods for the determination of the properties and critical behaviour of percolation and the Ising model
For this thesis, numerical methods have been developed, based on Monte Carlo methods, which allow for investigating percolation and the Ising model with high precision. Emphasis is on methods to use modern parallel computers with high efficiency. Two basic approaches for parallelization were chosen: replication and domain decomposition, in conjunction with suitable algorithms. For percolation, the Hoshen-Kopelman algorithm for cluster counting was adapted to different needs. For studying fluctuations of cluster numbers, its traditional version (i. e., which is already published in literature) was used with simple replication. For simulating huge lattices, the Hoshen-Kopelman algorithm was adapted to domain decomposition, by dividing the hyperplane of investigation into strips that were assigned to different processors. By using this way of domain decomposition, it is viable to simulate huge lattices (with world record sizes) even for dimensions d greater than 2 on massively-parallel computers with distributed memory and message passing. For studying properties of percolation in dependence of system size, the Hoshen-Kopelman algorithm was modified to work on changing domains, i. e., growing lattices. By using this method, it is possible to simulate a lattice of linear size Lmax and investigate lattices of size L less than Lmax for free. Here again, replication is a viable parallelization strategy. For the Ising model, the standard Monte Carlo method of importance sampling with Glauber kinetics and multi-spin coding is adapted to parallel computers by domain decomposition of the lattice into strips. Using this parallelization method, it is possible to use massively-parallel computers with distributed memory and message passing in order to study huge lattices (again world record sizes) over many Monte Carlo steps, in order to investigate the dynamical critical behaviour in two dimensions
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