By Topic

Computational Intelligence and AI in Games, IEEE Transactions on

Issue 2 • Date June 2012

Filter Results

Displaying Results 1 - 10 of 10
  • Table of contents

    Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (164 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Computational Intelligence and AI in Games publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (38 KB)  
    Freely Available from IEEE
  • N-Grams and the Last-Good-Reply Policy Applied in General Game Playing

    Page(s): 73 - 83
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1388 KB) |  | HTML iconHTML  

    The aim of general game playing (GGP) is to create programs capable of playing a wide range of different games at an expert level, given only the rules of the game. The most successful GGP programs currently employ simulation-based Monte Carlo tree search (MCTS). The performance of MCTS depends heavily on the simulation strategy used. In this paper, we introduce improved simulation strategies for GGP that we implement and test in the GGP agent CADIAPLAYER, which won the International GGP competition in both 2007 and 2008. There are two aspects to the improvements: first, we show that a simple ϵ-greedy exploration strategy works better in the simulation play-outs than the softmax-based Gibbs measure currently used in CADIAPLAYER and, second, we introduce a general framework based on N-grams for learning promising move sequences. Collectively, these enhancements result in a much improved performance of CADIAPLAYER. For example, in our test suite consisting of five different two-player turn-based games, they led to an impressive average win rate of approximately 70%. The enhancements are also shown to be effective in multiplayer and simultaneous-move games. We additionally perform experiments with the last-good-reply policy (LGRP). The LGRP combined with N-grams is also tested. The LGRP has already been shown to be successful in Go programs and we demonstrate that it also has promise in GGP. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Discrete Evolutionary Model for Chess Players' Ratings

    Page(s): 84 - 93
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1808 KB) |  | HTML iconHTML  

    The Elo system for rating chess players, also used in other games and sports, was adopted by the World Chess Federation over four decades ago. Although not without controversy, it is accepted as generally reliable and provides a method for assessing players' strengths and ranking them in official tournaments. It is generally accepted that the distribution of players' rating data is approximately normal but, to date, no stochastic model of how the distribution might have arisen has been proposed. We propose such an evolutionary stochastic model, which models the arrival of players into the rating pool, the games they play against each other, and how the results of these games affect their ratings, in a similar manner to the Elo system. Using a continuous approximation to the discrete model, we derive the distribution for players' ratings at time t as a normal distribution, where the variance increases in time as a logarithmic function of t. We validate the model using published rating data from 2007-2010, showing that the parameters obtained from the data can be recovered through simulations of the stochastic model. The distribution of players' ratings is only approximately normal and has been shown to have a small negative skew. We show how to modify our evolutionary stochastic model to take this skewness into account, and we validate the modified model using the published official rating data. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Evolving Multimodal Networks for Multitask Games

    Page(s): 94 - 111
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1919 KB) |  | HTML iconHTML  

    Intelligent opponent behavior makes video games interesting to human players. Evolutionary computation can discover such behavior, however, it is challenging to evolve behavior that consists of multiple separate tasks. This paper evaluates three ways of meeting this challenge via neuroevolution: 1) multinetwork learns separate controllers for each task, which are then combined manually; 2) multitask evolves separate output units for each task, but shares information within the network's hidden layer; and 3) mode mutation evolves new output modes, and includes a way to arbitrate between them. Whereas the fist two methods require that the task division be known, mode mutation does not. Results in Front/Back Ramming and Predator/Prey games show that each of these methods has different strengths. Multinetwork is good in both domains, taking advantage of the clear division between tasks. Multitask performs well in Front/Back Ramming, in which the relative difficulty of the tasks is even, but poorly in Predator/Prey, in which it is lopsided. Interestingly, mode mutation adapts to this asymmetry and performs well in Predator/Prey. This result demonstrates how a human-specified task division is not always the best. Altogether the results suggest how human knowledge and learning can be combined most effectively to evolve multimodal behavior. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Bitwise-Parallel Reduction for Connection Tests

    Page(s): 112 - 119
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1658 KB) |  | HTML iconHTML  

    This paper introduces bitwise-parallel reduction (BPR), an efficient method for performing connection tests in hexagonal connection games such as Hex and Y. BPR is based on a known property of Y that games can be reduced to a single value indicating the fully connected player (if any) through a sequence of reduction operations. We adapt this process for bitwise-parallel implementation and demonstrate its benefit over a range of board sizes. BPR is by far the fastest known method if connection tests only need to be performed once per game, for example, to evaluate board fills following Monte Carlo playouts. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Information Set Monte Carlo Tree Search

    Page(s): 120 - 143
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2106 KB) |  | HTML iconHTML  

    Monte Carlo tree search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information. This paper investigates the application of MCTS methods to games with hidden information and uncertainty. In particular, three new information set MCTS (ISMCTS) algorithms are presented which handle different sources of hidden information and uncertainty in games. Instead of searching minimax trees of game states, the ISMCTS algorithms search trees of information sets, more directly analyzing the true structure of the game. These algorithms are tested in three domains with different characteristics, and it is demonstrated that our new algorithms outperform existing approaches to handling hidden information and uncertainty in games. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Benchmarks for Grid-Based Pathfinding

    Page(s): 144 - 148
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (471 KB) |  | HTML iconHTML  

    The study of algorithms on grids has been widespread in a number of research areas. Grids are easy to implement and offer fast memory access. Because of their simplicity, they are used even in commercial video games. But, the evaluation of work on grids has been inconsistent between different papers. Many research papers use different problem sets, making it difficult to compare results between papers. Furthermore, the performance characteristics of each test set are not necessarily obvious. This has motivated the creation of a standard test set of maps and problems on the maps that are open for all researchers to use. In addition to creating these sets, we use a variety of metrics to analyze the properties of the test sets. The goal is that these test sets will be useful to many researchers, making experimental results more comparable across papers, and improving the quality of research on grid-based domains. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • IEEE Computational Intelligence Society Information

    Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (38 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Computational Intelligence and AI in Games information for authors

    Page(s): C4
    Save to Project icon | Request Permissions | PDF file iconPDF (29 KB)  
    Freely Available from IEEE

Aims & Scope

The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Simon M. Lucas
School of Computer Science and Electronic Engineering
University of Essex
Colchester, Essex  CO43SQ, U.K.
sml@essex.ac.uk
Phone:+44 1206 872 048
Fax:+44 1206 872 788