NDLI: LS-Draughts - A Draughts Learning System based on genetic algorithms, neural network and temporal differences

Content Provider	IEEE Xplore Digital Library
Author	Neto, H.C. Julia, R.M.S.
Copyright Year	2007
Description	Author affiliation: Fed. Univ. of Uberlandia, Uberlandia (Neto, H.C.; Julia, R.M.S.)
Abstract	The objective of this paper is the proposal of a learning system - the LS-Draughts - which aims at using genetic algorithms (GAs) to automatically generate a concise and efficient set of features which are relevant in representing the game board states and for optimizing the training of a draught player agent. This agent consists of an Artificial Neural Network whose weights are updated by the temporal differences (TD) reinforcement learning methods. The NET-FEATUREMAP mapping is used to represent a game board state in the Network input. The network output corresponds to a real number (prediction) that indicates to what extent the input state is favorable to the agent. The agent is trained by self-play coupled with a cloning technique. The minimax algorithm is used to choose the best action to be executed considering the current game board state. Such a learning process is close to that proposed by Mark Lynch (NeuroDraughts). However, the LS-Draughts expands the NeuroDraughts as it automatically generates an effective and concise set of features to be used in the NET-FEATUREMAP mapping, whereas the latter uses a fixed and manually defined set of features. A tournament was promoted between the best player obtained by the LS-Draughts and the best available player of the NeuroDraughts. The tournament was won by the player of the LS-Draughts, which confirms that the GAs can be an important tool for improving the general performance of automatic players.
Starting Page	2523
Ending Page	2529
File Size	337298
Page Count	7
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424788
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Genetic algorithms Neural networks Evolutionary computation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A co-evolutionary algorithm for neural network learning

Parallel Learning Evolutionary Algorithm Based on Neural Network Ensemble

A memetic accuracy-based neural learning classifier system

Hybrid Evolutionary Learning Approaches for The Virus Game

Model-based evolutionary computing: a neural network and genetic algorithm architecture

AC servo system based on MEC optimization and fuzzy neural network control

Evolutionary Computation Schemes based on Max Plus Algebra and Their Application to Image Processing

A comparative study on GA based and BP based induction of neural network trees

Behavior learning of multiple mobile robots based on spiking neural networks with a parallel genetic algorithm

LS-Draughts - A Draughts Learning System based on genetic algorithms, neural network and temporal differences

Similar Documents

A co-evolutionary algorithm for neural network learning

Parallel Learning Evolutionary Algorithm Based on Neural Network Ensemble

A memetic accuracy-based neural learning classifier system

Hybrid Evolutionary Learning Approaches for The Virus Game

Model-based evolutionary computing: a neural network and genetic algorithm architecture

AC servo system based on MEC optimization and fuzzy neural network control

Evolutionary Computation Schemes based on Max Plus Algebra and Their Application to Image Processing

A comparative study on GA based and BP based induction of neural network trees

Behavior learning of multiple mobile robots based on spiking neural networks with a parallel genetic algorithm

LS-Draughts - A Draughts Learning System based on genetic algorithms, neural network and temporal differences