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Building a Language Data Set in Telugu Using Machine Learning Techniques to Address Suicidal Ideation and Behaviors in Adolescents Read chapter Read first chapter

2021 | OriginalPaper | Chapter

An Evolutionary Approach to Combinatorial Gameplaying Using Extended Classifier Systems

Authors : Karmanya Oberoi, Sarthak Tandon, Abhishek Das, Swati Aggarwal

Published in: Applications of Artificial Intelligence and Machine Learning

Publisher: Springer Singapore

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Abstract

Extended classifier system (XCS) is an extension of a popular online rule-based machine learning technique, learning classifier system (LCS), in which a classifier’s fitness is based on its accuracy instead of the prediction itself, and a genetic algorithm (GA) and reinforcement learning (RL) component is utilized for exploratory and learning purposes, respectively. With the emergence of increasingly intricate rule-based learning techniques, there is a need to examine feasible methods of learning that can overcome the challenges posed by complex scenarios while supporting online performance. Checkers is a strategic, combinatorial game having a high branching factor and a complex state space that provides a promising avenue for scrutinizing novel approaches. This paper presents a preliminary investigation into feasibility of XCS in such complex avenues by taking 6 × 6 checkers as a specific case of study. The XCS agent was adapted to this problem, trained with random agent and was able to perform well against the alpha–beta pruning algorithm of various depths as well as human agents of different skill levels (beginner, intermediate and advanced).

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previous chapter A Review on Deep Learning Models for Short-Term Load Forecasting
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Metadata
Title
An Evolutionary Approach to Combinatorial Gameplaying Using Extended Classifier Systems
Authors
Karmanya Oberoi
Sarthak Tandon
Abhishek Das
Swati Aggarwal
Copyright Year
2021
Publisher
Springer Singapore
Book
Applications of Artificial Intelligence and Machine Learning

Print ISBN: 978-981-16-3066-8

Electronic ISBN: 978-981-16-3067-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-3067-5_54

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