Knowledge-Free and Learning-Based Methods in Intelligent Game Playing

The book is focused on the developments and prospective challenging problems in the area of mind game playing (i.e. playing games that require mental skills) using Computational Intelligence (CI) methods, mainly neural networks, genetic/evolutionary programming and reinforcement learning. The majori...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Mandziuk, Jacek, 19..-
التنسيق: Livre numérique
اللغة:Anglais
منشور في: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
الطبعة:1st ed. 2010.
سلاسل:Studies in Computational Intelligence 276
الوصول للمادة أونلاين:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
ملاحظة: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Knowledge-Free and Learning-Based Methods in Intelligent Game Playing, Texte imprimé, 9783642262135
• Knowledge-Free and Learning-Based Methods in Intelligent Game Playing, Texte imprimé, 9783642117794
• Knowledge-Free and Learning-Based Methods in Intelligent Game Playing, Texte imprimé, 9783642116773
• Knowledge-Free and Learning-Based Methods in Intelligent Game Playing, Texte imprimé, 9783642262135
• Knowledge-Free and Learning-Based Methods in Intelligent Game Playing, Texte imprimé, 9783642117794
• Knowledge-Free and Learning-Based Methods in Intelligent Game Playing, Texte imprimé, 9783642116773
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490 0 |a Studies in Computational Intelligence  |v 276  |x 1860-9503 
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505 1 |a I: AI Tools and State-of-the-Art Accomplishments in Mind Games Foundations of AI and CI in Games. Claude Shannon s Postulates Basic AI Methods and Tools State of the Art II: CI Methods in Mind Games. Towards Human-Like Playing An Overview of Computational Intelligence Methods CI in Games Selected Approaches III: An Overview of Challenges and Open Problems Evaluation Function Learning Game Representation Efficient TD Training Move Ranking and Search-Free Playing Modeling the Opponent and Handling the Uncertainty IV: Grand Challenges Intuition Creativity and Knowledge Discovery Multi-game Playing Summary and Perspectives. 
506 |a Accès en ligne pour les établissements français bénéficiaires des licences nationales 
506 |a Accès soumis à abonnement pour tout autre établissement 
506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
520 |a The book is focused on the developments and prospective challenging problems in the area of mind game playing (i.e. playing games that require mental skills) using Computational Intelligence (CI) methods, mainly neural networks, genetic/evolutionary programming and reinforcement learning. The majority of discussed game playing ideas were selected based on their functional similarity to human game playing. These similarities include: learning from scratch, autonomous experience-based improvement and example-based learning. The above features determine the major distinction between CI and traditional AI methods relying mostly on using effective game tree search algorithms, carefully tuned hand-crafted evaluation functions or hardware-based brute-force methods. On the other hand, it should be noted that the aim of this book is by no means to underestimate the achievements of traditional AI methods in game playing domain. On the contrary, the accomplishments of AI approaches are undisputable and speak for themselves. The goal is rather to express my belief that other alternative ways of developing mind game playing machines are possible and urgently needed. 
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