Knowledge-Free and Learning-Based Methods in Intelligent Game Playing [recurso electrónico] / by Jacek Mandziuk.

Por: Mandziuk, Jacek [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoSeries Studies in Computational Intelligence ; 276Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Descripción: XVIII, 254 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642116780Tema(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q342Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer eBooksResumen: 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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Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos Q342 (Browse shelf(Abre debajo)) 1 No para préstamo 374075-2001

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.

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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