Motivated Reinforcement Learning

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Motivated Reinforcement Learning / Kathryn E. Merrick, Mary Lou Maher

資料種別:

電子ブック

出版情報:

SpringerLink Books - AutoHoldings
Springer Berlin Heidelberg, 2009

著者名:

ISBN:

9783540891864 [3540891862]
9786612332050 [6612332050]
9783642100352 [364210035X]

書誌ID:

OB00500052

子書誌情報

フルテキスト

所蔵情報

書誌詳細

主題:

Information Technology - Artificial Intelligence

言語:

英語

目次情報:

Non-Player Characters and Reinforcement Learning / Part I：

Non-Player Characters in Multiuser Games / 1：

Types of Multiuser Games / 1.1：

Massively Multiplayer Online Role-Playing Games / 1.1.1：

Multiuser Simulation Games / 1.1.2：

Open-Ended Virtual Worlds / 1.1.3：

Character Roles in Multiuser Games / 1.2：

Existing Artificial Intelligence Techniques for Non-Player Characters in Multiuser Games / 1.3：

Reflexive Agents / 1.3.1：

Learning Agents / 1.3.2：

Evolutionary Agents / 1.3.3：

Smart Terrain / 1.3.4：

Summary / 1.4：

References / 1.5：

Motivation in Natural and Artificial Agents / 2：

Defining Motivation / 2.1：

Biological Theories of Motivation / 2.2：

Drive Theory / 2.2.1：

Motivational State Theory / 2.2.2：

Arousal / 2.2.3：

Cognitive Theories of Motivation / 2.3：

Curiosity / 2.3.1：

Operant Theory / 2.3.2：

Incentive / 2.3.3：

Achievement Motivation / 2.3.4：

Attribution Theory / 2.3.5：

Intrinsic Motivation / 2.3.6：

Social Theories of Motivation / 2.4：

Conformity / 2.4.1：

Cultural Effect / 2.4.2：

Evolution / 2.4.3：

Combined Motivation Theories / 2.5：

Maslow's Hierarchy of Needs / 2.5.1：

Existence Relatedness Growth Theory / 2.5.2：

Towards Motivated Reinforcement Learning / 2.6：

Defining Reinforcement Learning / 3.1：

Dynamic Programming / 3.1.1：

Monte Carlo Methods / 3.1.2：

Temporal Difference Learning / 3.1.3：

Reinforcement Learning in Complex Environments / 3.2：

Partially Observable Environments / 3.2.1：

Function Approximation / 3.2.2：

Hierarchical Reinforcement Learning / 3.2.3：

Motivated Reinforcement Learning / 3.3：

Using a Motivation Signal in Addition to a Reward Signal / 3.3.1：

Using a Motivation Signal Instead of a Reward Signal / 3.3.2：

Comparing the Behaviour of Learning Agents / 3.4：

Player Satisfaction / 4.1：

Psychological Flow / 4.1.1：

Structural Flow / 4.1.2：

Formalising Non-Player Character Behaviour / 4.2：

Models of Optimality for Reinforcement Learning / 4.2.1：

Characteristics of Motivated Reinforcement Learning / 4.2.2：

Comparing Motivated Reinforcement Learning Agents / 4.3：

Statistical Model for Identifying Learned Tasks / 4.3.1：

Behavioural Variety / 4.3.2：

Behavioural Complexity / 4.3.3：

Developing Curious Characters Using Motivated Reinforcement Learning / 4.4：

Curiosity, Motivation and Attention Focus / 5：

Agents in Complex, Dynamic Environments / 5.1：

States / 5.1.1：

Actions / 5.1.2：

Reward and Motivation / 5.1.3：

Motivation and Attention Focus / 5.2：

Observations / 5.2.1：

Events / 5.2.2：

Tasks and Task Selection / 5.2.3：

Experience-Based Reward as Cognitive Motivation / 5.2.4：

Arbitration Functions / 5.2.5：

A General Experience-Based Motivation Function / 5.2.6：

Curiosity as Motivation for Support Characters / 5.3：

Curiosity as Interesting Events / 5.3.1：

Curiosity as Interesting and Competence / 5.3.2：

Motivated Reinforcement Learning Agents / 5.4：

A General Motivated Reinforcement Learning Model / 6.1：

Algorithms for Motivated Reinforcement Learning / 6.2：

Motivated Flat Reinforcement Learning / 6.2.1：

Motivated Multioption Reinforcement Learning / 6.2.2：

Motivated Hierarchical Reinforcement Learning / 6.2.3：

Curious Characters in Games / 6.3：

Curious Characters for Multiuser Games / 7：

Motivated Reinforcement Learning for Support Characters in Massively Multiplayer Online Role-Playing Games / 7.1：

Character Behaviour in Small-Scale, Isolated Games Locations / 7.2：

Case Studies of Individual Characters / 7.2.1：

General Trends in Character Behaviour / 7.2.2：

Curious Characters for Games in Complex, Dynamic Environments / 7.3：

Designing Characters That Can Multitask / 8.1：

Designing Characters for Complex Tasks / 8.1.1：

Games That Change While Characters Are Learning / 8.2.1：

Curious Characters for Games in Second Life / 8.3.1：

Motivated Reinforcement Learning in Open-Ended Simulation Games / 9.1：

Game Design / 9.1.1：

Character Design / 9.1.2：

Evaluating Character Behaviour in Response to Game Play Sequences / 9.2：

Discussion / 9.2.1：

Future / 9.3：

Towards the Future / 10：

Using Motivated Reinforcement Learning in Non-Player Characters / 10.1：

Other Gaming Applications for Motivated Reinforcement Learning / 10.2：

Dynamic Difficulty Adjustment / 10.2.1：

Procedural Content Generation / 10.2.2：

Beyond Curiosity / 10.3：

Biological Models of Motivation / 10.3.1：

Cognitive Models of Motivation / 10.3.2：

Social Models of Motivation / 10.3.3：

Combined Models of Motivation / 10.3.4：

New Models of Motivated Learning / 10.4：

Motivated Supervised Learning / 10.4.1：

Motivated Unsupervised Learning / 10.4.2：

Evaluating the Behaviour of Motivated Learning Agents / 10.5：

Concluding Remarks / 10.6：

Index / 10.7：

Non-Player Characters and Reinforcement Learning / Part I：

Non-Player Characters in Multiuser Games / 1：

Types of Multiuser Games / 1.1：

Massively Multiplayer Online Role-Playing Games / 1.1.1：

Multiuser Simulation Games / 1.1.2：

Open-Ended Virtual Worlds / 1.1.3：

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