Download Free Proceedings The Eighth Aaai Conference On Artificial Intelligence And Interactive Digital Entertainment 8 12 October 2012 Stanford California Book in PDF and EPUB Free Download. You can read online Proceedings The Eighth Aaai Conference On Artificial Intelligence And Interactive Digital Entertainment 8 12 October 2012 Stanford California and write the review.

Game AI Pro3: Collected Wisdom of Game AI Professionals presents state-of-the-art tips, tricks, and techniques drawn from developers of shipped commercial games as well as some of the best-known academics in the field. This book acts as a toolbox of proven techniques coupled with the newest advances in game AI. These techniques can be applied to almost any game and include topics such as behavior trees, utility theory, path planning, character behavior, and tactical reasoning. KEY FEATURES Contains 42 chapters from 50 of the game industry’s top developers and researchers. Provides real-life case studies of game AI in published commercial games. Covers a wide range of AI in games, with topics applicable to almost any game. Includes downloadable demos and/or source code, available at http://www.gameaipro.com SECTION EDITORS Neil Kirby General Wisdom Alex Champandard Architecture Nathan Sturtevant Movement and Pathfinding Damian Isla Character Behavior Kevin Dill Tactics and Strategy; Odds and Ends
Steve Rabin’s Game AI Pro 360: Guide to Architecture gathers all the cutting-edge information from his previous three Game AI Pro volumes into a convenient single source anthology covering game AI architecture. This volume is complete with articles by leading game AI programmers that further explore modern architecture such as behavior trees and share architectures used in top games such as Final Fantasy XV, the Call of Duty series and the Guild War series. Key Features Provides real-life case studies of game AI in published commercial games Material by top developers and researchers in Game AI Downloadable demos and/or source code available online
The practices and perception of music creation have evolved with the cultural, social and technological contexts of music and musicians. But musical authorship, in its many technical and aesthetic modes, remains an important component of music culture. Musicians are increasingly called on to share their experience in writing. However, cultural imperatives to account for composition as knowledge production and to make claims for its uniqueness inhibit the development of discourse in both expert and public spheres. Internet pioneer Philip Agre observed a discourse deficit in artificial intelligence research and proposed a critical technical practice, a single disciplinary field with “one foot planted in the craft work of design and the other foot planted in the reflexive work of critique. … A critical technical practice rethinks its own premises, re-evaluates its own methods, and reconsiders its own concepts as a routine part of its daily work.” This volume considers the potential for critical technical practice in the evolving situation of composition across a wide range of current practices. In seeking to tell more honest, useful stories of composition, it hopes to contribute to a new discourse around the creation of music.
Intelligent environments (IE) play an increasingly important role in many areas of our lives, including education, healthcare and the domestic environment. The term refers to physical spaces incorporating pervasive computing technology used to achieve specific goals for the user, the environment or both. This book presents the proceedings of the workshops of the 8th International Conference on Intelligent Environments (IE ‘12), held in Guanajuato, Mexico, in June 2012. The workshops which make up the conference range from regular lectures to practical sessions. They provide a forum for scientists, researchers and engineers from both industry and academia to engage in discussions on newly emerging or rapidly evolving topics in the field. Topics covered in the workshops include intelligent environments supporting healthcare and well-being; artificial intelligence techniques for ambient intelligence; large-scale intelligent environments; intelligent domestic robots; intelligent environment technology in education; multimodal interfaces applied in skills transfer, healthcare and rehabilitation; the reliability of intelligent environments and improving industrial automation using intelligent environments. IE can enrich user experience, better manage the environment's resources, and increase user awareness of that environment. This book will be of interest to all those whose work involves the application of intelligent environments.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Game AI Pro2: Collected Wisdom of Game AI Professionals presents cutting-edge tips, tricks, and techniques for artificial intelligence (AI) in games, drawn from developers of shipped commercial games as well as some of the best-known academics in the field. It contains knowledge, advice, hard-earned wisdom, and insights gathered from across the com
Scientific discovery is often regarded as romantic and creative--and hence unanalyzable--whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought processes used to discover scientific laws. The programs--BACON, DALTON, GLAUBER, and STAHL--are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science.
Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Narrative Intelligence (NI) — the confluence of narrative, Artificial Intelligence, and media studies — studies, models, and supports the human use of narrative to understand the world. This volume brings together established work and founding documents in Narrative Intelligence to form a common reference point for NI researchers, providing perspectives from computational linguistics, agent research, psychology, ethology, art, and media theory. It describes artificial agents with narratively structured behavior, agents that take part in stories and tours, systems that automatically generate stories, dramas, and documentaries, and systems that support people telling their own stories. It looks at how people use stories, the features of narrative that play a role in how people understand the world, and how human narrative ability may have evolved. It addresses meta-issues in NI: the history of the field, the stories AI researchers tell about their research, and the effects those stories have on the things they discover. (Series B)