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Advances in narrow artificial intelligence make possible agentive systems that do things directly for their users (like, say, an automatic pet feeder). They deliver on the promise of user-centered design, but present fresh challenges in understanding their unique promises and pitfalls. Designing Agentive Technology provides both a conceptual grounding and practical advice to unlock agentive technology's massive potential.

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Unread book in perfect condition.

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Unread book in perfect condition.

Advances in narrow artificial intelligence make possible agentive systems that do things directly for their users (like, say, an automatic pet feeder). They deliver on the promise of user-centered design, but present fresh challenges in understanding their unique promises and pitfalls. Designing Agentive Technology provides both a conceptual grounding and practical advice to unlock agentive technology's massive potential.

What if we have been wrong about learning? Learning may have more in common with marketing than we thought. Looking at marketing and learning's common root, How People Learn shows L&D professionals a new way of thinking about learning by exploring what happens when we learn. It considers applications from AI, marketing and ethics and is informed by psychology and contemporary neuroscience in order to show L&D professionals how to design training with their employees in mind so that training makes a real difference to skills, capabilities, performance and development, rather than being a waste of time, money and resources. Using the author's '5Di model', How People Learn demonstrates how to define, design and deploy training in a user-centred way so it works both for and with employees. It also includes guidance on what training resources to create when employees are actively searching for learning content. Using this book, L&D practitioners will be able to use pull and push techniques to provide content that people use and experiences that transform their behaviour. From how to use simulations, storytelling and anticipation to the importance of observation and status, this book gives L&D professionals everything they need to build effective training programmes and learning experiences. With a foreword by Dr Roger Schank, the Chairman and CEO of Socratic Arts and Executive Director of Engines for Education, and case studies from companies such as BP and the BBC, this is an urgent read for learning professionals.

What if we have been wrong about learning? Learning may have more in common with marketing than we thought. Looking at marketing and learning's common root, How People Learn shows L&D professionals a new way of thinking about learning by exploring what happens when we learn. It considers applications from AI, marketing and ethics and is informed by psychology and contemporary neuroscience in order to show L&D professionals how to design training with their employees in mind so that training makes a real difference to skills, capabilities, performance and development, rather than being a waste of time, money and resources. Using the author's '5Di model', How People Learn demonstrates how to define, design and deploy training in a user-centred way so it works both for and with employees. It also includes guidance on what training resources to create when employees are actively searching for learning content. Using this book, L&D practitioners will be able to use pull and push techniques to provide content that people use and experiences that transform their behaviour. From how to use simulations, storytelling and anticipation to the importance of observation and status, this book gives L&D professionals everything they need to build effective training programmes and learning experiences. With a foreword by Dr Roger Schank, the Chairman and CEO of Socratic Arts and Executive Director of Engines for Education, and case studies from companies such as BP and the BBC, this is an urgent read for learning professionals.

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

Programming Game AI by Example provides a comprehensive and practical introduction to the “bread and butter” AI techniques used by the game development industry, leading the reader through the process of designing, programming, and implementing intelligent agents for action games using the C++ programming language. Techniques covered include state- and goal-based behavior, inter-agent communication, individual and group steering behaviors, team AI, graph theory, search, path planning and optimization, triggers, scripting, scripted finite state machines, perceptual modeling, goal evaluation, goal arbitration, and fuzzy logic.

From celebrated Relic author Douglas Preston, Wyman Ford races to stop a rogue AI in The Kraken Project, a New York Times bestselling thriller “as chilling as it is provocative" (James Rollins)NASA's Goddard Space Flight Center is designing a probe which will be dropped into the Kraken Mare, a methane sea on Titan, where it will embark on a journey of exploration. But things at Goddard go awry, and the AI program in the probe called "Dorothy" flees to the internet. Former CIA agent Wyman Ford is tapped to track down the software with the help of Dorothy's creator, Melissa Shepherd. As they trace Dorothy in cyberspace, they realize horrific experiences in the wasteland of the Internet have changed her―and they learn she's being pursued by a pair of Wall Street high-frequency traders who want to turn her into an algorithmic-trading slave-bot. Traumatized and angry, Dorothy jumps out of the Internet into a child's toy robot to hide. But is she bent on doing good―or on wiping out the human race?This edition of the book is the deluxe, tall rack mass market paperback.