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 Tactics and Strategy gathers all the cutting-edge information from his previous three Game AI Pro volumes into a convenient single source anthology that covers game AI strategy and tactics. This volume is complete with articles by leading game AI programmers that focus largely on combat decisions made in a wide variety of genres such as RTS, RPG, MOBA, strategy and tower defense games. 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

From Jessica Khoury, author of the Corpus Trilogy, comes a gripping story of royalty and rebellion, coursing across a far-away star system like a trail of jewels. Sixteen years ago, rebellion swept the galaxy known as the Belt of Jewels. Every member of the Leonov royal family was murdered--down to their youngest child, Princess Anya--making way for the Union government to seize power. But Stacia doesn't think much about politics. She spends her days half-wild, rambling her father's vineyard with her friends, Clio and Pol. That all changes when a Union ship appears burning through the sky, bearing the leader of the Union, the Direktor Eminent himself, who declares that Stacia's sleepy village is a den of empire loyalists. Even more shocking, the Direktor claims that Princess Anya Leonova is alive--and Stacia is the lost princessAs their home explodes into chaos, Pol smuggles Stacia to a hidden escape ship, leaving Clio in the hands of the Union. With everything she knows threading away into the stars, Stacia sets her heart on a single mission:She will find and rescue Clio, even with the whole galaxy on her trail.

The first volume of a three-volume complete edition of Newton's optical papers contains his Optical Lectures, delivered at Cambridge University between 1670 and 1672. The Lectures is Newton's first major scientific treatise, and consequently it represents a crucial link between his early years of discovery and his mature investigations and publications, such as the Optiks in 1704. It is divided into two parts: the first part devoted to color and the second to refraction. Originally published in 1984, this edition made available the complete text, together with translation and commentary, of both surviving versions of the Lectures, a draft and a vastly expanded revision. Until the time of publication, scholars had to depend on an uncritical text of the revision and an inadequate partial English translation, both published shortly after Newton's death. Professor Shapiro's critical edition has made a great contribution to the study of Newtonian science.

Strategy games are a unique and interesting testbed for AI protocols due their complex rules and large state and action spaces. Recent work in game AI has shown that strong, robust AI agents can be created by combining existing techniques of deep learning and heuristic search. Heuristic search techniques typically make use of an evaluation function to judge the value of a game state, however these functions have historically been hand-coded by game experts. Recent results have shown that it is possible to use modern deep learning techniques to learn these evaluation functions, bypassing the need for expert knowledge. In this thesis, we explore the implementation of this idea in Prismata, an online strategy game by Lunarch Studios. By generating game trace training data with existing state-of-the-art AI agents, we are able to use a Machine Learning (ML) approach to learn a new evaluation function. We trained several evaluation models with various parameters in order to compare prediction time with prediction accuracy. To evaluate the strength of our learned model, we ran a tournament between AI players which differ only in their state evaluation strategy. The results of this tournament demonstrate that our learned model when combined with the existing Prismata Hierarchical Portfolio Search system, produces a new AI agent which is able to defeat the previously strongest agents. A subset of the research presented in this thesis was the subject of a publication in the Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2019 Strategy Games Workshop [1].