AI Wisdom book series is going OUT OF PRINT
Used copies of AI Game Programming Wisdom 4 are selling for $440.00 on Amazon. There are a couple new copies of the first volume from Amazon resellers at a reasonable price ($54.73). Volumes 2 and 3 are still available, but since new books won't be printed, they will soon be just as scarce.
The whole series will soon be out of print - get your copies $54.73 (8 new books left from resellers) AI Game Programming Wisdom 1$39.96 (43% off) AI Game Programming Wisdom 2$39.96 (43% off) AI Game Programming Wisdom 3$440.00 (USED PRICE - out of print premium) AI Game Programming Wisdom 4
before they are gone:
Please don't contact me about AI Game Programming Wisdom 4 books. I don't have any to offer.
Game AI: Prediction
Abstract: This article will introduce Occupancy Maps, a technique for probabilistically tracking object positions. Occupancy Maps, an application of a broader Expectation Theory, can result in more interesting and realistic searching behaviors, and can also be used to generate emotional reactions to search events, like surprise (at finding a target in an unexpected place) and confusion (at failing to find a target in an expected place). It is also argued that the use of more in-depth knowledge-modeling techniques such as Occupancy Maps can relieve some of the complexity of a traditional FSM or HFSM approach to search behavior.
Probabilistic Target Tracking and Search Using Occupancy Maps
Abstract: Hidden Markov models are a probabilistic technique that provides an inexpensive and intuitive means of modeling stochastic processes. This article introduces the models, presents the computational details of tracking processes over time, and shows how they can be used to track player's movement and behavior based on scattered and uncertain observations.
Introduction to Hidden Markov Models
Abstract: This article describes how to create models of players based on their preferences for certain game states and shows how these models can be used to predicts a player's actions. We show how this enables the computer to reason more intelligently about its actions, to adapt to the player, and thereby to act as a more challenging and entertaining opponent. The article describes two ways to create models, player model search and probabilistic player model search, and illustrates their application with the help of pseudo-code. Finally, the article provides an example of how these techniques could be used to enhance a computer's diplomatic reasoning in a strategy game.
Preference-Based Player Modeling
Abstract: This article describes a lightweight, flexible machine learning technique we call player modeling, designed to help add adaptivity to your game AI. The basic idea is simple: the game maintains a profile of each player that captures the skills, weaknesses, preferences, and other characteristics of that player. This model is updated by the game as it interacts with the player. In turn, the game AI can query the player model to determine how best to adapt its behavior to that particular player - for example, by asking which of several possible tactics will be most challenging to the player. Using player modeling, a game's AI can adapt both during the course of a single play as well as over multiple sessions, resulting in a computer opponent that changes and evolves with time to suit the player.
Player Modeling for Adaptive Games
The article first defines the player model concept in more detail and then discusses strategies for designing a model to suit your game. It then presents a basic player model implementation. Subsequent sections describe how to actually integrate the modeling system with your game, including both how to update the model and how to make use of the information that it contains. The remainder of the article presents several advanced concepts, including a hierarchical player model, alternate model update methods, and other uses for the player model.
Abstract: In recent years, decision trees have gained popularity within the game development community as a practical learning method that can help an AI adapt to a player. Instead of picking from a canned set of reactions to player action, the AI has the opportunity to do something much more powerful: anticipate the player's action before he acts. In this article, we discuss a decision tree learning algorithm called ID3, which constructs a decision tree that identifies the telltale features of an experience to predict its outcome. We then establish ID3's role in Black & White, building on an earlier article in the first edition of AI Game Programming Wisdom. Finally, we consider some important aspects and extensions to the approach, and provide s ample code which implements a simple form of ID3.
Constructing a Decision Tree Based on Past Experience
Abstract: The task of pattern recognition is to abstract relevant information from the game world and, based on the retrieved information, construct concepts and deduce patterns for the use of higher level reasoning and decision-making systems. We view pattern recognition in computer games from two perspectives: functional and methodological. In the functional approach, we analyze what is required from pattern recognition. We conclude that it can act in different roles, which in turn affect the choice of a method and its implementation. These roles depend on the level of decision-making, the stance toward the player, and the use of the modeled knowledge. In the methodological approach, we review a branch of pattern recognition techniques arising from soft computing. We discuss methods related to optimization, adaptation, and uncertainty. Our intention is to clarify where these methods should be used.
Understanding Pattern Recognition Methods
Client-Side Movement Prediction
Abstract: It is anticipated that the widespread adoption of learning in games will be one of the most important advances ever to be made in game AI. Genuinely adaptive AIs will change the way that games are played by forcing the player to continually search for new strategies to defeat the AI. This article presents a detailed examination of the different approaches available for adding learning and adaptation to games and draws on the author's experiences of AI development to provide numerous practical examples. The reader is guided through the decisions that must be made when designing an adaptive AI, and summaries of the problems that are most frequently encountered with practical implementations are provided along with descriptions of effective solutions. The CD that accompanies the book contains source code for a genetic programming class, which can be used to evolve rule-based AI, and genetic algorithm and population-based incremental learner classes, which can be used to evolve AI more generally. The practical application of all these classes is illustrated by evolving an AI that successfully navigates a simple environment.
Learning and Adaptation in Games
Abstract: This article provides a simple but efficient algorithm for recognizing repetitive patterns in number sequences. Pattern recognition is something that humans are very good at, but for a computer this is not so easy. Too often a game AI can be beaten by repeatedly performing the same trick, just because it is unable to perceive the pattern. This article explains how to deal with this problem and shows you how to map game events onto useful number sequences. Furthermore, it describes a few possible applications of the algorithm in computer games.
Pattern Recognition with Sequential Prediction
Abstract: N-Grams are statistical constructs used to predict sequences of events in situations that exhibit the property of local structure. Language is one such context: the probability of hearing the word "fries" is higher if one has just heard the word "french" than if one has just heard the word "fruit". Some games, specifically fighting games in which players develop signature move combinations, also exhibit this property. The article describes how to train an AI to recognize patterns and predict the human player's next move using N-Gram models.
Using N-Gram Statistical Models to Predict Player Behavior