AI Techniques for Game
Programming (The Premier Press
Game Development Series) by Mat
Buckland
Definitely Easy
AI Techniques for Game Programming takes the difficult topics of genetic
algorithms and neural networks, explaining them in plain English. Gone are
the tortuous mathematic equations and abstract examples to be found in
other books. Each chapter will take you through the theory a step at a time
using fun, practical examples and providing you with all the knowledge you
require to start incorporating these esoteric techniques into your own
games and applications. After a whirlwind tour of Windows programming
for those readers who require a refresher, you will learn how to use genetic
algorithms for optimization, path-finding and evolving control sequences for
your game agents. After learning the basics of neural networks, AI
Techniques for Game Programming will demonstrate how you can evolve

neural motion controllers for your game agents, and how neural networks
may be applied to obstacle avoidance and map exploration. You will learn
about backpropagation and pattern recognition and will discover how to
train a network to recognize mouse gestures. Finally, you’ll learn state-of-
the-art techniques for creating neural networks with dynamic topologies.
Each chapter is complimented by well-commented source code and most
provide fun exercises and problems for you to practice your new found
knowledge.
Personal Review: AI Techniques for Game Programming (The
Premier Press Game Development Series) by Mat Buckland
I know what you're thinking: Heck, a book published in the early 2000s with
reviews in 2005 has to be pretty dated as we look at 2010 and beyond.
Wrong! Sure, most AI programmers have moved past Win 32 with
numerous new techniques in C++ and Java, and the author's subsequent
book (Programming Game AI by Example Programming Game AI by
Example) is outstanding in filling in details left out for beginning and
intermediate programmers here. However, TGP has all the makings of a
classic, and if you miss it, your AI library will have a glaring and lonely
hole. Our aeronautic simulation group at xtmh dot com hires numerous
fresh grads from quality practical schools like Full Sail as well as quality
abstract schools like MIT. What we find is that students who have
grounded themselves in Buckland's two books, then evolved by staying up
to date with his refresing openness to communication and support, have
an outstanding balance of higher math skills (like quadratic programming,
tensors and vectors) and practical "biological" agent motivation wisdom.
Let's take another example: assume you love game design, but your skill
set is in writing, dialog and character development, not MIT-level tensor
mapping. Let's also say you've read the wonderful ultimate guide to writing
and design (The Ultimate Guide to Video Game Writing and Design
[ULTIMATE GT VIDEO GAME WRI -OS]. You are a skillful character,
situation and dialog writer and designer, and will likely find a welcome
place in supercomputing, the game industry, entertainment, or even the
rapidly integrating fields (thank you pixar) of film/TV/gaming. To be
successful and at the top of your game, you'll need to communicate your
characters' motives, flaws, quests, wins and losses (a lot like real life) to
the AI geniuses who will execute your vision in code. BOTH of Dille's
books are written at a basic enough level to help you start translating your
characterizations into agent behaviors. The opposite also is true-- if you're
more of a codie, you'll love opening your mind to thinking at a more agent
motivational level, with both time tested and new models of behavior in
varying situations. Let's be honest-- the games of 2020 and beyond are all
about intelligent agent interaction. The oldest problem in gaming-- how to
dumb down a smart hero character enough to make them need a quest,
but at the same time not make them look like idiots (stop, I don't want to
hear about amnesia), has a corrolary in real life: sure, we could have been
made smart enough to know all of life by just hacking our own brain, but
God and our own Higher Self User put this odd dichotomy of an
unconscious brain able to do array processor and direct geometric tensor

mapping-- basically matrix calculus of partial derivatives-- something no
supercomputer can do yet (we still have to convert geometric matrices and
tensors to numeric models for processing even in supercomputers)-- when
doing as simple an act as crossing a street in traffic; with a "conscious"
brain that sometimes has trouble with four function math! If you need to
figure out the paradox of dumbing down a genius in your character
development, and the dicotomy seems too far fetched-- simply check out
your average friend, or look in the mirror! Is this a digression? Nope-- we're
just hinting at how cool a combination you will be able to imagine if you
combine your study of data structures and algorithms with the now well
established multi agent AI techniques. A hint at the future: we're finding
clients as sophisticated as NASA and Los Alamos looking for new models
of AFFECTIVE programming right now. Meaning, characters that not only
learn and think as they move through your sims, but also are motivated by
that other squishy reality-- heart. So the critics who think Buckland is out of
date or too simple-- map your tensor into another frame of reference-- that
of bridging code with biological and emotional motivation-- and you'll see
why the most interesting games, sims and even real life "models" of
cellular and synaptic structure and function need BOTH high level math
and fun and interesting situational behaviors for the best games, and lives.
For More 5 Star Customer Reviews and Lowest Price:
AI Techniques for Game Programming (The Premier Press Game Development Series)
by Mat Buckland 5 Star Customer Reviews and Lowest Price!