Programming Game AI by Example

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1. tvalentiusFeb 2020
   Programming Game AI By Example

   https://www.amazon.com/Programming-Example-Wordware-Develope...

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2. megaman22Feb 2018
   I wish I'd learned about state machines... Most of the potentially interesting CS courses at my college were taught by terrible professors who were allergic to code.

   Thankfully I enjoyed screwing around with game programming, and picked up Matt Buckland's Programming Game AI by Example[1]. I've used essentially the same FSM design described there over and over in my career.

   [1] http://amzn.to/2FxZ54c

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3. tvalentiusMar 2017
   If you are developing AI for games, then Programming Game AI By Example by Mat Buckland are one of the best book for that : https://www.amazon.com/Programming-Example-Wordware-Develope...
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4. doucheDec 2016
   The Matt Buckland books, especially Programming Game AI by Example are very good, for simple, practical techniques.
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5. haxporFeb 2016
   I would go for "Programming Game AI by example" by Matt Buckland. Also great resource is aigamedev.com.
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6. GuiAJan 2015
   Speaking for the ones I have on my shelves:

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   The AI Game Programming Wisdom and Game Programming Gems are not revision of the same things. They're just part of the same series; the format of the books is more akin to a collection of articles (all by different authors from the industry), grouped by topic. For example in volume 1, you have:

   - General Wisdom (4 articles)

   - Useful Techniques and Specialized Systems (8)

   - Pathfinding with A* (5)

   - Pathfinding and Movement (6)

   - Tactical Issues and Intelligent Group Movement (6)

   - General Purpose Architectures (6)

   - Decision Making Architectures (5)

   - FPS, RTS, and RPG AI (6)

   - Racing and Sports AI (8)

   - Scripting (7)

   - Learning (10)

   (the groupings are different for each books)

   I quite recommend these books - the quality of the articles can be uneven, but you almost always learn something. The main critics against them are that 1) the material can be dated (these books are from the early 2000s) and 2) some of the content can be found online for free. They're still good resources to have on your shelves IMO.

   In the same vein, I recommend the GPU Gems and Graphics Gems for realtime graphics programming.

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   "Programming Game AI by Example" by Buckland is a great, hands on, practical approach to game programming. It is not academic or theoretical by any means, but that's one of its strong traits - it's code driven. It was my first AI read while in high school, and I really recommend it. The perfect audience for that would be a motivated teenager or undergrad who knows enough programming to make small games, but hasn't taken full AI courses yet.

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   "Artificial Intelligence: A Modern Approach" by Norvig et al. is kind of the CLRS of AI - a thorough, deep introduction to the field that will give you all the tools you need to solve pretty much any problem you can encounter. It can be a bit dry/academic at times, but that's the price you have to pay. It's great to learn from it as part of a focused course while you're still getting familiar with the field, and it's also great to flip to a random chapter every once in a while to revisit the basics when you're a more mature professional.

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   "Designing Sociable Robots" by Breazal is a cool read - it's a collection of 13 fairly independent chapters by the author which mash together HCI, social sciences, robotics, electronics, programming, and each explain how a subcomponent of a full "sociable" robot (in this case her lab's project, Kismet) works (e.g. the vision system, an expressive vocalization system, etc.).

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   "Society of Mind" by Minsky is a very interesting read. It's more high level and philosophical, but it's definitely worth it. It's almost 30 years old though, and I'm not sure how much his work has held up to modern theories - I don't follow that field too closely.

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   "Behavioral Mathematics for Game AI" by Mark is not that code heavy compared to the others (especially the Game AI Wisdom series), although it does have some, and is rather more oriented towards mathematical modeling. It touches on game theory, utility functions, probability distributions, etc. I think it's the most mathy an AI text can be before becoming pretty much pure math, which is great- it's a great stepping stone for the folks who are programmers first and who want to know more of the math theory behind it all (whether just for understanding or to be able to build their own algorithms).

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