IADaveMark

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About IADaveMark

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    Moderator - Artificial Intelligence

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  1. Behavior Pure Decision AI...best method to use

    If you read the "culinary guide" link I posted earlier, it has all of this in there and more. It will prevent you from intermixing your terms the way you are.
  2. Behavior Pure Decision AI...best method to use

    A "state machine" isn't necessarily a decision architecture. It is a way of keeping track of what state you are in. You can use all sorts of stuff on each of your many transitions to determine what to do next. A BT is, ultimately, going to result in you being in a state of some sort. The difference is that you are lifting the transition logic out of the individual states and putting them into a single reasoner. The result is the same, but the decision logic is cleaner. A utility system will also put you in a state if you want it, but, like a BT, the reasoner is a separate entity. For your game, the heavy lifting is done in the reasoner itself. You actually will have few, if any, "states" that you are in. You may have "mindsets" that coaches and GMs are in (e.g. "rebuilding year") but those are just numbers that affect how you look at other numbers.
  3. Behavior Pure Decision AI...best method to use

    Suggesting that the economic and strategic decision-making in football is different than the decision process used in FPS games is... uh... fairly obvious? I would suggest that it hardly bears mentioning. However, comparing it to something like a TBS game like Civ is more accurate and has plenty of overlap in the mentality and methodology. And yes, Civ-style games are almost entirely utility-based (i.e. mathematical modeling of decisions). As for uncertainty of the payback, it's pretty easy to apply an estimated success rate of payoffs and multiply the payoff amount by that. e.g. I have a 75% chance of this panning out and if it does, I will net $1000 worth of value. Otherwise, it may be $200 worth of value. So what's your average payoff given those factors? That's why it is called "maximization of expected utility". I know a book you can read that covers mathematical modeling of behavior in game AI. Can't think of the title right now, though...
  4. Behavior Pure Decision AI...best method to use

    Ubiquitous answer to all "how would I" questions... If you were doing this in a pen-and-paper game or a board game, how would you make the decision?
  5. Behavior Pure Decision AI...best method to use

    Ubiquitous answer for a starting point... http://intrinsicalgorithm.com/IAonAI/2012/11/ai-architectures-a-culinary-guide-gdmag-article/ Figure that everything you are going to be doing is weighing and scoring the relative value of different numerical inputs, biased by the personalities of the deciders. Straight up utility system is the way to go.
  6. Formal name for a Weighted List AI

    http://intrinsicalgorithm.com/IAonAI/2013/02/both-my-gdc-lectures-on-utility-theory-free-on-gdc-vault/ http://intrinsicalgorithm.com/IAonAI/2015/10/building-a-better-centaur-ai-at-massive-scale-gdc-ai-summit-lecture/ http://intrinsicalgorithm.com/IAonAI/2011/12/getting-more-behavior-out-of-numbers-gdmag-article/
  7. One thing to remember is that players are really good at coming up with explanations for the NPC behaviors even when there isn't one. They will ascribe meaning and intent to what is pouring out of a random die roll. If you start with perfect knowledge and then fuzzy it up a bit, it may look just as plausible as your "exploration and learning" method... but with a lot less work.
  8. I agree that grid-based is the root of the problem here. I just assumed that he couldn't switch to navmesh for whatever reason.
  9. You are correct Nypyren -- my bad. At that point, of course, stringpulling and similar are your solutions. An LOS check up the next nodes on the path list until one fails is the simple explanation. Then you just steer towards the last one you can see.
  10. 2nd link in my post is his code on Github.
  11. So no one read my post on JPS+? That's the standard way for not only allowing any angle movement but reducing the complexity of the search space so that your pathing calls are faster to begin with. Seriously... watch the vid with Steve Rabin.
  12. Quantum Neural Networks

    For game AI? More than likely, useless. Perhaps you are asking in the wrong place?
  13. JPS+ (or better) http://www.gdcvault.com/play/1022094/JPS-Over-100x-Faster-than https://github.com/SteveRabin/JPSPlusWithGoalBounding
  14. ANN error rate stuck at 0.5

    And now you get to pay yourself. That's how programming works.
  15. And if you environment isn't that dense and fairly regular, the complexity of your paths is going to be small. Not like you are plowing through 1000 node paths.