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IADaveMark

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About IADaveMark

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  1. There's tons of other information out there on "steering" and "obstacle avoidance" and whatnot.
  2. Somewhere right near your link for steering is his examples for obstacle avoidance -- be it for fixed objects or dynamic ones. Edit: Ugh... clicked over there and was reminded that his java apps don't exist anymore and so one of the best websites for n00bs looking to get into this is trashed. Sigh.
  3. IADaveMark

    Traveling Santa Problem

    One of the most interesting and successful solutions for the NP-Hard Traveling Salesman Problem was done via swarm theory. Basically spawning 1000 "ants" at the start point and having them select their next destination randomly from among those that they haven't visited yet. As each ant reaches the end, they report back along the path they took and adjust the weights of those connections pro or con. They they respawn and do it over again. The result is that as the weights for the paths gradually change, they end up biasing the paths towards the optimal one. They were able to solve a 16 node problem in great time which, using normal methods was a complete bitch to do. The method you are using by dividing things up geographically is similar to doing a coarse graph and using hierarchical A*.
  4. IADaveMark

    about GOAP

    Not a big stretch since GOAP pretty much uses A* to traverse the potential state space.
  5. IADaveMark

    Evolving neural networks

    You're gonna want to understand NNs a bit better before you make a claim like that.
  6. Sorry that no one has answered, but it is perhaps because your question is really not worded well at all... plus there seem to be missing images?
  7. IADaveMark

    questions about Utility AI

    Specifically "taco salad". http://intrinsicalgorithm.com/IAonAI/2012/11/ai-architectures-a-culinary-guide-gdmag-article/
  8. IADaveMark

    questions about Utility AI

    Sorry... my fault.
  9. IADaveMark

    about GOAP

    http://alumni.media.mit.edu/~jorkin/goap.html http://alumni.media.mit.edu/~jorkin/gdc2006_orkin_jeff_fear.pdf
  10. IADaveMark

    questions about Utility AI

    Start here: http://intrinsicalgorithm.com/IAonAI/2013/02/both-my-gdc-lectures-on-utility-theory-free-on-gdc-vault/ Then an entire architecture built on utility: http://intrinsicalgorithm.com/IAonAI/2015/10/building-a-better-centaur-ai-at-massive-scale-gdc-ai-summit-lecture/
  11. "Closest node" is the first part here. That means you don't want to search the entire space for something that can hit the target and then see how close it is. That means a lot of testing. However, if you do a pathfind from your current location, you are more likely to find "the closest node" quickly. That said, since you don't have a defined goal spot, you would need to do something along the lines of a flood fill out from your current location -- e.g. Dijkstra. -- and test each node you come to. Please note that this is a bitch in many games because raycasts can be expensive. Therefore testing each potential spot can add up quickly. Another approach is to work outwards from the target. That is, paint what the target can see and then find one of those spots that's close to you. I forget the description for that sort of visibility painting since I'm just now into my first caffeine of the morning but someone else may jump in and help.
  12. IADaveMark

    The blackboards of behavior trees

    Well a lot of the world info isn't going to need to get passed. Passing copies of the data isn't a great idea for a lot of reasons. Just look it up from the behavior objects as needed. Same thing for the character's data... just look it up. You can pass in a reference to the world or character if needed, too. Methinks you skipped a few parts of the behavior tree tutorial.
  13. IADaveMark

    The blackboards of behavior trees

    This sounds like you are making it a bit more complicated than it should be. Also, there really is little difference between the way a BT process its environment and some other architectures.
  14. IADaveMark

    Hidden information games AI

    Bayesian inference. Not new, but the go-to tech for hidden information.
  15. Yeah, that was based off of occupancy maps by Damian Isla. He thought it was so groovy that he and Christian Baekkelund made Third Eye Crime. At about 11:40 of this, Damian shows off a demo of occupancy maps and describes how it is done. (His first 30 minutes of the lecture is about knowledge representation.) https://gdcvault.com/play/1267/(307)-Beyond-Behavior-An-Introduction Start at 30:30 of this for more about the design of the game, Third Eye Crime based on it: https://gdcvault.com/play/1018057/From-the-Behavior-Up-When
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