IADaveMark

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About IADaveMark

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    Moderator - Artificial Intelligence

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  1. Yeah, not sure why you would overlay a grid onto something that is inherently edge-based like a road network. In fact, road networks are a dream for pathfinding. It's open areas with arbitrary geometry that are a PITA. That said, there is something to the notion of putting it into a hierarchy. Cluster graphs are the best bet. That's where you find groups that are close together and largely interconnected and treat them as a single "meta node". You then find which meta nodes are connected to each other and store the aggregate distance between them. You can then do a high level pathfind knowing full well that lower level ones will work. I'm oversimplifying, of course, but I'm wondering if you actually need that. Depends on how many intersections (nodes) you have on your map.
  2. Game AI architecture for a RTS game

    That said, there are all sorts of benefits to using a decision tree (or more specifically, a behavior tree) than using a "rule based system". Since you seem very new to game AI (another reason this is a big challenge for you), read the article that Alvaro linked to (it's mine). That will give you an overview of the different types of architectures.
  3. Game AI architecture for a RTS game

    Preach, Brother Alvaro!
  4. If you're going to do it, Alvaro nailed it. Each decision should be a scored utility function for it's worth/desirability. Pick the best one and go. Note that different entities will be doing their own "thinking". e.g. where a unit moves is a different thought process than what should I build next. There is a brief portion of this combo lecture that talks about the AI in XCom: EU. They used utility values for scoring the moves in their tactical turn-based game. (Start at 16:30) https://www.gdcvault.com/play/1018058/AI-Postmortems-Assassin-s-Creed
  5. What are your weights and effective distances for each of those? Increasing avoidance slightly might push the agent around the combined force of the 2 obstacles. Also, that is exactly the problem with a steering only solution, however. This is very similar to the "cul-de-sac" problem, in fact. The go-to solution to get out of stuff like that is pathfinding (e.g. A*). Also, you don't have to align movement to the grid if a straight line exists to a waypoint further down the path.
  6. Incidentally, the Civilization games (which are the staple of turn-based strategy) all have the source code available. (e.g. for Civ 5, you download the SDK) Dig into that and find out how they did all of the above.
  7. Learning Pathfinding

    Don't bother with multi-threading. If almost no game companies use it, there's a damn good reason.
  8. Congrats on tackling one of the hardest genres for AI in games. You could spend an entire lecture series on how to do all of the above. Not terribly conducive to a message board reply. Additionally, if you don't know AI at all, this is going to be a TON of work. I'm talking years. Start your research on AI tech here. (And this doesn't even cover things like pathfindinging at all.) http://intrinsicalgorithm.com/IAonAI/2012/11/ai-architectures-a-culinary-guide-gdmag-article/
  9. Don't do machine learning. If your results with heuristic approaches has been mediocre, then your heuristics were mediocre. This is a viable option when done right. MCTS is not machine learning... it is brute force search. This will certainly be a viable option since it can handle hidden information. As always, however, my initial question to you is... how do you play the game? What information do you take into account when playing? How do you use that information to make your decisions? Therein lies your AI.
  10. The GDC 2018 AI Summit is currently welcoming submissions on AI-related topics such as: Postmortems of the AI in recently released (or soon to be released) games – especially with a focus of “challenges faced… and overcome!” Advancements and improvements in AI architectures (e.g. behavior trees, planners, utility systems, MCTS, etc.) New architectures and approaches for AI-related problems (e.g. data-driven, modular systems, etc.) AI authoring tools Improvements in navigation and avoidance algorithms Animation control through AI systems Multi-agent coordination in tactical, strategic, or social situations Use of AI for content generation in games Use of AI for gameplay management, pacing, etc. Non-traditional uses of AI in game development applications (e.g. tools, debugging, etc.) AI for narrative generation and chatbots AI in VR, mixed reality, and AR Experimental AI designs Learn more at http://www.gdconf.com/news/nows-time-submit-talks-gdc-2018-ai-summit/.
  11. The GDC 2018 AI Summit is currently welcoming submissions on AI-related topics such as: Postmortems of the AI in recently released (or soon to be released) games – especially with a focus of “challenges faced… and overcome!” Advancements and improvements in AI architectures (e.g. behavior trees, planners, utility systems, MCTS, etc.) New architectures and approaches for AI-related problems (e.g. data-driven, modular systems, etc.) AI authoring tools Improvements in navigation and avoidance algorithms Animation control through AI systems Multi-agent coordination in tactical, strategic, or social situations Use of AI for content generation in games Use of AI for gameplay management, pacing, etc. Non-traditional uses of AI in game development applications (e.g. tools, debugging, etc.) AI for narrative generation and chatbots AI in VR, mixed reality, and AR Experimental AI designs Learn more at http://www.gdconf.com/news/nows-time-submit-talks-gdc-2018-ai-summit/. View full story
  12. Navmesh is a perfectly viable solution. Also, the hierarchical system is to speed up pathfinding, not navmesh generation. Typically, the approach for mesh generation on large worlds is to split it into chunks. Not only does this keep your generation from choking your machine, it speeds things up during level changes since you only need to re-do the affected chunk(s). What program are you using to generate your mesh?
  13. Blind Poker Artificial Neural Network

    You have such a thin view of the current state space. Also, your decision space is pretty much guided by "does this card make my hand better?" In THe, the state space is based on your current hand, AND what the other players could make with the board, AND what their betting says about their hand, AND the math of the pot odds for calls or how you can force them into sub-optimal pot odds decisions based on YOUR bet sizing, etc. There is a lot more going on there. Additionally, your decision space is much wider (particularly in no limit hold'em) since bet sizing is such a major part of the game. Now I'm not a fan of using ANNs in hold'em, but that's because I hate the "feeling around in the dark" approach for something that is mathematically definable right from the start. (Note, I'm interested in this... )
  14. Blind Poker Artificial Neural Network

    I got that comment a lot when the article first came out but mostly because the GDMag art staff had a blast with the layout. Mexican food everywhere!
  15. Blind Poker Artificial Neural Network

    Start here... http://intrinsicalgorithm.com/IAonAI/2012/11/ai-architectures-a-culinary-guide-gdmag-article/