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About leopardpm

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  1. really interesting, Dave! I have always been a fan of influence maps (variants of Flow Field, Distance Maps, etc) in their power for pathing large numbers of agents. You have gone much further and utilized the information in a way which brings about recognizable behaviors without adding an AI system - very impressive. As you hinted, combining these with an AI system would seem to be very powerful and lightweight. Question: Have you found a better (memory or speed) method of storing your influence maps beyond a basic array? Although using the info in an array is exceptionally fast, I am always concerned about the amount of time it takes to re-generate the maps. I have used my own hand-rolled 'brushfire' algo to regenerate partial maps (only the parts that have changed), But these videos of yours seem to show each agent with their own influence maps which are being regenerated with every movement of that agent. Seems like it could get overloaded quickly in scaled up version - am I right? or is the map creation pretty trivial even at scale? I assume there might be one general purpose 'static obstacle' map utilized by all agents for basic pathing, but the threat maps and ally maps are fully dynamic and need to be entirely recreated basically each cycle or so...right? What am I missing in my thoughts here... I will look at your other provided links also to see examples of combining with an AI system to see exactly how you do the 'combining' - I am gathering each AI definition will have the methods of utilizing the available influence map information hardcoded into the AI method itself (though, I guess you could make it data-driven just as easily too - which is something I note you like to do in order to make your software more generalized, powerful and applicable to different situations.
  2. leopardpm

    FSM, BT, HTN, Goap, other

    I am both a big fan of DaveMark, and, a fan of GOAP - though it has some severe limitations which Dave points out. I think of GOAP as a 'Dynamic Script Generator' - given a particular Goal, a GOAP routine will search through all possible strings of possible Actions to find the 'best' (as defined by the programmer) one to use in the current situation. There are ALOT of ways to make GOAP more efficient (mostly methods to reduce the exponential search space by pre-filtering possible Actions given the situation). But.... I really like Daves' IAUS system, but I don't think that it is useful multi-turn action sequences, especially at the level that GOAP can do it. But, it might be possible to utilize some sort of hybrid which focuses on the best parts of each - during Combat situations, IAUS is an all-around winner... but when talking about more 'simulation' environments (general townspeople doing townie things), GOAP action plans (Dynamic Scripts) work well because they rarely get 'interrupted' and need re-planning. your thoughts, Dave?
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