Ne

New Pathfinding Algorithm

Hacker News

New Pathfinding Algorithm

Hello HN, I am creating a city builder video game and wanted to figure out a way to efficiently and realistically path hundreds of thousands of agents around the game world, with path preferences that the agents can utilize (such as wanting to walk through china town on their way to their destination) If you're not aware, algorithms like Dijkstra and A* will only return one shortest path between two points, which is not very realistic when you're walking around e.g. Manhattan, NYC and have a binomial coefficient number of possible paths to choose from. The other limitation is that these algorithms wont factor in path preferences (might not be entirely true for A*). So I created an algorithm that can cache all possible shortest paths between two points in N space (so all node pairs cached is N^2 space). Additionally, I figured out a way to store the data in a (C++) vector and maintain constant find/access time, since hash tables have a lot of memory overhead. I wrote up an introduction article (with video proof at the end, though it's my first YouTube video so bear with me ha ha) that will be followed by a technical one when I have time. If you have any tips on how I can put the algorithm through the ringer, and how I could create a white paper if successful, I would greatly appreciate it! I don't have any academic connections so I'm not sure who I could reach out to professionally. Link: https://www.yesboxstudios.com/2022/04/27/all-nck-shortest-paths-in-near-optimal-time-and-space-complexity-introduction/

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios, efficiently · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, video, way · Missing: mobile apps, personal, entrepreneurs
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, builder · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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