guillaume-chevalier/AI-Planning-Solver-Shakeys-World-PDDL
Solving a planning problem (Shakey's World) with the FF and IPP planners, the PDDL language and some Python meta-programming to glue things together.
This project helps AI planning researchers and students compare the performance of different automated planning algorithms. It takes a problem definition in PDDL (Planning Domain Definition Language) for a simulated robot in 'Shakey's World' and uses different planners to find a sequence of actions to achieve a goal, like tidying a room. The output is a plan (a list of actions) and metrics on how long it took to find it. This is useful for anyone studying or benchmarking AI planning systems.
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Use this if you are an AI planning student or researcher needing to benchmark the Fast Forward (FF) and Interference Progression Planner (IPP) algorithms on a classic problem scaled to varying complexities.
Not ideal if you are looking for a general-purpose, ready-to-deploy AI planning solution for a real-world application, as this is a specific academic benchmark.
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Python
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MIT
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Last pushed
May 28, 2017
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