...and "plan" is the key term here. We explored the best the @ICAPSConference field has to offer and solved the problems using a combination of SoA planners. We topped the leaderboard in several tracks, and it's a testament to how powerful modern planning systems have become.
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The setting was to define a series of moves that brings a graph with marked nodes to another marking configuration, with every step along the way being an independent set. In the attached image, you can see a 3-step plan.
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Another exciting achievement for the MuLab to report on: earlier this week it was announced at the 2nd Workshop on Combinatorial Reconfiguration that the PARIS team, which included MuLab members @cjmuise and @alice_petrov_ , won several tracks in the inaugural Core Challenge.
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The 2024 @ICAPSConference was announced today to be in Banff, BC, Canada. Artwork already found on the lab whiteboard...
MuLab will be there to represent!
Finally, we have Planning Tech for Planning Pedagogy -- a nifty little method to mark planning assignments /with a planner/. Very effective at detecting bugs in planning models.
icaps22.icaps-conference.org…
github.com/AI-Planning/p4pp
Next we have MACQ -- the Model Acquisition Toolkit. A one-stop-shop for action model acquisition, launching with 4 common/representative techniques, and a full analysis+taxonomy of the subfield.
icaps22.icaps-conference.org…
macq.planning.domains/
First up is Planutils -- a library and package manager that brings the best the planning field has to offer right to your (docker enabled) fingertips! Want to run any commonly used planner with minimal overhead? This is the place to go.
icaps22.icaps-conference.org…
The work is a shining example of what is possible with an appropriately designed neuro-symbolic architecture. Perception and planning are used precisely where it makes sense, and to great effect. A very well-timed "win" for the use of planning techniques with #ICAPS2022 ongoing.
Step 4 dynamically builds a prompt using these "similar" examples and fetches the response from an LLM for the specific query. With some quick post-processing to make sure the generated code is syntactically correct, we're all set!
