MuLab @QuMuLab

Research lab at Queen's University School of Computing mulab.ai Joined May 2020

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MuLab @QuMuLab

4 years ago

It was an awesome team effort -- with researchers from the University of Basel, IBM T.J. Watson Research, Invitae, Queen’s University, Linkoping University, and University of Freiburg -- and you can read all about it at the contest page: core-challenge.github.io/2022/ 4/4

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MuLab @QuMuLab

4 years ago

...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. 3/

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MuLab @QuMuLab

4 years ago

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. 1/

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MuLab @QuMuLab

4 years ago

Happy to report that planutils snagged 2nd place at the demo track for this year's #ICAPS2022!! Congrats to all the winners, and we'll see you at next year's ICAPS. icaps22.icaps-conference.org/awards#demo

MuLab @QuMuLab

4 years ago

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/demos/#demo-377

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MuLab @QuMuLab

4 years ago

Congrats @san_a_far, and welcome to @PfaffCatherine's Queen's University Soccer Lab and the @QuMuLab ! We're lucky to have you, and can't wait to see the awesome research you start to explore!!

sana_far @san_a_far

4 years ago

I am honored to be a recipient of the @VectorInst Scholarship in Artificial Intelligence 2022-23 at @queensu

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MuLab @QuMuLab

4 years ago

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!

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Tathagata @tchakra2

4 years ago

We recently open-sourced MACQ, a toolkit meant to be your one-stop shop for learning #AI models for sequential decision-making, from observed agent data. Join us at #ICAPS2022 today for a live demo 🤓 with @cjmuise @QuMuLab Link 👉 macq.planning.domains

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MuLab @QuMuLab

4 years ago

Hope to see you there and at future #ICAPS2022 demo sessions!

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MuLab @QuMuLab

4 years ago

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/demos/#demo-376 github.com/AI-Planning/p4…

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MuLab @QuMuLab

4 years ago

Attending #ICAPS2022? Come check out 3 powerhouse demos from the MuLab & friends!! icaps22.icaps-conference.org/demos/ (next session @ 18UTC in just a few min!)

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MuLab @QuMuLab

4 years ago

The improvement in unseen environments jumped from a 16.3% success rate in last year's winner to 36.7%. Not exactly a minor improvement! Congrats to the whole team, and we'll see you for ALFRED @ EAI23!!

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MuLab @QuMuLab

4 years ago

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.

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MuLab @QuMuLab

4 years ago

Very happy to share the news that recent @QueensComputing graduate, Xiaotian Liu, co-advised by @hectorpal (ServiceNow) & @cjmuise (Queen's) has won the #CVPR2022 #EAI22 embodied agent ALFRED challenge: askforalfred.com/EAI22/

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MuLab @QuMuLab

4 years ago

This work not only extends our ability to extract action theories from natural language to the temporal setting, but it further provides a compelling (and general) means of building input-specific prompts for LLM completion. More fun extensions to LLM prompt manipulation to come!

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MuLab @QuMuLab

4 years ago

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!

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MuLab @QuMuLab

4 years ago

The final work to share for a bit (need to get back to writing things up) is an undergraduate thesis by Alexander Beamish on extracting temporal planning theories from natural language.