Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program

Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adéle Ribeiro, James Seale Smith, Alexis Bellot, Tyler Hayes
Proceedings of The First AAAI Bridge Program on Continual Causality, PMLR 208:1-10, 2023.

Abstract

Both of the fields of continual learning and causality investigate complementary aspects of human cognition and are fundamental components of artificial intelligence if it is to reason and generalize in complex environments. Despite the burgeoning interest in investigating the intersection of the two fields, it is currently unclear how causal models may describe continuous streams of data and vice versa, how continual learning may exploit learned causal structure. We proposed to bridge this gap through the inaugural AAAI-23 “Continual Causality” bridge program, where our aim was to take the initial steps towards a unified treatment of these fields by providing a space for learning, discussions, and to build a diverse community to connect researchers. The activities ranged from traditional tutorials and software labs, invited vision talks, and contributed talks based on submitted position papers, as well as a panel and breakout discussions. Whereas materials are publicly disseminated as a foundation for the community: https://www.continualcausality.org, respectively discussed ideas, challenges, and prospects beyond the inaugural bridge are summarized in this retrospective paper.

Cite this Paper

BibTeX
@InProceedings{pmlr-v208-mundt23a, title = {Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program}, author = {Mundt, Martin and Cooper, Keiland W. and Dhami, Devendra Singh and Ribeiro, Ad\'ele and Smith, James Seale and Bellot, Alexis and Hayes, Tyler}, booktitle = {Proceedings of The First AAAI Bridge Program on Continual Causality}, pages = {1--10}, year = {2023}, editor = {Mundt, Martin and Cooper, Keiland W. and Dhami, Devendra Singh and Ribeiro, Adéle and Smith, James Seale and Bellot, Alexis and Hayes, Tyler}, volume = {208}, series = {Proceedings of Machine Learning Research}, month = {07--08 Feb}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v208/mundt23a/mundt23a.pdf}, url = {https://proceedings.mlr.press/v208/mundt23a.html}, abstract = {Both of the fields of continual learning and causality investigate complementary aspects of human cognition and are fundamental components of artificial intelligence if it is to reason and generalize in complex environments. Despite the burgeoning interest in investigating the intersection of the two fields, it is currently unclear how causal models may describe continuous streams of data and vice versa, how continual learning may exploit learned causal structure. We proposed to bridge this gap through the inaugural AAAI-23 “Continual Causality” bridge program, where our aim was to take the initial steps towards a unified treatment of these fields by providing a space for learning, discussions, and to build a diverse community to connect researchers. The activities ranged from traditional tutorials and software labs, invited vision talks, and contributed talks based on submitted position papers, as well as a panel and breakout discussions. Whereas materials are publicly disseminated as a foundation for the community: https://www.continualcausality.org, respectively discussed ideas, challenges, and prospects beyond the inaugural bridge are summarized in this retrospective paper.} }
Endnote
%0 Conference Paper %T Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program %A Martin Mundt %A Keiland W. Cooper %A Devendra Singh Dhami %A Adéle Ribeiro %A James Seale Smith %A Alexis Bellot %A Tyler Hayes %B Proceedings of The First AAAI Bridge Program on Continual Causality %C Proceedings of Machine Learning Research %D 2023 %E Martin Mundt %E Keiland W. Cooper %E Devendra Singh Dhami %E Adéle Ribeiro %E James Seale Smith %E Alexis Bellot %E Tyler Hayes %F pmlr-v208-mundt23a %I PMLR %P 1--10 %U https://proceedings.mlr.press/v208/mundt23a.html %V 208 %X Both of the fields of continual learning and causality investigate complementary aspects of human cognition and are fundamental components of artificial intelligence if it is to reason and generalize in complex environments. Despite the burgeoning interest in investigating the intersection of the two fields, it is currently unclear how causal models may describe continuous streams of data and vice versa, how continual learning may exploit learned causal structure. We proposed to bridge this gap through the inaugural AAAI-23 “Continual Causality” bridge program, where our aim was to take the initial steps towards a unified treatment of these fields by providing a space for learning, discussions, and to build a diverse community to connect researchers. The activities ranged from traditional tutorials and software labs, invited vision talks, and contributed talks based on submitted position papers, as well as a panel and breakout discussions. Whereas materials are publicly disseminated as a foundation for the community: https://www.continualcausality.org, respectively discussed ideas, challenges, and prospects beyond the inaugural bridge are summarized in this retrospective paper.
APA
Mundt, M., Cooper, K.W., Dhami, D.S., Ribeiro, A., Smith, J.S., Bellot, A. & Hayes, T.. (2023). Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program. Proceedings of The First AAAI Bridge Program on Continual Causality, in Proceedings of Machine Learning Research 208:1-10 Available from https://proceedings.mlr.press/v208/mundt23a.html.

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