Neuro-symbolic AI
We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence. By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we're aiming to create a revolution in AI, rather than an evolution.
Our work
Getting AI to reason: using neuro-symbolic AI for knowledge-based question answering
ResearchAI, you have a lot of explaining to do
ReleaseIBM, MIT and Harvard release “Common Sense AI” dataset at ICML 2021
ReleaseMimicking the brain: Deep learning meets vector-symbolic AI
ResearchIBM-Stanford team’s solution of a longstanding problem could greatly boost AI
Research
Tools + code
Transition-based AMR Parser
Transition-based parser for Abstract Meaning Representation (AMR) in Pytorch.
View project ↗IBM Hyperlinked Knowledge Graph
A set of libraries to provide an abstraction layer to access the endpoints of databases that store IBM Hyperlinked Knowledge Graphs.
View project ↗Forbid-Iterative Planner
Forbid-Iterative Planner is an automated PDDL-based planner that includes planners for top-k, top-quality, and diverse computational tasks.
View project ↗ITOPS: an ontology for IT Operations
Ontology source files for the IT Operations Ontology derived from Wikidata, DBPedia and Wikipedia.
View project ↗Classical planner FD-Novelty-PO
Code for IJCAI 2021 paper “The Fewer the Merrier: Pruning Preferred Operators with Novelty”
View project ↗
Publications
Alexander Tuisov, Michael Katz2021IJCAI 2021
Shirin Sohrabi Araghi, Michael Katz, et al.2021IJCAI 2021
Daniel Fišer, Daniel Gnad, et al.2021IJCAI 2021
Cameron Allen, Michael Katz, et al.2021IJCAI 2021
Djallel Bouneffouf, Raphael Feraud, et al.2021IJCAI 2021
Jiayuan Mao, Zhezheng Luo, et al.2021IJCAI 2021
