Nate Derbinsky
nate.derbinsky@gmail.com
Cambridge, MA
Education
Experience
Publications
Simulating a Logistics Enterprise Using an Asymmetrical Wargame Simulation with Soar Reinforcement Learning and Coevolutionary Algorithms
Proc. of the Genetic and Evolutionary Computation Conference Companion (GECCO), 1907-1915. Online. (2021)
Ying Zhao, Erik Hemberg, Nate Derbinsky, Gabino Mata, Una-May O'Reilly. [paper] [doi] [bib]
Leverage Artificial Intelligence to Learn, Optimize, and Wargame (LAILOW) for Navy Ships
Proc. of the 18th Annual Acquisition Research Symposium (ARS), 353-368. Online. (2021)
Ying Zhao, Gabino Mata, Erik Hemberg, Una-May O'Reilly, Nate Derbinsky, Bruce Cormany, Joy Allen, Andrew Haley, Adam Hilliard. [paper] [webinar] [dair] [bib]
Model AI Assignments 2019
Proc. of the Ninth Symposium on Educational Advances in Artificial Intelligence (EAAI), 9751-9753. Honolulu, HI. (2019)
Todd W. Neller, Raja Sooriamurthi, Michael Guerzhoy, Lisa Zhang, Paul Talaga, Christopher Archibald, Adam Summerville, Joseph Osborn, Cinjon Resnick, Avital Oliver, Surya Bhupatiraju, Kumar A. Krishna, Nate Derbinsky, Laney Strange, Marion Neumann, Jonathan Chen, Zac Christensen, Michael Wollowski, Oscar Youngquist. [paper] [talk] [materials] [doi] [bib]
Continual and Real-time Learning for Modeling Combat Identification in a Tactical Environment
NeurIPS 2018 Workshop on Continual Learning. Montreal, Canada. (2018)
Ying Zhao, Nate Derbinsky, Lydia Wong, Jonah Sonnenshein, Tony Kendall. [paper] [poster] [bib]
Model AI Assignments 2018
Proc. of the Eighth Symposium on Educational Advances in Artificial Intelligence (EAAI), 7959-7960. New Orleans, LA. (2018)
Todd W. Neller, Zack Buttler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth. [paper] [talk] [materials] [bib]
Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning
Proc. of the 9th International Joint Conference on Knowledge Discovery, Engineering and Management (KEOD), 233-238. Funchal, Portugal. (2017)
Ying Zhao, Emily Mooren, Nate Derbinsky. [paper] [doi] [bib]
Graduate School Preparation within an Undergraduate Program (Work in Progress)
Proc. of the 124th American Society for Engineering Education Annual Conference & Exposition (ASEE). Columbus, OH. (2017)
Aaron Carpenter, Nate Derbinsky, Yugu Yang-Keathley, Durga Suresh-Menon. [paper] [doi] [bib]
Sustainable Methods for Impactful Service Learning in Computer Science
Proc. of the 48th ACM Technical Symposium on Computing Science Education (SIGCSE), 723-723. Seattle, WA. (2017)
Nate Derbinsky, Durga Suresh-Menon. [proposal] [abstract] [doi] [bib]
Cornhole: A Widely-Accessible AI Robotics Task
Proc. of the Seventh Symposium on Educational Advances in Artificial Intelligence (EAAI), 4771-4774. San Francisco, CA. (2017)
Nate Derbinsky, Tyler Frasca. [paper] [talk] [doi] [repo] [bib]
Architectural Mechanisms for Mitigating Uncertainty during Long-Term Declarative Knowledge Access
Proc. of the Fourth Annual Conference on Advances in Cognitive Systems (ACS). Evanston, IL. (2016)
Justin Li, Steven Jones, Shiwali Mohan, Nate Derbinsky. [paper] [poster] [conf] [bib]
Testing fine-grained parallelism for the ADMM on a factor-graph
Proc. of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (PCO), 835-844. Chicago, IL. (2016)
Ning Hao, AmirReza Oghbaee, Mohammad Rostami, Nate Derbinsky, José Bento. [paper] [talk] [arXiv] [doi] [repo] [bib]
Testing fine-grained parallelism for the ADMM on a factor-graph
GPU Technology Conference (GTC). San Jose, CA. (2016)
Ning Hao, AmirReza Oghbaee, Mohammad Rostami, Nate Derbinsky, José Bento. [poster] [bib]
A Comparison of Supervised Learning Algorithms for Telerobotic Control using Electromyography Signals
Proc. of the 30th AAAI Conference on Artificial Intelligence (AAAI), 4208-4209. Phoenix, AZ. (2016)
Tyler Frasca, Antonio G. Sestito, Craig Versek, Douglas E. Dow, Barry C. Husowitz, Nate Derbinsky. [paper] [poster] [doi] [bib]
Model AI Assignments 2016
Proc. of the Sixth Symposium on Educational Advances in Artificial Intelligence (EAAI), 4139-4140. Phoenix, AZ. (2016)
Todd W. Neller, Laura E. Brown, James B. Marshall, Lisa Torrey, Nate Derbinsky, Andrew A. Ward, Thomas E. Allen, Judy Goldsmith, Nahom Muluneh. [paper] [talk] [materials] [doi] [bib]
SPARTA: Fast Global Planning of Collision-Avoiding Robot Trajectories Contributed Talk
NIPS 2015 Workshop on Learning, Inference and Control of Multi-Agent Systems. Montreal, Canada. (2015)
Charles J. Mathy, Felix Gonda, Dan Shmidt, Nate Derbinsky, Alexander A. Alemi, José Bento, Francesco M. Delle Fave, Jonathan S. Yedidia. [paper] [poster] [bib]
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
Proc. of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2864-2870. Austin, TX. (2015)
Charles J. Mathy, Nate Derbinsky, José Bento, Jonathan Rosenthal, Jonathan S. Yedidia. [paper] [talk] [arXiv] [doi] [bib]
Proximal Operators for Multi-Agent Path Planning
Proc. of the 29th AAAI Conference on Artificial Intelligence (AAAI), 3657-3663. Austin, TX. (2015)
José Bento, Nate Derbinsky, Charles J. Mathy, Jonathan S. Yedidia. [paper] [arXiv] [doi] [bib]
Scalable Methods to Integrate Task Knowledge with the Three-Weight Algorithm for Hybrid Cognitive Processing via Optimization Invited Paper
Biologically Inspired Cognitive Architectures, 8, 107-117. (2014)
Nate Derbinsky, José Bento, Jonathan S. Yedidia. [paper] [doi] [repo] [bib]
A Message-Passing Algorithm for Multi-Agent Trajectory Planning
Advances in Neural Information Processing Systems 26 (NIPS), 521-529. Lake Tahoe, NV. (2013)
José Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia. [paper] [arXiv] [doi] [bib]
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing
Papers from the 2013 AAAI Fall Symposium Series: Integrated Cognition (Disney Inventor Award), 17-24. Arlington, VA. (2013)
Nate Derbinsky, José Bento, Jonathan S. Yedidia. [paper] [talk] [arXiv] [bib]
Effective and Efficient Forgetting of Learned Knowledge in Soar's Working and Procedural Memories
Cognitive Systems Research, 24, 104-113. (2013)
Nate Derbinsky, John E. Laird. [paper] [doi] [bib]
An Improved Three-Weight Message-Passing Algorithm
arXiv:1305.1961 [cs.AI] (2013)
Nate Derbinsky, José Bento, Veit Elser, Jonathan S. Yedidia. [paper] [arXiv] [bib]
Resource-Efficient Methods for Feasibility Studies of Scenarios for Long-Term HRI Studies
Proc. of the 6th International Conference on Advances in Computer-Human Interactions (ACHI), 95-100. Nice, France. (2013)
Nate Derbinsky, Wan C. Ho, Ismael Duque, Joe Saunders, Kerstin Dautenhahn. [paper] [bib]
Online Determination of Value-Function Structure and Action-value Estimates for Reinforcement Learning in a Cognitive Architecture
Advances in Cognitive Systems, 2, 221-238. (2012)
John E. Laird, Nate Derbinsky, Miller Tinkerhess. [paper] [bib]
A Multi-Domain Evaluation of Scaling in a General Episodic Memory
Proc. of the 26th AAAI Conference on Artificial Intelligence (AAAI), 193-199. Toronto, Canada. (2012)
Nate Derbinsky, Justin Li, John E. Laird. [paper] [talk] [poster] [doi] [bib]
Functional Interactions between Memory and Recognition Judgments
Proc. of the 26th AAAI Conference on Artificial Intelligence (AAAI), 228-234. Toronto, Canada. (2012)
Justin Li, Nate Derbinsky, John E. Laird. [paper] [doi] [bib]
Exploring Reinforcement Learning for Mobile Percussive Collaboration
Proc. of the 12th International Conference on New Interfaces for Musical Expression (NIME), 70-75. Ann Arbor, MI. (2012)
Nate Derbinsky, Georg Essl. [paper] [talk] [bib]
Demonstrations of Multiple Architectural Capabilities
The Soar Cognitive Architecture, pp. 287-306, MIT Press, Cambridge, MA. (2012)
John E. Laird, Nate Derbinsky, Nicholas A. Gorski, Samuel Wintermute, Joseph Xu. [publisher] [amazon] [bib]
Episodic Memory
The Soar Cognitive Architecture, pp. 225-246, MIT Press, Cambridge, MA. (2012)
John E. Laird, Andrew Nuxoll, Nate Derbinsky. [publisher] [amazon] [bib]
Semantic Memory
The Soar Cognitive Architecture, pp. 203-224, MIT Press, Cambridge, MA. (2012)
John E. Laird, Yongjia Wang, Nate Derbinsky. [publisher] [amazon] [bib]
Competence-Preserving Retention of Learned Knowledge in Soar's Working and Procedural Memories
Proc. of the 11th International Conference on Cognitive Modeling (ICCM), 205-210. Berlin, Germany. (2012)
Nate Derbinsky, John E. Laird. [paper] [talk] [bib]
Computationally Efficient Forgetting via Base-Level Activation Best Poster Award
Proc. of the 11th International Conference on Cognitive Modeling (ICCM), 109-110. Berlin, Germany. (2012)
Nate Derbinsky, John E. Laird. [paper] [poster] [bib]
Algorithms for Scaling in a General Episodic Memory (Extended Abstract)
Proc. of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 1387-1388. Valencia, Spain. (2012)
Nate Derbinsky, Justin Li, John E. Laird. [paper] [poster] [bib]
Effective and Efficient Management of Soar's Working Memory via Base-Level Activation
Papers from the 2011 AAAI Fall Symposium Series: Advances in Cognitive Systems (ACS), 82-89. Arlington, VA. (2011)
Nate Derbinsky, John E. Laird. [paper] [talk] [bib]
A Case Study in Integrating Probabilistic Decision Making and Learning in a Symbolic Cognitive Architecture: Soar Plays Dice
Papers from the 2011 AAAI Fall Symposium Series: Advances in Cognitive Systems (ACS), 162-169. Arlington, VA. (2011)
John E. Laird, Nate Derbinsky, Miller Tinkerhess. [paper] [bib]
A Functional Analysis of Historical Memory Retrieval Bias in the Word Sense Disambiguation Task
Proc. of the 25th AAAI Conference on Artificial Intelligence (AAAI), 663-668. San Francisco, CA. (2011)
Nate Derbinsky, John E. Laird. [paper] [talk] [doi] [bib]
Cognitive Architecture in Mobile Music Interactions
Proc. of the 11th International Conference on New Interfaces for Musical Expression (NIME), 104-107. Oslo, Norway. (2011)
Nate Derbinsky, Georg Essl. [paper] [poster] [bib]
A Preliminary Functional Analysis of Memory in the Word Sense Disambiguation Task
Proc. of the 2nd Symposium on Human Memory for Artificial Agents (AISB), 25-29. York, United Kingdom. (2011)
Nate Derbinsky, John E. Laird. [paper] [talk] [bib]
Performance Evaluation of Declarative Memory Systems in Soar
Proc. of the 20th Behavior Representation in Modeling and Simulation Conference (BRIMS), 33-40. Sundance, UT. (2011)
John E. Laird, Nate Derbinsky, Jonathan Voigt. [paper] [bib]
Towards Efficiently Supporting Large Symbolic Declarative Memories
Proc. of the 10th International Conference on Cognitive Modeling (ICCM), 49-54. Philadelphia, PA. (2010)
Nate Derbinsky, John E. Laird, Bryan Smith. [paper] [talk] [bib]
Extending Soar with Dissociated Symbolic Memories
Proc. of the 1st Symposium on Human Memory for Artificial Agents (AISB), 31-37. Leicester, United Kingdom. (2010)
Nate Derbinsky, John E. Laird. [paper] [talk] [bib]
Exploring the Space of Computational Memory Models
Proc. of the 1st Symposium on Human Memory for Artificial Agents (AISB), 38-41. Leicester, United Kingdom. (2010)
Nate Derbinsky, Nicholas A. Gorski. [paper] [talk] [bib]
Efficiently Implementing Episodic Memory
Proc. of the 8th International Conference on Case-Based Reasoning (ICCBR), 403-417. Seattle, WA. (2009)
Nate Derbinsky, John E. Laird. [paper] [talk] [doi] [bib]
A Year of Episodic Memory Invited Paper
Proc. of the Workshop on Grand Challenges for Reasoning from Experiences (IJCAI), 7-10. Pasadena, CA. (2009)
John E. Laird, Nate Derbinsky. [paper] [talk] [bib]
Patents
Funding
Awards
Teaching & Advising
2021
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2019
2018
2017
2016
2015
2014
2013
2011
2010
2009
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2005
Service
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2005
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