Ethical Decision Making
TLDR: An approach that enables autonomous systems to comply with ethical theories.
“Ethically Compliant Planning within Moral Communities
Samer Nashed, Justin Svegliato, Shlomo Zilberstein
Conference on AI, Ethics, and Society (AIES)”
“Ethically Compliant Sequential Decision Making — Distinguished Paper Award
Justin Svegliato, Samer Nashed, Shlomo Zilberstein
AAAI Conference on Artificial Intelligence (AAAI)”
“An Integrated Approach to Moral Autonomous Systems
Justin Svegliato, Samer Nashed, Shlomo Zilberstein
European Conference on Artificial Intelligence (ECAI)”
Exception Recovery
TLDR: An approach that enables autonomous systems to detect, identify, and handle exceptions.
“Introspective Autonomous Vehicle Operational Management
Justin Svegliato, Kyle Wray, Stefan Witwicki, Shlomo Zilberstein
US Patent 10,649,453”
“Belief Space Metareasoning for Exception Recovery
Justin Svegliato, Kyle Wray, Stefan Witwicki, Joydeep Biswas, Shlomo Zilberstein
International Conference on Intelligent Robots and Systems (IROS)”
State Abstractions
TLDR: An approach that solves MDPs using partial state abstractions.
“Selecting the Partial State Abstractions of MDPs: A Metareasoning Approach with Deep RL
Justin Svegliato*, Samer Nashed*, Abhinav Bhatia, Shlomo Zilberstein, Stuart Russell
International Conference on Intelligent Robots and Systems (IROS)”
“Solving Markov Decision Processes with Partial State Abstractions
Justin Svegliato*, Samer Nashed*, Matteo Brucato, Connor Basich, Shlomo Zilberstein
International Conference on Robotics and Automation (ICRA)”
Optimal Stopping
TLDR: An approach that estimates the optimal stopping point of anytime planners.
“A Model‑Free Approach to Meta‑Level Control of Anytime Algorithms
Justin Svegliato, Prakhar Sharma, Shlomo Zilberstein
International Conference on Robotics and Automation (ICRA)”
“Meta‑Level Control of Anytime Algorithms with Online Performance Prediction
Justin Svegliato, Kyle Wray, Shlomo Zilberstein
International Joint Conference on Artificial Intelligence (IJCAI)”
Agent-Aware State Estimation
TLDR: An approach that enables autonomous systems to discover their state by observing the behavior of other agents in the environment.
“Agent‑Aware State Estimation for Autonomous Vehicles
Shane Parr*, Ishan Khatri*, Justin Svegliato, Shlomo Zilberstein
International Conference on Intelligent Robots and Systems (IROS)”
Competence Awareness
TLDR: An approach for competence-aware systems that learn their competence and improve their autonomy via human feedback.
“Competence‑Aware Systems
Connor Basich, Justin Svegliato, Kyle Wray, Stefan Witwicki, Joydeep Biswas, Shlomo Zilberstein
Artificial Intelligence Journal (AIJ)”
“Improving Competence via Iterative State Space Refinement
Connor Basich, Justin Svegliato, Allyson Beach, Kyle Wray, Stefan Witwicki, Shlomo Zilberstein
International Conference on Intelligent Robots and Systems (IROS)”
“Learning to Optimize Autonomy in Competence‑Aware Systems
Connor Basich, Justin Svegliato, Kyle Wray, Stefan Witwicki, Joydeep Biswas, Shlomo Zilberstein
International Conference on Autonomous Agents and Multiagent Systems (AAMAS)”
Safe Decision Making
TLDR: An approach that enables autonomous systems to reason about safety.
“Metareasoning for Safe Decision Making in Autonomous Systems
Justin Svegliato, Connor Basich, Sandhya Saisubramanian, Shlomo Zilberstein
International Conference on Robotics and Automation (ICRA)”
Optimal Hyperparameter Tuning
TLDR: An approach that optimizes the hyperparameters of anytime planners.
“Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep RL
Abhinav Bhatia, Justin Svegliato, Samer Nashed, Shlomo Zilberstein
International Conference on Planning and Scheduling (ICAPS)”
“On the Benefits of Randomly Adjusting Anytime Weighted A*
Abhinav Bhatia, Justin Svegliato, Shlomo Zilberstein
Symposium on Combinatorial Search (SoCS)”