Connor Basich
Research Assistant and PhD Student
University of Massachusetts Amherst
Biography
I am a 6th year PhD student at the University of Massachusetts Amherst in the Resource-Bounded Reasoning Lab advised by Dr. Shlomo Zilberstein.
I work in the areas of planning under uncertainty and stochastic decision making for autonomous agents. My work has focused on designing and analyzing models that lie in the intersection of model-based planning and online learning with the goal of enabling the deployment of robust robotic systems for long-term autonomous decision-making in the open world.
Download my resumé.
Education
PhD in Computer Science, Spring 2023 (Ant.)
University of Massachusetts Amherst
M.S. in Computer Science, 2020
University of Massachusetts Amherst
B.A. in Math and Computer Science, 2017
Washington University in St. Louis
Recent Publications
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(2023).
An Introspective Approach for Competence-Aware Autonomy.
University of Massachusetts Libraries.
(2023).
Competence-Aware Systems.
Artificial Intelligence.
(2023).
Semi-Autonomous Systems with Contexual Competence Awareness.
International Conference on Autonomous Agents and MultiAgent Systems (AAMAS).
(2022).
Introspective competence modeling for AV decision making.
US Patent.
(2022).
A Sampling Based Approach to Robust Planning for a Planetary Lander.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
(2022).
Analyzing the efficacy of flexible execution, replanning, and plan optimization for a planetary lander.
International Conference on Automated Planning and Scheduling (ICAPS).
(2022).
Competence-Aware Path Planning Via Introspective Perception.
IEEE Robotics and Automation Letters (RA-L).
(2022).
Metareasoning for Safe Decision Making in Autonomous Systems.
IEEE International Conference on Robotics and Automation (ICRA).
(2022).
Planning with Intermittent State Observability: Knowing When to Act Blind.
IEEE/RSJ International Conference on Intelligent Robots and Systems.
(2021).
Using Metareasoning to Maintain and Restore Safety for Reliably Autonomy.
International Joint Conference on Artificial Intelligence (IJCAI) R2AW Workshop.
(2021).
A sampling-based optimization approach to handling environmental uncertainty for a planetary lander.
International Conference on Automated Planning and Scheduling (ICAPS) PlanRob Workshop.
(2021).
Planning with Inconsistent Sensors: Knowing When to Act Blind.
International Joint Conference on Artificial Intelligence (IJCAI) R2AW Workshop.
(2021).
Solving Markov decision processes with partial state abstractions.
IEEE International Conference on Robotics and Automation (ICRA).
(2021).
Improving Competence via Iterative State Space Refinement.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
(2020).
Competence-Aware Systems for Long-Term Autonomy.
International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) Doctoral Consortium.
(2020).
Improving Competence for Reliable Autonomy.
European Conference on Artificial Intelligence (ECAI) AREA Workshop.
(2020).
Learning to Optimize Autonomy in Competence-Aware Systems.
International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).
(2020).
Using Flexible Execution, Replanning, and Model Parameter Updates to Address Environmental Uncertainty for a Planetary Lander.
Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation for Space, i-SAIRAS.
(2019).
Satisfying Social Preferences in Ridesharing Services.
IEEE Intelligent Transportation Systems Conference (ITSC).
(2019).
The Value of Incorporating Social Preferences in Dynamic Ridesharing.
International Conference on Automated Planning and Scheduling SPARK Workshop.
Contact
- cbasich@cs.umass.edu