Stephen Josef Wissow

Photo of Stephen Wissow

I am a doctoral candidate in the Department of Computer Science at the University of New Hampshire where I work with Professor Wheeler Ruml in the UNH Artificial Intelligence Group, and I also collaborate with Dr. Masataro Asai from the MIT-IBM Watson AI Lab in Cambridge, MA. My research interests are in algorithms for planning and autonomy. I completed my MS degree in computer science in 2020 with research in the area of network time synchronization and one-way latency measurement with Professor Radim Bartoš.

See my papers and current research below. You can find my CV here.

Please feel free to reach out! sjw[at]cs[dot]unh[dot]edu / LinkedIn / GitHub

Papers

2024

Stephen Wissow and Masataro Asai, Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning, Proceedings of the Twenty Seventh European Conference on Artificial Intelligence (ECAI-24), 2024.
Stephen Wissow, Fanhao Yu, and Wheeler Ruml, Tunable Suboptimal Heuristic Search, Proceedings of the Seventeenth International Symposium on Combinatorial Search (SoCS-24), 2024. (slides)
Masataro Asai and Stephen Wissow, Extreme Value Monte Carlo Tree Search (Extended Abstract), Proceedings of the Seventeenth International Symposium on Combinatorial Search (SoCS-24), 2024. (slides)
Caleb K. Hill, Stephen Wissow, and Wheeler Ruml, Comparing Planners: Beyond Coverage Tables, Proceedings of the ICAPS-24 Workshop on the International Planning Competition (WIPC-24), 2024. (slides)

2023

Stephen Wissow and Masataro Asai, Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning, Proceedings of the ICAPS-23 Workshop on Heuristics and Search for Domain-Independent Planning (HSDIP-23), 2023. (slides)

Current Research

Tunable Suboptimal Heuristic Search (Fall 2023--present, Prof. Wheeler Ruml): achieving the best empirical cost-vs-time trade-off when tuning suboptimality to achieve desired running time. Comparative analysis of bounded and unbounded suboptimal algorithms, and presentation of a new, robust tunable algorithm that appears to fare better than all others on domains with dead-end states.
Distributional Heuristics: Balancing Exploration and Exploitation in Agile Search (Summer 2022--present, Dr. Masataro Asai): In joint collaboration with Dr. Asai, this work presents a new Multi-Armed Bandit (MAB), UCB1-Normal2, for agile planning (satisficing planning that minimizes planning time with zero regard for solution cost) within a Monte Carlo Tree Search (MCTS) framework that outperforms previous related work and the traditional baseline of Greedy Best-First Search (GBFS) on a range of International Planning Competition (IPC) benchmarks. Future work will evaluate in terms of CPU time instead of expansions, and consider our new distributional heuristic, hFFrand, which exhibits variation in heuristic value on a majority of domains tested (here showing initial state heuristic value for increasingly difficult instances per domain):
Planning with a Known Deadline (Fall 2021--present, Prof. Wheeler Ruml): Investigating online prediction of future search effort in order to take greatest advantage of planning time remaining prior to a known deadline.

Previous Research

Motion Planning with Dynamic Obstacles (Fall 2020--Fall 2021, Prof. Wheeler Ruml): Implementing offline anytime BIT* and integrating into a field-tested ROS-based autonomy stack for a surface vessel for performance comparison with alternative realtime algorithm.
Time Enough: Synchronization for Latency Measurement, MS thesis, University of New Hampshire, 2020 (advisor: Radim Bartoš): Empirical investigation of relative robustness of several network time synchronization methods with respect to asymmetric latency and asymmetric jitter for the purpose of one-way latency measurement.

Class Projects

Planning with a Known Deadline (CS 931: Planning for Robots, Fall 2021, Prof. Wheeler Ruml): Comparing different heuristics and error models for use in a deadline-aware version of beam search.
Data-Driven Dynamics Model for a Boat (CS 850: Machine Learning, Spring 2021, Prof. Marek Petrik): Used k-nearest neighbors to learn a data-driven model from physics-based simulated data as proof-of-concept for use in model-predictive control.

Teaching

As Instructor of Record

CS 659: Introduction to the Theory of Computation (Spring 2022, Spring 2023)
CS 580: Introduction to User Interface Programming/Android (Spring 2019)

As Teaching Assistant

CS 758/858: Introduction to Algorithms (Wheeler Ruml, Fall 2022)
CS 520: Assembly Language Programming and Machine Organization (Fall 2019)
CS 619: Introduction to Object-Oriented Design and Development (Spring 2019)
Programming Assistance Center (Spring 2019—Fall 2019)

As Tutor

CS 410P: Introduction to Scientific Programming/Python (Spring 2019)
CS 417: From Programs to Computer Science (Spring 2019)

As Lab Assistant

CS 515: Data Structures and Introduction to Algorithms (Fall 2018)

Resources

Programming Assistance Center
NP-completeness proof example: traveling salesperson problem (TSP) (from CS 758/858 F22)
Who goes first in a two-player game, 0 or 1:
The numbers generated by this widget come from RANDOM.ORG's true random number generator.