Lonnie Chrisman

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7ranked-venue papers

6as first author

0since 2021 · last 1996

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 7 · 6 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorSystems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

3 papers

Reinforcement learning · 43% Learning theory · 28% Trustworthy machine learning · 28%

Topics — the 3 heaviest of 4, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Reinforcement learning › partially observable reinforcement learning perceptual aliasing	1	1992	Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach · AAAI 1992
Machine learning › Trustworthy machine learning › fairness bias evaluation	1	1989	Evaluating Bias During Pac-Learning · ML 1989
Machine learning › Learning theory PAC learning	1	1989	Evaluating Bias During Pac-Learning · ML 1989

Methods — techniques the papers use, named apart from their topics

reinforcement learning · 0.0planning · 0.0PAC learning · 0.0

Year	Publication	Venue	Position
1996	Independence with Lower and Upper Probabilities Lonnie Chrisman	UAI	1
1996	Propagation of 2-Monotone Lower Probabilities on an Undirected Graph Lonnie Chrisman	UAI	1
1995	Experience with rover navigation for lunar-like terrains abstract Reliable navigation is critical for a lunar rover, both for autonomous traverses and safeguarded remote teleoperation. This paper describes an implemented system that has autonomously driven a prototype wheeled lunar rover over a kilometer in natural, outdoor terrain. The navigation system uses stereo terrain maps to perform local obstacle avoidance, and arbitrates steering recommendations from both the user and the rover. The paper describes the system architecture, each of the major components, and the experimental results to date. Reid G. Simmons, Eric Krotkov, Lonnie Chrisman, Fábio G. Cozman, Richard Goodwin, Martial Hebert, Lalitesh Katragadda, Sven Koenig, Gita Krishnaswamy, Yoshikazu Shinoda, William Whittaker, Paul R. Klarer	IROS (1)	3
1995	Incremental conditioning of lower and upper probabilities Lonnie Chrisman	Int. J. Approx. Reason.	1
1992	Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach Lonnie Chrisman	AAAI	1
1991	Sensible Planning: Focusing Perceptual Attention Lonnie Chrisman, Reid G. Simmons	AAAI	1
1989	Evaluating Bias During Pac-Learning Lonnie Chrisman	ML	1