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20172026
most citedOptimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning

34 citations · 173 across the 146 of their papers we have counts for

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Showing 2024 · cs.AIShow all

7 papers · 2 filters

cs.AI2024

More than Marketing? On the Information Value of AI Benchmarks for Practitioners

Amelia Hardy, Anka Reuel, Kiana Jafari Meimandi +6

Public AI benchmark results are widely broadcast by model developers as indicators of model quality within a growing and competitive market. However, these advertised scores do not…

cs.AI2024★ 5 cited

BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices

Anka Reuel, Amelia Hardy, Chandler Smith +3

AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attr…

cs.AI2024

Multi-scale Generative Modeling for Fast Sampling

Xiongye Xiao, Shixuan Li, Luzhe Huang +6

While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models hav…

cs.AI2024

Semi-Markovian Planning to Coordinate Aerial and Maritime Medical Evacuation Platforms

Mahdi Al-Husseini, Kyle H. Wray, Mykel J. Kochenderfer

The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The selection of any one of…

cs.AI2024

ConstrainedZero: Chance-Constrained POMDP Planning using Learned Probabilistic Failure Surrogates and Adaptive Safety Constraints

Robert J. Moss, Arec Jamgochian, Johannes Fischer +2

To plan safely in uncertain environments, agents must balance utility with safety constraints. Safe planning problems can be modeled as a chance-constrained partially observable Ma…

cs.AI2024

Addressing Myopic Constrained POMDP Planning with Recursive Dual Ascent

Paula Stocco, Suhas Chundi, Arec Jamgochian +1

Lagrangian-guided Monte Carlo tree search with global dual ascent has been applied to solve large constrained partially observable Markov decision processes (CPOMDPs) online. In th…