activity
20192022
most citedAutonomous Penetration Testing using Reinforcement Learning

62 citations · 68 across the 7 of their papers we have counts for

collaborators

8 papers

cs.AI20221 cited

Adaptive Discretization using Voronoi Trees for Continuous-Action POMDPs

Marcus Hoerger, Hanna Kurniawati, Dirk Kroese +1

Solving Partially Observable Markov Decision Processes (POMDPs) with continuous actions is challenging, particularly for high-dimensional action spaces. To alleviate this difficult…

cs.RO20212 cited

Trust and Safety

S. K. Devitt, R. Horne, Z. Assaad +15

Robotics in Australia have a long history of conforming with safety standards and risk managed practices. This chapter articulates the current state of trust and safety in robotics…

cs.RO20211 cited

An NCAP-like Safety Indicator for Self-Driving Cars

Jimy Cai Huang, Hanna Kurniawati

This paper proposes a mechanism to assess the safety of autonomous cars. It assesses the car's safety in scenarios where the car must avoid collision with an adversary. Core to thi…

cs.AI2020

An On-Line POMDP Solver for Continuous Observation Spaces

Marcus Hoerger, Hanna Kurniawati

Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMD…

cs.RO2020

Non-Linearity Measure for POMDP-based Motion Planning

Marcus Hoerger, Hanna Kurniawati, Alberto Elfes

Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with compl…

cs.RO20192 cited

Multilevel Monte-Carlo for Solving POMDPs Online

Marcus Hoerger, Hanna Kurniawati, Alberto Elfes

Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMD…