138 citations · 223 across the 13 of their papers we have counts for
14 papers
Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments
Jonathon Schwartz, Hanna Kurniawati, Marcus Hutter
The design of autonomous agents that can interact effectively with other agents without prior coordination is a core problem in multi-agent systems. Type-based reasoning methods ac…
A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees
Marcus Hoerger, Hanna Kurniawati, Dirk Kroese +1
The Partially Observable Markov Decision Process (POMDP) provides a principled framework for decision making in stochastic partially observable environments. However, computing goo…
Adaptive Discretization using Voronoi Trees for Continuous POMDPs
Marcus Hoerger, Hanna Kurniawati, Dirk Kroese +1
Solving continuous Partially Observable Markov Decision Processes (POMDPs) is challenging, particularly for high-dimensional continuous action spaces. To alleviate this difficulty,…
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…
Partially Observable Markov Decision Processes (POMDPs) and Robotics
Hanna Kurniawati
Planning under uncertainty is critical to robotics. The Partially Observable Markov Decision Process (POMDP) is a mathematical framework for such planning problems. It is powerful…
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…
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…
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…
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…
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…
Partially Observable Planning and Learning for Systems with Non-Uniform Dynamics
Nicholas Collins, Hanna Kurniawati
We propose a neural network architecture, called TransNet, that combines planning and model learning for solving Partially Observable Markov Decision Processes (POMDPs) with non-un…
Autonomous Penetration Testing using Reinforcement Learning
Jonathon Schwartz, Hanna Kurniawati
Penetration testing (pentesting) involves performing a controlled attack on a computer system in order to assess it's security. Although an effective method for testing security, p…
A Stochastic Process Model of Classical Search
Dimitri Klimenko, Hanna Kurniawati, Marcus Gallagher
Among classical search algorithms with the same heuristic information, with sufficient memory A* is essentially as fast as possible in finding a proven optimal solution. However, i…
Analysis of Asymptotically Optimal Sampling-based Motion Planning Algorithms for Lipschitz Continuous Dynamical Systems
Georgios Papadopoulos, Hanna Kurniawati, Nicholas M. Patrikalakis
Over the last 20 years significant effort has been dedicated to the development of sampling-based motion planning algorithms such as the Rapidly-exploring Random Trees (RRT) and it…