Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Kurniawati, Hanna"'
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 achieve this by maintaining a belief over a set of potential behaviours
Externí odkaz:
http://arxiv.org/abs/2306.06067
The Partially Observable Markov Decision Process (POMDP) provides a principled framework for decision making in stochastic partially observable environments. However, computing good solutions for problems with continuous action spaces remains challen
Externí odkaz:
http://arxiv.org/abs/2305.08049
Solving continuous Partially Observable Markov Decision Processes (POMDPs) is challenging, particularly for high-dimensional continuous action spaces. To alleviate this difficulty, we propose a new sampling-based online POMDP solver, called Adaptive
Externí odkaz:
http://arxiv.org/abs/2302.10439
Solving Partially Observable Markov Decision Processes (POMDPs) with continuous actions is challenging, particularly for high-dimensional action spaces. To alleviate this difficulty, we propose a new sampling-based online POMDP solver, called Adaptiv
Externí odkaz:
http://arxiv.org/abs/2209.05733
Autor:
Kurniawati, Hanna
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 due to its careful quantification of the non-deterministic effects of
Externí odkaz:
http://arxiv.org/abs/2107.07599
Autor:
Huang, Jimy Cai, Kurniawati, Hanna
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 this mechanism is a safety measure, called Safe-Kamikaze Distance (SKD),
Externí odkaz:
http://arxiv.org/abs/2104.00859
Autor:
Hoerger, Marcus, Kurniawati, Hanna
Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantia
Externí odkaz:
http://arxiv.org/abs/2011.02076
Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with complex dynamics. Most state-of-the-art methods perform search that relies
Externí odkaz:
http://arxiv.org/abs/2005.14406
Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantia
Externí odkaz:
http://arxiv.org/abs/1907.09673
Autor:
Collins, Nicholas, Kurniawati, Hanna
We propose a neural network architecture, called TransNet, that combines planning and model learning for solving Partially Observable Markov Decision Processes (POMDPs) with non-uniform system dynamics. The past decade has seen a substantial advancem
Externí odkaz:
http://arxiv.org/abs/1907.04457