[HTML][HTML] Deliberation for autonomous robots: A survey
Autonomous robots facing a diversity of open environments and performing a variety of tasks
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …
Asnets: Deep learning for generalised planning
In this paper, we discuss the learning of generalised policies for probabilistic and classical
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …
Relational abstractions for generalized reinforcement learning on symbolic problems
R Karia, S Srivastava - arXiv preprint arXiv:2204.12665, 2022 - arxiv.org
Reinforcement learning in problems with symbolic state spaces is challenging due to the
need for reasoning over long horizons. This paper presents a new approach that utilizes …
need for reasoning over long horizons. This paper presents a new approach that utilizes …
Heuristic search in dual space for constrained stochastic shortest path problems
We consider the problem of generating optimal stochastic policies for Constrained
Stochastic Shortest Path problems, which are a natural model for planning under uncertainty …
Stochastic Shortest Path problems, which are a natural model for planning under uncertainty …
Learning generalized policy automata for relational stochastic shortest path problems
Several goal-oriented problems in the real-world can be naturally expressed as Stochastic
Shortest Path problems (SSPs). However, the computational complexity of solving SSPs …
Shortest Path problems (SSPs). However, the computational complexity of solving SSPs …
Planning under uncertainty using reduced models: Revisiting determinization
L Pineda, S Zilberstein - … of the International Conference on Automated …, 2014 - ojs.aaai.org
We introduce a family of MDP reduced models characterized by two parameters: the
maximum number of primary outcomes per action that are fully accounted for and the …
maximum number of primary outcomes per action that are fully accounted for and the …
I-dual: solving constrained SSPs via heuristic search in the dual space
We consider the problem of generating optimal stochastic policies for Constrained
Stochastic Shortest Path problems, which are a natural model for planning under uncertainty …
Stochastic Shortest Path problems, which are a natural model for planning under uncertainty …
Fast SSP solvers using short-sighted labeling
State-of-the-art methods for solving SSPs often work by limiting planning to restricted
regions of the state space. The resulting problems can then be solved quickly, and the …
regions of the state space. The resulting problems can then be solved quickly, and the …
[HTML][HTML] Depth-based short-sighted stochastic shortest path problems
FW Trevizan, MM Veloso - Artificial Intelligence, 2014 - Elsevier
Abstract Stochastic Shortest Path Problems (SSPs) are a common representation for
probabilistic planning problems. Two approaches can be used to solve SSPs:(i) consider all …
probabilistic planning problems. Two approaches can be used to solve SSPs:(i) consider all …
Distance-penalized active learning via markov decision processes
D Wang, J Lipor, G Dasarathy - 2019 IEEE Data Science …, 2019 - ieeexplore.ieee.org
We consider the problem of active learning in the context of spatial sampling, where the
measurements are obtained by a mobile sampling unit. The goal is to localize the change …
measurements are obtained by a mobile sampling unit. The goal is to localize the change …