Results 101 to 110 of about 35,395 (320)

Revisiting a long‐overlooked skull: Implications for the distribution of Dinodontosaurus brevirostris (Kannemeyeriiformes) in the Brazilian Triassic

The Anatomical Record, EarlyView.
Abstract Dicynodonts (Anomodontia: Dicynodontia) were one of the main groups of terrestrial tetrapods in Permian and Triassic faunas. In Brazil, the genus Dinodontosaurus is one of the most common tetrapod taxon in the Triassic Santa Maria Supersequence. This genus has a complex taxonomic history and is represented in the Triassic of both Argentina and
Julia Lara Rodrigues de Souza, João Lucas Da Silva, Voltaire D. P. Neto, Arielli Fabrício Machado, Juan A. Escobar, Felipe L. Pinheiro   +5 more
wiley   +1 more source

Interference Coordination Based on POMDP in Multi-Cell OFDMA System

Dianxin kexue, 2013
The inter-cell interference coordination（ICIC）of OFDMA system was studied，and a dynamic ICIC algorithm based on the theory of partially observable Markov decision process（POMDP）was proposed.The statistic model of interference and SINR of channel was ...
Qiang Wei, Tao Yang, Hui Feng, Bo Hu
doaj   +2 more sources

When to monitor or control: Informed invasive species management using a partially observable Markov decision process (POMDP) framework

Methods in Ecology and Evolution
Resource allocation for invasive species management requires information about the size of the invasive population, which may be expensive and time‐consuming to obtain.
Thomas K. Waring, Vera L. J. Somers, Michael A. McCarthy, Christopher M. Baker   +3 more
doaj   +1 more source

Sampling-Based Path Planning and Semantic Navigation for Complex Large-Scale Environments

Robotics
This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments.
Shakeeb Ahmad, James Sean Humbert
doaj   +1 more source

A Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes

IEEE Access, 2018
In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le, Ngo Anh Vien, TaeChoong Chung   +2 more
doaj   +1 more source

When to monitor or control; informed invasive species management using a partially observable Markov decision process (POMDP) framework

, 2023
Thomas Waring, Vera L. J. Somers, Michael A. McCarthy, Christopher M. Baker   +3 more
openalex   +2 more sources

An ontological morphological phylogenetic framework for living and extinct ray‐finned fishes (Actinopterygii)

The Anatomical Record, EarlyView.
Abstract The ray‐finned fishes include one out of every two species of living vertebrates on Earth and have an abundant fossil record stretching 380 million years into the past. The division of systematic knowledge of ray‐finned fishes between paleontologists working on extinct animals and neontologists studying extant species has obscured the ...
Jack Stack
wiley   +1 more source

Design and Application of an Intelligent Decision-Making System for Unmanned Aerial Vehicles Based on Deep Reinforcement Learning

IEEE Access
This paper uses deep reinforcement learning algorithms to achieve intelligent decision-making for unmanned aerial vehicles (UAVs) to realize autonomous obstacle avoidance and target tracking of UAVs.
Xiaobo Song
doaj   +1 more source

Centralized Patrolling With Weakly-Coupled Agents Using Monte Carlo Tree Search

IEEE Access, 2019
In this paper, a new optimal multi-agent continuous patrol algorithm is proposed to solve the information gathering problem in dynamic environments. First, the environment is modeled as a layout graph with information attached to vertices.
Xin Zhou, Weiping Wang, Yifan Zhu, Tao Wang, Bo Zhang   +4 more
doaj   +1 more source

Quantitative access control with partially-observable Markov decision processes

Proceedings of the second ACM conference on Data and Application Security and Privacy, 2012
We study observation-based strategies for partially-observable Markov decision processes (POMDPs) with parity objectives. An observationbased strategy relies on partial information about the history of a play, namely, on the past sequence of observations.
Fabio Martinelli, Charles Morisset
openaire   +4 more sources