Controlling an Autonomous Agent for Exploring Unknown Environments using Switching Prelearned Modules

2018 ◽  
Vol 138 (2) ◽  
pp. 157-164
Author(s):  
Takahito Hata ◽  
Masanori Suganuma ◽  
Tomoharu Nagao
Keyword(s):  
Autonomous Agent ◽  
Unknown Environments

Controlling an Autonomous Agent for Exploring Unknown Environments Using Switching Prelearned Modules

2018 ◽  
Vol 101 (5) ◽  
pp. 84-93
Author(s):  
TAKAHITO HATA ◽  
MASANORI SUGANUMA ◽  
TOMOHARU NAGAO
Keyword(s):  
Autonomous Agent ◽  
Unknown Environments

scholarly journals Semantic Active Visual Search System Based on Text Information for Large and Unknown Environments

2021 ◽  
Vol 101 (2) ◽  
Author(s):  
Mathias Mantelli ◽  
Diego Pittol ◽  
Renan Maffei ◽  
Jim Torresen ◽  
Edson Prestes ◽  
...  
Keyword(s):  
Visual Search ◽  
Search System ◽  
Unknown Environments ◽  
Text Information

scholarly journals Housing and road transport modify the brain neurotransmitter systems of pigs: Do pigs raised in different conditions cope differently with unknown environments?

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0210406 ◽  
Author(s):  
Laura Arroyo ◽  
Daniel Valent ◽  
Ricard Carreras ◽  
Raquel Peña ◽  
Josefa Sabrià ◽  
...  
Keyword(s):  
Road Transport ◽  
Neurotransmitter Systems ◽  
Unknown Environments ◽  
Brain Neurotransmitter ◽  
The Brain

Fuzzy Local Navigation of a simulated real robot in unknown environments

2005 ◽  
Author(s):  
J. Gasos ◽  
M.C. Garcia-Alegre ◽  
R.G. Rosa
Keyword(s):  
Unknown Environments ◽  
Local Navigation ◽  
Real Robot

scholarly journals Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  
Keyword(s):  
Species Interactions ◽  
Computational Models ◽  
Human Microbiome ◽  
Agent Based Modeling ◽  
Autonomous Agent ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Interactions ◽  
Mucosal Surfaces ◽  
Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

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