TY - JOUR
T1 - Framework proposal for adaptive mobile intelligent agents
AU - Ruiz-Ledesma, Elena Fabiola
AU - Palma-Orozco, Rosaura
AU - Acosta-Gonzaga, Elizabeth
N1 - Publisher Copyright:
© 2021, Institute of Advanced Engineering and Science. All rights reserved.
PY - 2021/10
Y1 - 2021/10
N2 - Intelligent agents are computational entities which have elements that provide them with the ability to perceive and manipulate their environment: sensors and actuators. These are characterized by displaying various properties that adapt and achieve their objectives. Autonomy, learning, collaboration and reasoning are examples of these properties which together make them intelligent artificial entities. This article shows the development of a framework that has made it possible to speed-up the construction of a system of adaptive mobile intelligent agents, called SySAge. The system agents have integrated search and learning techniques for the execution of automated processes focused on solving search, classification and optimization problems. It has been found that through learning, the agents were able to estimate input parameters and apply them in estimating the shortest route in a graph, considering cost and penalty aspects. To determine the choice of search technique, a probabilistic selection was used. The autonomous behavior of each agent was appreciated through the various attempts to solve the search problem and not to focus the information acquired individually on a single agent.
AB - Intelligent agents are computational entities which have elements that provide them with the ability to perceive and manipulate their environment: sensors and actuators. These are characterized by displaying various properties that adapt and achieve their objectives. Autonomy, learning, collaboration and reasoning are examples of these properties which together make them intelligent artificial entities. This article shows the development of a framework that has made it possible to speed-up the construction of a system of adaptive mobile intelligent agents, called SySAge. The system agents have integrated search and learning techniques for the execution of automated processes focused on solving search, classification and optimization problems. It has been found that through learning, the agents were able to estimate input parameters and apply them in estimating the shortest route in a graph, considering cost and penalty aspects. To determine the choice of search technique, a probabilistic selection was used. The autonomous behavior of each agent was appreciated through the various attempts to solve the search problem and not to focus the information acquired individually on a single agent.
KW - Artificial intelligence
KW - Intelligents agents
KW - Mobile agents
KW - Multi-agent systems
UR - http://www.scopus.com/inward/record.url?scp=85116715116&partnerID=8YFLogxK
U2 - 10.11591/eei.v10i5.2841
DO - 10.11591/eei.v10i5.2841
M3 - Artículo
AN - SCOPUS:85116715116
SN - 2089-3191
VL - 10
SP - 2759
EP - 2770
JO - Bulletin of Electrical Engineering and Informatics
JF - Bulletin of Electrical Engineering and Informatics
IS - 5
ER -