Artificial
intelligence in general and software agents in particular are recognized as
computer science disciplines that aim to model or simulate so-called
intelligent human behaviors such as perception, decision-making, understanding,
learning, etc. This work presents an approach to designing a generic
Intelligent Agent that can be used in a multi-agent system to solve a complex
problem. The generic agent that is proposed can be instantiated as a concrete
agent, which is enabled with learning and autonomy capabilities by using
Artificial Neural Networks. To highlight the generic aspect, the proposition is
instantiated to be used in agriculture, health and education. The instantiated
software agent applied in agriculture can process images in real time and
detect defect on plants’ leaf.
In the health field, the agent process image to diagnose breast cancer. When
applied in Education, the agent can load an image of a student’s script and grade it. The
performance of the designed agent system has the same accuracy as that of the
respective neural networks used to instantiate them. In the educational field,
the software agent has an accuracy of 98.9% and in the health field, it has an accuracy of 99.56% while in the
agricultural field, it has an accuracy of 97.2%.
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