File Name: bio-inspired artificial intelligence theories methods and technologies .zip
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Traditionally artificial intelligence has been focused on attempting to replicate the cognitive abilities of the human brain. Alternative approaches to artificial intelligence take inspiration from a wider range of biological processes such as evolution, networks of neurons and learning. In recent decades there has been an explosion of new artificial intelligence methods inspired by even more biological processes, such as the immune system, colonies of ants, physical embodiment, development, coevolution, self-organization, and behavioral autonomy, to mention just a few. As a result, it discusses biological and artificial systems that operate at a wide range of time and space scales, but manages to move fluently from slow evolutionary time, to life-time learning, to real time adaptation. On the space scale, it goes from individual cells and neurons, to multicellular organisms, and all the way to societies. I found this book notable for at least two reasons. First, it provides a coherent intellectual framework to organize all these computational developments by grounding them in their biological nature and in the pervasiveness of evolution throughout biology.
From Intelligent Robotics and Autonomous Agents series. By Dario Floreano and Claudio Mattiussi. A comprehensive introduction to new approaches in artificial intelligence and robotics that are inspired by self-organizing biological processes and structures. New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization.
The ideas from natural and biological activities have provoked the progress of many sophisticated algorithms. These algorithms are broadly categorized as evolutionary computation and swarm intelligence SI algorithms. This section tries to provide a brief idea about some of the contemporary algorithms in the field of bio inspired computing. Add to Cart. Instant access upon order completion. Free Content.
Request PDF | On Jan 1, , Dario Floreano and others published Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies | Find, read and.
Bioinspired intelligent algorithm BIA is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly.
University A to Z Departments. The group carries out a variety of research in the field of evolutionary computing from theory to practice. Particular areas of focus for the group in this area are in Cartesian Genetic Programming, artificial biochemical networks and multi-agent system. Within these areas we concentrate on the methods and the design of evolvable, scalable, biologically-motivated representations. This includes work upon developmental representations, implicit context representations, and multiple chromosome approaches.
Summary: A comprehensive introduction to new approaches in artificial intelligence and robotics that are inspired by self-organizing biological processes and structures. New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning.
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