Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (Intelligent Robotics and Autonomous Agents series)
Dario Floreano, Claudio Mattiussi
Format: PDF / Kindle (mobi) / ePub
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. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligence -- to mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systems -- including several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading.
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Speciﬁc dimension (temperature, energy, etc.) and the intersection of these ranges deﬁnes the viability space of the organism or artifact (ﬁgure 1.43, left). Consider now a population of randomly generated individuals scattered across the entire space. In the absence of selection pressure by competitors or environmental change, all individuals within the viability space can reproduce, but offspring that fall outside the viability space by the effect of mutations are eliminated. This type of.
Food will have a higher chance of survival and reproduction. Natural selection is the most debated, often misunderstood, and abused pillar of natural evolution. In the engineering community, it is commonly described as selection of the ﬁttest; “ﬁttest” is often associated with “best”; and selective reproduction of the best is often associated with progress. However, organisms are not always selected for how well they score individually. For example, some animal societies maintain a number of.
Boundary conditions that reﬂect and absorb particles can be implemented simply as ﬁxed boundary conditions corresponding, respectively, to the ﬁxed presence or absence of particles in the virtual cells beyond the boundary. Initial conditions. In order to start the updating of the state of the cells of the system according to the transition function it is necessary to specify the initial state of all the cells. This is known as the assignment of the initial condition or seed of the cellular.
The glider gun, a conﬁguration designed by R.W. Gosper that is able to produce a new glider every 30 time steps (ﬁgure 2.16). A fundamental role in Conway’s endeavor was also played by the discovery of the eater, a static structure that is able 123 2.4 Some Classic Cellular Automata 0 1 ... 2 28 29 30 glider t Figure 2.16 A glider gun generates a glider every 30 steps. Note that besides producing the glider the gun regenerates its initial conﬁguration (the two spurious live cells on.
Eventually result in the appearance of new genes. Indeed, it has been argued that gene duplication and diversiﬁcation could play an adaptive role in coping with environmental challenges and may account for the rapid evolution of complexity of invertebrates (Ohno 1970). For example, it has been shown that a duplicated gene can mutate into a new type of functional gene, as in the case of olfactory-receptor genes (Glusman et al. 2001). Artiﬁcial Evolution Artiﬁcial evolution includes a wide set of.