Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (Intelligent Robotics and Autonomous Agents series)

Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (Intelligent Robotics and Autonomous Agents series)

Dario Floreano, Claudio Mattiussi

Language: English

Pages: 659

ISBN: 0262062712

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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Let us consider the differences between analog and digital signals and their consequences in terms of information content, resistance to noise, and power consumption of the corresponding circuits. The number of distinct signal amplitudes is finite in the digital case and infinite in the analog case. Moreover, analog signals are typically continuous in time, whereas digital signals are discretized also in time. At first sight, the information conveyed by an analog signal is thus potentially infinite,

et al. 1997). 66 1 Evolutionary Systems The evaluation of the behavior of each robot controller belonging to the evolving population is done by starting the robot from a series of 64 predefined positions regularly spaced in the environment (figure 1.27). Each starting position is probed in sequence in a series of trials, each of which lasts until the robot reaches the target object or hits an obstacle or a wall or uses all the allotted time without reaching the target object. The fitness is

that neither of these tactics can guarantee the correctness of an evolved system, since unexpected behaviors of the evolved circuit can manifest themselves in points of the operational envelope that have not been experienced during evolution, although the probability of this happening might be rendered small by extensive testing. To avoid closing this discussion on too pessimistic a note, in considering these difficulties of the evolutionary approach we must not forget that the formal verification

diversity takes place during reproduction. Offspring are copies of selected parents with small variations. This error-prone copy process can generate individuals with new or modified characteristics. Some 4 1 NEUTRAL EVOLUTION Evolutionary Systems of these characteristics will have an effect on the ability of the organism to survive and reproduce. Those new or modified features that give the organism a better ability to cope with the environment with respect to its peers and therefore to

terms of the average dynamical behavior of the CAs and does not refer to the dynamical behavior of each single CA having a given value of λ, some researchers have questioned the validity of this classification (Mitchell et al. 1993; Mitchell 1998). More recently, in a series of papers (Chua et al. 2002, 2003, 2004, 2005) Chua and coworkers have proposed an interesting new approach to the classification of the elementary CAs. The approach is based on the association of an ordinary differential

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