Bio Inspired Artificial Intelligence 1st Edition by Director Dario Floreano, Claudio Mattiussi ISBN 0262062712 978-0262062718

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Product details: 

ISBN 10: 0262062712

ISBN 13:  978-0262062718

Author:   Director Dario Floreano, 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. 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.

Table of contents: 

1 Pillars of Evolutionary Theory
2 The Genotype
3 Artificial Evolution
4 Genetic Representations
5 Initial Population
6 Fitness Functions
7 Selection and Reproduction
8 Genetic Operators
9 Evolutionary Measures
10 Types of Evolutionary Algorithms
11 Schema Theory
12 Human-Competitive Evolution
13 Evolutionary Electronics
14 Lessons from Evolutionary Electronics
15 The Role of Abstraction
16 Analog and Digital Circuits
17 Extrinsic and Intrinsic Evolution
18 Digital Design
19 Evolutionary Digital Design
20 Analog Design
21 Evolutionary Analog Design
22 Multiple Objectives and Constraints
23 Design Verification
24 Closing Remarks
25 Suggested Readings
26 The Basic Ingredients
27 Cellular Automata
28 Modeling with Cellular Systems
29 Some Classic Cellular Automata
30 Other Cellular Systems
31 Computation
32 Artificial Life
33 Complex Systems
34 Analysis and Synthesis of Cellular Systems
35 Closing Remarks
36 Suggested Readings
37 Biological Nervous Systems
38 Artificial Neural Networks
39 Neuron Models
40 Architecture
41 Signal Encoding
42 Synaptic Plasticity
43 Unsupervised Learning
44 Supervised Learning
45 Reinforcement Learning
46 Evolution of Neural Networks
47 Neural Hardware
48 Hybrid Neural Systems
49 Closing Remarks
50 Suggested Readings
51 Potential Advantages of a Developmental Representation
52 Rewriting Systems
53 Synthesis of Developmental Systems
54 Evolution and Development
55 Defining Artificial Evolutionary Developmental Systems

56 Evolutionary Rewriting Systems
57 Evolutionary Developmental Programs
58 Evolutionary Developmental Processes
59 Closing Remarks
60 Suggested Readings
61 How Biological Immune Systems Work
62 The Constituents of Biological Immune Systems
63 Lessons for Artificial Immune Systems
64 Algorithms and Applications
65 Shape Space
66 Negative Selection Algorithm
67 Clonal Selection Algorithm
68 Examples
69 Closing Remarks
70 Suggested Readings
71 Behavior in Cognitive Science
72 Behavior in Artificial Intelligence
73 Behavior-Based Robotics
74 Biological Inspiration for Robots
75 Robots as Biological Models
76 Robot Learning
77 Evolution of Behavioral Systems
78 Evolution and Learning in Behavioral Systems
79 Evolution and Neural Development in Behavioral Systems
80 Coevolution of Body and Control
81 Toward Self-Reproduction
82 Simulation and Reality
83 Closing Remarks
84 Suggested Readings
85 Biological Self-Organization
86 Particle Swarm Optimization
87 Ant Colony Optimization
88 Swarm Robotics

89 Coevolutionary Dynamics: Biological Models
90 Artificial Evolution of Competing Systems
91 Artificial Evolution of Cooperation
92 Closing Remarks
93 Suggested Readings

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