Artificial life : an overview

Artificial life , a field that seeks to increase the role of synthesis in the study of biological phenomena, has great potential, both for unlocking the secrets of life and for raising a host of disturbing issues--scientific and technical as well as philosophical and ethical. This book brings together a series of overview articles that appeared in the first three issues of the groundbreaking journal Artificial Life , along with a new introduction by Christopher Langton, Editor-in-Chief of Artificial Life , founder of the discipline, and Director of the Artificial Life Program at the Santa Fe Institute.

Foreword

Editor's Introduction

1 Artificial Life as a Tool for Biological Inquiry

Introduction

Brief Survey of Artificial Life Models Applied to Problems in Biology

2.1 The Molecular Level: Wetware Systems

2.2 The Cellular Level: Software Systems

2.3 The Organism Level: Hardware Systems

2.4 Software Life at the Population Level: Equational Models versu s Artificial Life Models

Open Problems in Biology that Are Amenable to Study by Artificia l Life Modeling

3.1 Origin of Life and Self-Organization

3.2 Cultural Evolution

3.3 Origin and Maintenance of Sex

3.4 Shifting Balance Paradigm

3.5 Fitness and Adaptedness

3.6 Structure of Ecosystems

3.7 Mind in Nature

References

Cooperation and Community Structure in Artificial Ecosystems

1 Introduction

2 The Evolution of Cooperation

2.1 The Prisoner's Dilemma

2.2 Evolutionary Dynamics

2.3 Spatial Games

3 Artificial Community Structure

3.1 Food Webs

3.2 Community Models

3.3 Artificial Ecologies

4 Discussion

Acknowledgments

References

Extended Molecular Evolutionary Biology: Artificial Life Bridging the Gap Between Chemistry and Biology

1 Molecular Replication and Template Chemistry

2 Mutation, Error-propagation, and Optimization

3 Mutational Stability of Structures

4 Shape Space Covering

5 Evolutionary Biotechnology

6 The Theory of Evolution and Artificial Life

Acknowledgments

References

Visual Models of Morphogenesis

1 Introduction

2 Features of Models of Morphogenesis

3 Space-Oriented Models

3.1 Reaction-Diffusion Pattern Models

3.2 A Reaction-Diffusion Model of Differentiation

3.3 Diffusion-Limited Accretive Growth

3.4 Diffusion-Limited Aggregation

3.5 Cellular Automata

3.6 Voxel Automata

3.7 Development in Expanding Space

4 Structure-Oriented Models

4.1 L-Systems

4.2 Branching Structures with Exogenous Control

4.3 Map L-Systems

4.4 Mobile Cells in a Continuous Medium

5 Conclusions

Acknowledgments

References

The Artificial Life Roots of Artificial Intelligence

1 Introduction

2 Delineating the Field

2.1 The Subject Matter Is Intelligent Behavior

2.2 The Methodology Is Based on Building Artificial Systems

2.3 Behavior-Oriented Al Is Strongly Influenced by Biology

2.4 Behavior-Oriented AI is Complementary to Other Approaches to A

2.5 The Rest of the Paper Focuses on Emergence

3 Behavior Systems

3.1 Behavior Systems Should Be Viewed as Living Systems

3.2 Some Guidelines Are Known for Designing Behavior Systems

3.3 Different Approaches Are Explored for Designing the Behavior P rograms

3.3.1 Neural Networks Approaches

3.3.2 Algorithmic Approaches

3.3.3 Circuit Approaches

3.3.4 Dynamics Approaches

4 Emergent Behavior

4.1 Emergence Can Be Defined in Terms of the Need for New Descript ive Categories

4.2 The Most Basic Form of Emergent Behavior Is Based on Side Effe cts

4.3 A Second Form of Emergent Behavior Is Based on Spatiotemporal Structures

5 Emergent Functionality

5.1 There Are Severe Difficulties in Using Existing Artificial Ne ural Network Techniques or Evolutio...

5.2 A Selectionist Approach May Be the Key for Generating Emergen t Functionality

6 Conclusions

Some Open Issues

Acknowledgment

References

Toward Synthesizing Artificial Neural Networks that Exhibit Cooperative Intelligent Behavior: Some Open Issues in Artificial Life

1 Introduction

2 Al Versus AL Approach to Cognition

3 Animal Intelligence: Open Questions in AL

3.1 Common Behaviors in Animals

3.1.1 Social Grouping

3.1.2 Specialization of Labor

3.1.3 Food Finding, Preparation, and Storage

3.1.4 Symbiotic Behavior

3.1.5 Dominance, Combat, and Territoriality

3.1.6 Mate Selection and Mating

3.1.7 Nesting

3.1.8 Parenting

3.1.9 Predation Strategies

3.1.10 Predator Avoidance and Defense

3.1.11 Dissembling Behaviors

3.1.12 Primitive Tool Use and Culture

3.1.13 Other Complex Behaviors

3.2 Animal Cooperation via Communication

3.2.1 Insect Communication

3.2.2 Avian Communication

3.2.3 Mammalian Communication

3.2.4 Primate Communication

3.2.5 Cross-Species Communication

3.3 Animal Development and Learning

4 Synthesizing Animal Intelligence via Evolution and Learning

4.1 Evolution/Learning of Food Discrimination

4.2 Evolution of Foraging and Trail Laying

4.3 Evolution of Communication

4.4 Evolution of Predation and Predator Avoidance

4.5 Toward the Synthesis of Protohuman Intelligence

5 Other Research Issues and Methodological Principles

5.1.1 Principle of Hypothesis-Driven

Abstraction Hierarchies

5.1.2 Principle of Minimal Effective Embodiment

5.1.3 Principle of Midpoint Entry

5.1.4 Principle of Indirectness

5.1.5 Principle of Naturalness

6 Conclusions

Acknowledgment

References

Modeling Adaptive Autonomous Agents

1 Introduction

2 What is an Adaptive Autonomous Agent?

3 Guiding Principles

4 Characteristics of Agent Architectures

4.1 Task-Oriented Modules

4.2 Task-Specific Solutions

4.3 Role of Representations is Deemphasized

4.4 Decentralized Control Structure

4.5 Goal-Directed Activity is an Emergent Property

4.6 Role for Learning and Development

5 Some Example Autonomous Agents

5.1 A Mobile Robot

5.2 An Interface Agent

5.3 A Scheduling System

6 Overview of the State of the Art

6.1 Action Selection

6.1.1 The Problem

6.1.2 Progress Made

6.1.3 Open Problems

6.2 Learning from Experience

6.2.1 The Problem

6.2.2 Progress Made

6.2.3 Open Problems

7 Conclusions

Acknowledgments

References

Chaos as a Source of Complexity and Diversity in Evolution

1 Complexity, Diversity, and Emergence

2 Edge of Chaos in an Imitation Game: Chaos as a Source of Comple xity

3 Key Concept for the Origin of Complexity and Diversity: Dynamic Clustering in Networks of Chaotic ...

4 Clustering in Hypercubic Coupled Maps: Self-organizing Genetic Algorithms

4.1 I-Bit Clustering

4.2 2-Bit Clustering

4.3 Parity Check Clustering

5 Maintenance of Diversity and Dynamic Stability: Homeochaos

6 Source of Novelty and Growth of Diversity: Open Chaos

7 Beyond Top-Down and Bottom-Up Approaches

Conclusion

Acknowledgments

References

An Evolutionary Approach to Synthetic Biology: Zen and the Art of Creating Life

1 Synthetic Biology

2 Recognizing Life

3 What Natural Evolution Does

3.1 Evolution in Sequence Space

3.2 Natural Evolution in an Artificial Medium

4 The Approach

5 The Computational Medium

6 The Genetic Language

7 Genetic Operators

7.1 Mutations

7.2 Flaws

7.3 Recombination-Sex

7.3.1 The Nature of Sex

7.3.2 Implementation of Digital Sex

7.4 Transposons

8 Artificial Death

9 Operating System

10 Spatial Topology

11 Ecological Context

11.1 The Living Environment

11.2 Diversity

11.3 Ecological Attractors

12 Cellularity

13 Multicellularity

13.1 Biological Perspective-Cambrian Explosion

13.2 Computational Perspective--Parallel Processes

13.3 Evolution as a Proven Route

13.4 Fundamental Definition

13.5 Computational Implementation

13.6 Digital "Neural Networks"--Natural Artificial Intelligence

14 Digital Husbandry

15 Living Together

16 Challenges

Acknowledgments

References

Beyond Digital Naturalism

1 Life and the Organization Problem in Biology

2 Replicator Equations Without Replicators

3 Organizations Must be Constructed

4 Organization--De Arte Combinatoria1

4.1 Constructive Part

4.2 Dynamical Part

4.2.1 Level 0

4.2.2 Level 1

4.2.3 Level 2

4.2.4 Biology

5 A functional perpetuum mobile

6 ALife and Real Life

7 Sources

Acknowledgments

References

Learning About Life

1 Introduction

2 New Ways of Thinking

3 Tools for Learning

4 Learning Experiences

4.1 LEGO/Logo Creatures

4.2 StarLogo Termites

5 Decentralized Thinking

5.1 Positive Feedback Isn't Always Negative

5.2 Randomness Can Help Create Order

5.3 A Flock Isn't a Big Bird

5.4 A Traffic Jam Isn't Just a Collection of Cars

5.5 The Hills are Alive

6 Conclusions

Acknowledgments

References

Book Reviews: Books on Artificial Life and Related Topics

Computer Viruses as Artificial Life

1 Introduction

2 What Is a Computer Virus?

2.1 Related Software

3 Virus Structure and Operation

4 Evolution of Viruses

4.1 First Generation: Simple

4.2 Second Generation: Self-Recognition

4.3 Third Generation: Stealth

4.4 Fourth Generation: Armored

4.5 Fifth Generation: Polymorphic

5 Defenses and Outlook

6 Viruses as Artificial Life

6.1 Viruses as Patterns in Space-Time

6.2 Self-Reproduction of Viruses

6.3 Information Storage of a Self-Representation

6.4 Virus Metabolism

6.5 Functional Interactions with the Virus's Environment

6.6 Interdependence of Virus Parts

6.7 Virus Stability Under Perturbations

6.8 Virus Evolution

6.9 Growth

6.10 Other Behavior

7 Concluding Comments

References

Genetic Algorithms and Artificial Life

1 Introduction

2 Overview of Genetic Algorithms

3 Interactions Between Learning and Evolution

3.1 The Baldwin effect

3.2 Capturing the Baldwin Effect in a Simple Model

3.3 Evolutionary Reinforcement Learning (ERL)

4 Ecosystems and Evolutionary Dynamics

4.1 Echo

4.2 Measuring Evolutionary Activity

5 Learning Classifier Systems

6 Immune Systems

7 Social Systems

8 Open Problems and Future Directions

Acknowledgments

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Summary

Summary

Table of Contents