Artificial Life, or A-Life, is an interdisciplinary major that applies the principles of living systems to achieve novel solutions to complex real-world problems. Students study the scientific, engineering, philosophical and social issues involved in the ability to synthesize life-like behaviors in computers. They will learn how simple, low-level rules can lead to emergent properties and high-level, life-like behavior.

Topics will include studies of the origin of life, self-assembly, growth and development, evolutionary and ecological dynamics, animal and robot behavior, social organization and cultural evolution.

Students will be able to create self-organizing systems that behave like living organisms in a digital environment. Students will be versed in aspects of evolutionary algorithms, agent based models and cellular automata.

What is Artificial Life?

Definition: the re-creation of biological phenomena within computers and other digital means.
Think of humanoid robots, intelligent insects and virtual creatures that can fly spaceships. A-Life scientists model their technology on natural means, such as evolution, insect behavior, cell mutations and migrating bird formations to create "intelligent" hardware and software.

Artificial Life Programming Objectives (BS)

Research and discuss the key historical moments, personalities and innovations of artificial life
Discuss the philosophical, social and ethical implications of the ability to simulate and synthesize life-like entities and behaviors
Research and discuss ways in which creating biological and social processes through programming can change our environment
Define, evaluate and give examples of applications for various artificial life programming methodologies
Design and implement programs that model real-life organisms and situations
Design and implement computer programs and simulations which demonstrate the following artificial life programming methodologies: evolutionary algorithms, agent-based models and cellular automata
Identify and solve problems in areas of prediction algorithms and outcomes analysis
Develop and implement tests to demonstrate algorithm performance and identify strategies to optimize algorithms

Course List and Program Requirements
University Core Curriculum
Faculty