Teaching Assistant: Samer Abubaker
Class Location and Time: Mondays, 6:00-9:00PM, Engineering Building, Room J01 (Lecture), and C3 - Complex Adaptive Systems and Computational Intelligence Lab (Labs)
Description: Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. This course is strongly grounded on the foundations of complex systems and theoretical biology. It aims to provide an understanding of the distributed architectures of natural complex systems, and how those are used to produce computational tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on biology, complexity, computer science, informatics, cognitive science, robotics, and cybernetics.
Aims: Students are introduced to fundamental topics in evolutionary systems and bio-inspired computing, and build up their proficiency in the application of various algorithms in real-world problems.
Luis Rocha: Wednesdays 9 - 12am, Engineering Building C3 (Complex Adaptive Systems and Computational Intelligence Lab) or Online, or by appointment
Samer Abubaker: Tuesday: 3:00 pm- 4:00 pm, Wednesday: 12:00 pm- 3:00 pm, and Thursday: 2:00 pm- 3:00 pm, Engineering Building EB K1. Also available for onlinemeetings .
Lab | Assignment |
---|---|
Lab 0 |
Python review (No Assignment) |
Lecture | File & Description |
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Lecture 1 | What is Life? |
Lecture 2 | Life and Information |
Lecture 3 | Uncertainty-based Information and the Logic and Organization of Life |
Aleksander, I. [2002]. "Understanding Information Bit by Bit". In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Grant. |
Cobb, Matthew. [2013]. "1953: When Genes Became 'Information'." Cell 153 (3): 503-506. |
Dennet, D.C. [2005]. "Show me the Science". New York Times, August 28, 2005 |
Gleick, J. [2011]. The Information: A History, a Theory, a Flood. Random House. Chapter 8. |
Langton, C. [1989]. "Artificial Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47. |
James, R., and Crutchfield, J. (2017). Multivariate Dependence beyond Shannon Information. Entropy, 19(10), 531. |
Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 1, pp. 1-23. |
Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77. |
Prokopenko, Mikhail, Fabio Boschetti, and Alex J. Ryan. "An information theoretic primer on complexity, self organization, and emergence." Complexity 15.1 (2009): 11-28. |
Polt, R. [2012]. "Anything but Human". New York Times, August 5, 2012 |
More to be added as class progresses
Langton, C. [1989]. "Artificial Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47. |
Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77. |
More to be added as class progresses |
Floreano, D. and C. Mattiussi [2008]. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press. Available in electronic format for SUNY students. |
Last Modified: January 31, 2023