Comportamiento adaptativo
This course is part of Introducción a la inteligencia artificial.
This course explores adaptive behavior mechanisms developed by living organisms to respond to changing environments. Using the synthetic method, students will study various concepts from biology and cybernetics applied to artificial intelligence. The curriculum covers algorithms that enable systems to adapt to complexity and environmental changes, complemented by topics on robustness. Through practical examples and applications, students will understand how adaptive intelligence works in both natural and artificial systems. The course culminates in a final project where participants develop an artificial system exhibiting adaptive behavior, providing hands-on experience with these powerful AI concepts.
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What you'll learn
Understand the fundamental concepts of adaptive behavior in artificial intelligence Analyze self-organization mechanisms that generate adaptation in various systems Explore the complementary relationship between adaptability and robustness Implement biologically-inspired algorithms for adaptive systems Apply adaptive behavior principles to practical problems like urban mobility Develop artificial systems that exhibit adaptive behavior through programming Create and analyze Braitenberg vehicles to demonstrate behavior-based systems Recognize the properties of networks that contribute to system robustness
Skills you'll gain
This course includes:
1.1 Hours PreRecorded video
3 assignments, 1 programming assignment
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There are 4 modules in this course
This course explores adaptive behavior mechanisms in natural and artificial systems across four comprehensive modules. Students begin by examining the historical development of adaptive behavior concepts in animals and machines, including key foundations of modern AI. The second module focuses on self-organization as an adaptation mechanism, covering collective intelligence, swarm intelligence, and synchronization algorithms. Module three explores robustness as a complementary concept to adaptation, introducing antifragility and network properties. The final module demonstrates practical applications, particularly in urban mobility contexts like traffic light coordination and public transportation systems. Throughout the course, students engage with theoretical concepts and apply them in programming assignments, culminating in the development of Braitenberg vehicles that demonstrate adaptive behavior.
Adaptación en animales y máquinas
Module 1 · 1 Hours to complete
Auto-organización
Module 2 · 57 Minutes to complete
Robustez
Module 3 · 1 Hours to complete
Aplicaciones
Module 4 · 5 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Introducción a la inteligencia artificial
Instructor
Complex Systems Expert and Self-Organization Specialist
Dr. Carlos Gershenson serves as a SUNY Empire Innovation Professor at Binghamton University and President of the Complex Systems Society (2024-2027). Previously, he was a research professor at UNAM's Institute for Applied Mathematics and Systems Research (IIMAS), where he led the Self-Organizing Systems Laboratory. His academic credentials include a Ph.D. in Sciences (summa cum laude) from the Free University of Brussels, focusing on self-organizing systems, a Master's in Evolutionary and Adaptive Systems from the University of Sussex, and a Computer Engineering degree from the Arturo Rosenblueth Foundation
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