Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Distributed Intelligent Systems: Self-Organization and Foraging Strategies

In course

ENG-466: Distributed intelligent systems

The goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing and optimizing coordination strategies. The course is a well-balanced mixture of theory and practical activities. ...

Description

This lecture explores self-organization in natural systems, where global patterns emerge from local interactions. It delves into foraging strategies of ants, including individual navigation, recruitment mechanisms, and the formation of recruitment trails. The role of randomness in foraging organization and the biological significance of exploitation-exploration balance are discussed. The lecture also covers the Traveling Salesman Problem and Ant Colony Optimization algorithms, showcasing how artificial systems can mimic natural behaviors to solve complex problems.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

Alcherio Martinoli

I received my Diploma in Electrical Engineering from the Swiss Federal Institute of Technology in Zurich (ETHZ), and a Ph.D. in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). I am currently an Associate Professor at the School of Architecture, Civil, and Environmental Engineering and the head of the Distributed Intelligent Systems and Algorithms Laboratory. Before joining EPFL I carried out research activities at the Institute of Biomedical Engineering of the ETHZ, at the Institute of Industrial Automation of the Spanish Research Council in Madrid, Spain, and at the California Institute of Technology, Pasadena, U.S.A. Additional information can be found on my full CV.

Official source

Related concepts (32)

Ant

Ants are eusocial insects of the family Formicidae and, along with the related wasps and bees, belong to the order Hymenoptera. Ants evolved from vespoid wasp ancestors in the Cretaceous period. More than 13,800 of an estimated total of 22,000 species have been classified. They are easily identified by their geniculate (elbowed) antennae and the distinctive node-like structure that forms their slender waists.

Leafcutter ant

Leafcutter ants, a non-generic name, are any of 47 species of leaf-chewing ants belonging to the two genera Atta and Acromyrmex. These species of tropical, fungus-growing ants are all endemic to South and Central America, Mexico, and parts of the southern United States. Leafcutter ants can carry twenty times their body weight and cut and process fresh vegetation (leaves, flowers, and grasses) to serve as the nutritional substrate for their fungal cultivates.

Myrmecia (ant)

Myrmecia is a genus of ants first established by Danish zoologist Johan Christian Fabricius in 1804. The genus is a member of the subfamily Myrmeciinae of the family Formicidae. Myrmecia is a large genus of ants, comprising at least 93 species that are found throughout Australia and its coastal islands, while a single species is only known from New Caledonia. One species has been introduced out of its natural distribution and was found in New Zealand in 1940, but the ant was last seen in 1981.

Ant colony optimization algorithms

In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.

Argentine ant

The Argentine ant (Linepithema humile, formerly Iridomyrmex humilis) is an ant native to northern Argentina, Uruguay, Paraguay, Bolivia and southern Brazil. This invasive species was inadvertently introduced by humans on a global scale and has become established in many Mediterranean climate areas, including South Africa, New Zealand, Japan, Easter Island, Australia, Europe, Hawaii, and the continental United States.

Related lectures (34)

Simplex Algorithm

Covers the Simplex algorithm for function minimization with linear constraints.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Understanding Generalization: Implicit Bias & Optimization

Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.

Optimisation Strategies: Energy Systems Modelling and Optimization

Explores solving strategies for energy system optimization problems and different types of optimization approaches.

Related concepts (32)

Ant

Leafcutter ant

Myrmecia (ant)

Ant colony optimization algorithms

Argentine ant