In this lecture, we wrap up our introduction of Simulated Annealing and then move on to an introduction to Distributed Artificial Intelligence and Swarm/Collective Intelligence. We start with some clarifications about entropy related to why the logarithm is used. We then revisit basic Monte Carlo integration, based on the Law of Large Numbers, and how it motivates the need for a Boltzmann distribution sampler. We then outline the Metropolis–Hastings algorithm (for Markov Chain Monte Carlo (MCMC) methods). This allows us to finally describe the Simulated Annealing (SA) algorithm, which combines the Metropolis algorithm with a temperature annealing schedule. After wrapping up the discussion of SA, we move on to introducing Distributed AI and Swarm Intelligence. This discussion starts with a description of "Ant System (AS)", the prototype version of what eventually became Ant Colony Optimization (ACO). We will pick up with more details of AS/ACO for combinatorial optimization in the next lecture.
Whiteboard notes for this lecture can be found at: https://www.dropbox.com/s/ngsm9w4am1i6224/IEE598-Lecture6A-2022-03-03-Distributed_AI_and_Swarm_Intelligence-Part_1-Ant_Colony_Optimization.pdf?dl=0
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