DRILL Answer the following question’s in your notebook: 1.How does ACO differ from PSO? 2.What does positive feedback do in a swarm? 3.What does negative feedback do in a swarm? 4.How do we build fluctuations into a swarm?

Swarm Theory: “The Wisdom of Crowds” Creating a Swarm (pt 2)

Models for Swarming Behavior Ant Colony Optimization (ACO) Ants are great at finding the closest source of food Ants use pheromones to communicate information Particle Swarm Optimization (PSO) Birds are excellent at finding multiple sources of food simultaneously Birds broadcast information locally Works best for finding discrete solutions Works best for finding continuous solutions

Swarm Theory: Setting it up To solve a problem using swarming techniques you will have to: 1.Define the domain 2.Create an algorithm 3.Write instructions for the agent 4.Define the criteria for completion

Swarm Theory: Probability Computers don’t “think” the same way we do. How do we tell a computer to send an agent towards the most likely solution? We use an understanding of probability and randomly generated numbers to accomplish the task.

Swarm Theory: Probability Experiment – an act for which the outcome is uncertain (e.g. coin toss, dice rolls, survey) Probability – the likelihood that something will occur or be true Sample Space – the set of all possible outcomes for an experiment Event – any subset of the sample space

Swarm Theory: Probability Probability – the likelihood that something will occur or be true The sum of all probabilities for all events in the sample space must sum to 1.

Swarm Theory: Probability If a box contains 4 red marbles and 6 blue marbles, what are the odds of picking a red marble from the box without looking? Experiment: Picking a marble Sample Space: R R R R B B B B B B Event, E: Pick any red marble

Swarm Theory: Probability Random Number – a number drawn from a set of numbers where each number is equally likely to be drawn Random Number Generators – a deterministic algorithm that generates a string of random numbers (better called pseudo-random)

Swarm Theory: Probability A random number between 0 and 1 is drawn in order to be placed into a bin. If the number is between 0 and 0.5, it will be placed into Bin#1. If it is greater than 0.5 and less than 1, it will be placed into Bin#2. What are the odds that it will be placed into Bin#2?

Defining the Domain Domain – the set of all possible input values to a function. F A In this case, the domain is spatial: 8x8 grid of spaces

Creating an Algorithm Algorithm – a step by step procedure for solving a problem, usually iterative 1.Positive Feedback –> 2.Negative Feedback –> 3.Fluctuation –> 4.Multiple Interactions –> Scent trail Smell fades Random #’s Determines direction Scent increases chance of direction being chosen Iteration “Fastest Way to Food”

Building the Agent Each agent in a swarm is given a set of simple instructions that require it to search the domain, test its surroundings and communicate results. 1.Explore grid and look for food 2.Leave scent trail as you return to nest 3.Share information with nest “Fastest Way to Food”

Setting the Threshold Every swarm intelligence program must know when to stop…that stopping point is called the threshold. 1.End if the path to food remains constant 2.End if the path to food continues to change after more than N iterations *N is a number that you choose and can play with. “Fastest Way to Food”

Swarm Theory Homework: – Read over tomorrow’s lecture (U1 L5.ppt) Pop quiz is possible – Set up a swarm intelligence to solve this problem: The CEO of a large company calls a meeting of her executives to decide where to relocate their offices. There are five spaces in a large building nearby. All of the spaces could serve as a new location, but one of them is optimal. How should the executives choose their new office space (use swarm intelligence)?