si 1 swarm intelligence 1

7/29/2019 SI 1 Swarm Intelligence 1

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Swarm Intelligence

COMP 5002

Lecture 1: Introduction

Overview

Introductions

Course

Logistics

Process

Deliverables

Project

Lectures

Assignment

Outline

Introductions

Tony White, Associate Professor

Office: Herzberg 5354

Tel: 520-2600 x2208

Fax: 520-4334 E-mail: [email protected]

Web: http://www.scs.carleton.ca/~arpwhite

Course: http://www.scs.carleton.ca/courses/5002


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Project Deliverables

Outl ine One paragraph description of project.

Essentially the abstract for the project paper.

Due: End of February 2008.

Project Report Journal-style paper, double column, ~8000 words, format is ACM.

Final Report due:7th April 2008 (last day of term)

Implementation Demonstration of software: before7th April 2008.

Software delivery, including source code, required at time ofdemonstration.

Plagiarism

Plagiarism n

1. A piece of writing that has been copied fromsomeone else and is presented as being yourown work

2. The act of plagiarizing; taking someone'swords or ideas as if they were your own

Source: WordNet 1.6, 1997 PrincetonUniversity

Results of Plagiarism

If suspected, an oral examination will occur.

For a first offence:

If confirmed, student will be given zero marks

for the piece of work and the incident will bereported to the Director.

On a second offence:

If confirmed, the student will be given an Fgrade for the course and asked to withdraw.The Director will be informed.


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Materials

Books: Swarm Intelligence, Bonabeau, Dorigo and Theraulaz, Oxford Press, ISBN 0-19-

513158-4 (hard), 0-19-513159-2 (paper)

Swarm Intelligence, Kennedy, Eberhart, Morgan Kaufmann Publishers, ISBN 1-55860-595-9

Self-Organization in Biological Systems, Camazine, Deneubourg, Franks, Sneyd,Theraulaz, Bonabeau, Princeton Univ. Press, ISBN 0-691-012113

The Origins of Order, Kauffman, Oxford Press, ISBN 0-19-507951-5

Emergence, Johnson, Simon and Schuster, ISBN 0-684-86875-X

Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence,Gerhard Weiss, MIT Press, ISBN 0-262-23203-0

Web http://www.scs.carleton.ca/~arpwhite/stigmergy-report.pdf

http://iridia.ulb.ac.be/~mdorigo/ (Ant Colony Optimization)

http://www.particleswarm.net/papers.html (Particle Swarm Optn)

http://dsp.jpl.nasa.gov/members/payman/swarm/ (Swarm biby)

Useful Search Queries

Swarm intelligence

Collective intelligence

Collective Robotics

Subsumption

Reactive agent

Artificial Immune Systems

Potential Optimization Projects

Improvements to swarm-based optimizationalgorithms (SBOA):

Hybrids of Genetic Algorithms (GA), GeneticProgramming (GP) and Ant Colony Optimization

(ACO) Integrating domain-specific heuristics

Application of SBOA to practical problems:

Scheduling, telecommunications, security,

Contrasting SBOA with other techniques:

TSP, QAP,


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Potential Problem-solving Projects

Application of swarm-based algorithms to:

Mobile agents deciding where to go and why!

Network routing; e.g. multi-priority and QoSintegration

Real supply chain management

Automatic programming (variation on GP)

Novel problems involving clustering: Document classification

Communications network design e.g. ring

Alarm correlation and fault diagnosis

Intrusion detection

Simulation Projects

Implementing (learning) agents for:

Soccer (look for RoboCup)

Economic systems (look for Kephart)

Social simulations

(http://www.biz.uiowa.edu/class/6K299_menczer/social.html)

Game playing; e.g. tic-tac-toe, go

Layered problem solving; e.g. subsumption

Social networking problems/systems

Theoretical Work

Analysis of simple swarm algorithms for:

Complexity

Asymptotic performance bounds

Contrast Ant Colony optimization with:

Reinforcement Learning (RL)

Neural Networks (NN)


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Implementation

Create or extend a mobile code framework

that facilitates the generation of swarm

systems.

Extend Repast: http://repast.sourceforge.net/

Students selecting this will lecture on the repast

framework.

Repast

University of Chicago's Social Science Research

Computing's Repast is a software framework for

creating agent based simulations using the Java

Provides library of classes for creating, running,

displaying and collecting data from an agent-

based simulation.

Repast can take snapshots of running simulations,

and create quicktime movies of simulations.

Review

Document state of the art in:

Swarm Engineering

Particle Swarm Optimization

Swarm-based robotics; e.g. Swarm bots

IMPORTANT:

Reviews arent description, theyre analytical


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Focus and Goal

Course will have a software agent focus:

How can simple, reactive agents solve complex

problems?

Background in multi-agent systems will be provided.

Background in GA, GP and RL will be provided.

Course has as a goal:

To provide students with an ability to understand and

exploit biological metaphors with a view to applying

them to problems in their own domain.

Course Outline

The course will cover the topics selected from the following list:

Introduction to agent systems, and multiagent systems. Describe the variouscommunication mechanisms employed and architectures exploited.

Introduction to Swarm Intelligence, collective computation, and collectiveaction.

Natural examples of swarm intelligence: social insects - ants, bees, wasps,termites; emergent control of collective movement - bird flocks, grazing herds,fish schools.

Ant based algorithms for combinatorial optimization problems, andtelecommunications routing.

Division of labour, task allocation, task switching, and task sequencing.

Clustering, brood sorting, data analysis, and graph partitioning.

Course Outline

The course will cover the topics from the following list:

Nest building, and self-assembling.

Cooperative transport by insects and robots.

Learning mechanisms for software agents: GA, GP, RL and NN.

Introduction to the mobile agent, robots, and control methods.

Projects on mobile agents and simulators applying swarm intelligenceprinciples.

Software agent architectures for swarm-based problem solving.

Emergent behaviour in cellular automata.

Emergent behaviour in social systems

Reaction diffusion systems

Self-organized criticality

Artificial Immune Systems


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Overview

Swarm Intelligence is a new computational andbehavioural metaphor for solving distributedproblems

Based on the principles underlying the behaviourof natural systems consisting of many agents.

Technique inspired by the biological examplesprovided by social insects - bees, wasps, ants, andtermites - and by swarming, flocking, herding, andshoaling phenomena in vertebrates.

Emphasizes distributed solutions to problems,direct or indirect interactions among relativelysimple agents, flexibility, and robustness.

Overview

Swarm Intelligence provides a new way to control multipleagent systems - the emergent strategy

local interactions between simple agents mediated by environment

self-organize in such a way as to achieve the required task.

Systems appear to transcend the abilities of the constituentindividual agents

Emergence of high level control has been found to be mediated bynothing more than a small set of simple low level interactions

between individuals, and between individuals and theenvironment.

Overview

Applications include optimization algorithms,communications networks, and robotics ...

In this course we study natural systems exhibitingswarm intelligence, and apply the principles to thecontrol of simulated, distributed mobile agentsystems.

si 1 swarm intelligence 1

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