Swarm Intelligence95.590YLecture 1

Overview

• Introductions• Course

– Logistics– Process– Deliverables

• Project• Lectures• Assignment

– Outline

Introductions

• Tony White, Associate Professor– Office: Hertzberg 5354– Tel: 520-2600 x2208– Fax: 520-4334– E-mail: arpwhite@scs.carleton.ca– E-mail: arpwhite@hotmail.com– Web: http://www.scs.carleton.ca/~arpwhite

Course E-mail• All course-related e-mail to have a subject line

that contains the text “95.590Y”• All requests must be documented via e-mail, a

verbal agreement is insufficient.• Requests for extensions to an assignment or

project deadline, changes in student lecture schedule must occur more than 24 hours prior to the deadline or lecture time. Failure to do this will result in the student being considered in default of the deadline.

What I need now …

• An e-mail from you with:– Student #– Name– Expections of course, if any ☺

Logistics• Lectures:

– Monday 11:30am-1:00pm, ME 4494– Thursday 1:00pm-3:00pm, ME 4494

• Rescheduled classes:– From Thursday, 7th March to ...

• Office Hours: – Monday, 1:00pm-3:00pm– By arrangement, with e-mail confirmation of professor’s availability.

• Assignments: – Due by 5:00pm on date posted.– Late assignments will be penalized by 10% per day unless written

deferral is granted.– Soft and hard copies to be submitted.– E-mail of softcopy to Carleton address.

Course Expectations: Discussion

• Project: 50%– Can be purely written: review, theory, …– Can be an implementation: simulation, optimization,

…• Lecture(s): 30%

– Topics for lecture(s) will be assigned– Will be paper(s) or book chapter– Slides for lecture(s) must be submitted as course

documentation• Assignment: 20%

– Simple implementation of swarm algorithm: Ant Colony or Particle Swarm Optimization

Project Deliverables• Outline

– One paragraph description of project.– Essentially the abstract for the project paper.– Due: 31st January, 2002.

• Project Report– Journal-style paper, ~8000 words, format will be provided.– Outline due: 28th February, 2002– Final Report due: 12th April, 2002

• Implementation (if appropriate)– Design overview due: 28th February, 2002– Demonstration of software: before 12th April, 2002.– Software delivery, including source code, required at time of

demonstration.

Plagiarism

Plagiarism n 1. A piece of writing that has been copied from

someone else and is presented as being your own work

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

Source: WordNet ® 1.6, © 1997 Princeton University

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 be reported to the Director.

• On a second offence:– If confirmed, the student will be given an “F”

grade for the course. The Director will be informed.

Books• Books:

– Swarm Intelligence, Bonabeau, Dorigo and Theraulaz, Oxford Press, ISBN 0-19-513158-4 (hard), 0-19-513159-2 (paper) (LIB)

– 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– Bak: Self Organized Criticality (LIB)– Resnick: Turtles, Termites and Traffic Jams (LIB)

GREAT BOOK!

Web Materials• Web

– http://iridia.ulb.ac.be/~mdorigo/ (Ant Colony Optimization)– http://www.particleswarm.net/papers.html (Particle Swarm Opt’n)– http://dsp.jpl.nasa.gov/members/payman/swarm/ (Swarm bib’y)– …

You will find an out-of-date tutorial on Swarm Intelligence from me hosted in Systems and Computer Engineering. IGNORE IT!

Web Book References

• Bonabeau: Swarm Intelligence book: http://www.amazon.com/exec/obidos/ASIN/0195131592/ref=pd_bxgy_text_2/103-0017389-5158217

• Eberhart: Swarm Intelligence book: http://www.amazon.com/exec/obidos/ASIN/1558605959/ref=pd_bxgy_text_2/103-0017389-5158217

Useful Search Queries

• Swarm intelligence• Collective intelligence• Collective robotics• Swarm robotics• Subsumption• Reactive agent

Potential Optimization Projects• Improvements to swarm-based optimization

algorithms (SBOA):– Hybrids of Genetic Algorithms (GA), Genetic

Programming (GP) and Ant Colony Optimization (ACO)

– Integrating domain-specific heuristics• Application of SBOA to practical problems:

– Scheduling, telecommunications• Contrasting SBOA with other techniques:

– TSP, QAP, …

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 QoS

integration– “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

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– Layered problem solving; e.g. subsumption– Extending the Anthill project:

http://www.cs.unibo.it/projects/anthill/. Students selecting this will lecture on the framework.

– Extending Hive http://www.hivecell.net/. Students selecting this will lecture on the framework.

Anthill• Anthill is a framework aimed at supporting the design, development and analysis of

peer-to-peer protocols and algorithms. The goals of Anthill are to: (i) provide an environment for simplifying the design and deployment of new P2P systems, and (ii) provide a "testbed'' for studying and experimenting with P2P systems in order to understand their properties and evaluate their performance. Anthill is based on the multi-agent systems (MAS) paradigm. A MAS is a collection of autonomous agentsthat can observe their environment and perform simple local computations leading to actions based on these observations. The behavior of an agent may be non-deterministic and its actions may modify the agent environment as well as the agent location within the environment. What distinguishes MAS from other agent models is that there is no central coordination of activity. MAS often exhibit a property called swarm intelligence whereby a collection of simple agents of very limited individual capabilities achieves ``intelligent'' collective behavior. In this manner, they are able to solve problems that are beyond the capabilities or knowledge of individual agents. For example, ant colonies, which are natural instances of MAS, are known to be capable of solving complex optimization problems including those arising in communication networks. In our opinion, MAS can be profitably adopted for the design of innovative peer-to-peer algorithms.

Theoretical Work

• Analysis of simple swarm algorithms for:– Complexity– Asymptotic performance bounds

• Contrast Ant Colony optimization with:– Reinforcement Learning (RL)– Neural Networks (NN)

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.• Do something interesting with TeamBots 2.0

http://www.teambots.org/– Students selecting this will lecture on the TeamBots

framework.

Repast• The University of Chicago's Social Science Research Computing's Repast is a software framework for creating

agent based simulations using the Java language (requires version Java 1.3 or greater). It provides a library of classes for creating, running, displaying and collecting data from an agent based simulation. In addition, Repast can take snapshots of running simulations, and create quicktime movies of simulations. Repast borrows much from the Swarm simulation toolkit and can properly be termed "Swarm-like." In addition, Repast includes such features as run-time model manipulation via gui widgets first found in the Ascape simulation toolkit.

• Repast envisions a simulation as a state machine whose state is constituted by the collective states of all its components. These components can be divided up into infrastructure and representation. The infrastructure is the various mechanisms that run the simulation, display and collect data and so forth. The representation is what the simulation modeler constructs, the simulation model itself. The state of the infrastructure is then the state of the display, the state of the data collection objects etc. The state of the representation is the state of what is being modeled, the current values of all the agents' variables, the current value of the space or spaces in which they operate, as well as the state of any other representation objects (e.g. aggregate quasi-independent "institution" objects). The history of the simulation as a software phenomenon is the history of both these states, while the history of the simulation as a simulation is the history of the representational states. In Repast as in Swarm, any changes to the states of the infrastructural components and the representational components occur through a Schedule object. In short then, Repast allows a user to build a simulation as a state machine in which all the changes to the state machine occur through a schedule. This provides clarity and extensibility both for the simulation writer/user as well as the software designer seeking to extend the toolkit.

• Several other parts of Repast follow the Swarm paradigm and should be familiar to users of Swarm.• The name Repast is an acronym for REcursive Porous Agent Simulation Toolkit. Our goal with Repast is to

move beyond the representation of agents as discrete, self-contained entities in favor of a view of social actors as permeable, interleaved and mutually defining, with cascading and recombinant motives. We intend to support the modeling of belief systems, agents, organizations and institutions as recursive social constructions. The fuller goal of the toolkit is to allow situated histories to be replayed with altered assumptions. To achieve this goal, it will be necessary for Repast to provide a feast of advanced features, and it is toward that objective that we work. The 1.x releases of Repast are just the beginning of our work towards this objective.

Implementation

• Do something interesting with TeamBots2.0 http://www.teambots.org/– Students selecting this will lecture on the

TeamBots framework.

TeamBots• TeamBots is a Java-based collection of application programs and

Java packages for multiagent mobile robotics research. The TeamBots distribution is a full source-code release. The simulation environment written entirely in Java. Execution on mobile robotssometimes requires low-level libraries in C, but Java is used for all higher-level functions. At present, TeamBots will run on the Nomadic Technologies' Nomad 150 robot and (very soon) on Personal Robotics' Cye robot. TeamBots supports prototyping, simulation and execution of multirobot control systems. Robot control systems developed in TeamBots can run in simulation using the TBSimsimulation application, and on mobile robots using the TBHard robot execution environment.

Review

• Document state of the art in:– Swarm Engineering– Particle Swarm Optimization: critique the

algorithm and its future extendibility– Articulate the problems that require solution

for successful swarm deployment; i.e. describe a research agenda

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 OutlineThe course will cover the following topics:

0. Introduction to agent systems, and multiagent systems. Describe the various communication mechanisms employedand architectures exploited.

1. Introduction to Swarm Intelligence, collective computation, and collective action. 2. Natural examples of swarm intelligence: social insects - ants, bees, wasps, termites; emergent control of collective

movement - bird flocks, grazing herds, fish schools. 3. Ant based algorithms for combinatorial optimization problems, and telecommunications routing. 4. Division of labour, task allocation, task switching, and task sequencing. 5. Clustering, brood sorting, data analysis, and graph partitioning. 6. Nest building, and self-assembling. 7. Cooperative transport by insects and robots. 8. Learning mechanisms for software agents: GA, GP, RL and NN.9. Introduction to the mobile agent, robots, and control methods. 10. Projects on mobile agents and simulators applying swarm intelligence principles. 11. Software agent architectures for swarm-based problem solving.12. Emergent behaviour in cellular automata.13. Emergent behaviour in social systems14. Reaction diffusion systems15. Self-organized criticality

Overview• Swarm Intelligence is a new computational and behavioural metaphor for solving

distributed problems; it is based on the principles underlying the behaviour of natural systems consisting of many agents, such as ant colonies and bird flocks. The approach emphasizes distributed solutions to problems, direct or indirect interactions among relatively simple agents, flexibility, and robustness. Applications include optimization algorithms, communications networks, and robotics. In this course we study natural systems exhibiting swarm intelligence, and apply the principles to the control of simulated, distributed mobile agent systems.

• Swarm Intelligence provides a new way to control multiple agent systems - the emergent strategy, where local interactions between simple agents and the environment combine and self-organize in such a way as to achieve the required task. The technique is inspired by the biological examples provided by social insects -bees, wasps, ants, and termites - and by swarming, flocking, herding, and shoaling phenomena in vertebrates. The abilities of such systems appear to transcend the abilities of the constituent individual agents; in all the biological cases studied so far, the emergence of high level control has been found to be mediated by nothing more than a small set of simple low level interactions between individuals, and between individuals and the environment.