tony white, associate professor - carleton...

Contact information

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

Announcement• Amorphous Computing (1 lecture)

– http://www.swiss.ai.mit.edu/projects/amorphous/• Per Bak (1-2 lecture)

– “How Nature Works: the science of self-organized criticality”– http://public.logica.com/~stepneys/bib/nf/b/bak.htm

• Camazine et al (1 lecture)– Chapter 8, “Self Organization in Biological Systems”– Pattern Formation in Slime Molds and Bacteria

• Lectures required for 1st, 2nd and possibly 3rd classes in February (4th, 7th and 11th)

Announcement• Amorphous Computing (1 lecture)

– http://www.swiss.ai.mit.edu/projects/amorphous/• Resnick (1 lecture)

– “Turtles, Termites and Traffic Jams”– Starlogo

• Lectures required for 1st, 2nd and possibly 3rd classes in February (4th, 7th and 11th)

Summary

• Discussed– Agent as a black box– Deliberative vs reactive architecture– Symbolic nature of models

• Communication– Special structures: blackboards and protocols– Centralized

• Weaknesses– Brittle– Don’t scale …

Swarm Intelligencea.k.a. Self-Organizing Systems

Lecture 3

What is Swarm Intelligence?

• “Swarm Intelligence is a property of systems of non-intelligent robots exhibiting collectively intelligent behavior.” [Beni]

• Characteristics of a swarm:– distributed, no central control or data source;– no (explicit) model of the environment;– perception of environment, i.e. sensing;– ability to change environment.

What is Swarm Intelligence?

• Swarm systems are examples of behavior-based systems exhibiting:– multiple lower level competences;– situated in environment;– limited time to act;– autonomous with no explicit control provided;– problem solving is emergent behavior;– strong emphasis on reaction and adaptation;

What about Software AgentsDifferences … I would say!

• No global controller• Immersed in environment

– No symbolic view of the world– Environment is memory

• Communication is not directed– No KQML, dialogs or “contract net” protocols

http://www.micro.caltech.edu/micro/CORO/index.html

EE141:Swarm IntelligenceLecturers: Alcherio Martinoli

Rod GoodmanOwen HollandAdam Hayes

TAs: Adam HayesWilliam AgassounonKjerstin EastonPhilip TsaoJoseph ChenAmit Kenjale

Swarm Intelligence

• Minimalist but fully autonomousindividuals

• Fully distributed control• Exploitation of robot-robot and

robot-environment interactions

Swarm Intelligence: “Any attempt to designalgorithms or distributed problem-solving devicesinspired by the collective behavior of social insectcolonies and other animal societies.” [Bonabeau,Dorigo, and Theraulaz, 1999]

• Exploitation of explicit or implicit(stigmergic) communication

• Scalability (from a few up tothousands individuals)

• Enhanced robustness throughredundancy and minimalistdesign of the individuals

Biological Motivation• Biological Inspiration from: social insects (ants, bees, termites) flocks of birds,

herds of mammals, schools of fish, packs of wolves, pedestrians, traffic.

• Colonies of social insects can achieve flexible, reliable, intelligent, complex systemlevel performance from insect elements which are stereotyped, unreliable,unintelligent, and simple.

• Insects follow simple rules, use simple local communication (scent trails, sound,touch) with low computational demands.

• Global structure (e.g. nest) reliably emerges from the unreliable actions of many.

Human Swarms

All rights reserved

InsectSocieties

A natural model of distributed problem solving

• Collective systems capable of accomplishing difficult tasks, in dynamicand varied environments, without any external guidance or controland with no central coordination

• Achieving a collective performance which could not normally beachieved by any individual acting alone

• Constituting a natural model particularly suited to distributedproblem solving

• Many studies have taken inspiration from the mode of operation ofsocial insects to solve various problems in the artificial domain

InsectSocieties

Individual simplicity and collective complexity

• The behavioural repertoire of the insects is limited

• their cognitive systems are not sufficiently powerful to allow a singleindividual with access to all the necessary information about the stateof the colony to guarantee the appropriate division of labour and theadvantageous progress of the colony

• the colony as a whole is the seat of a stable and self-regulatedorganisation of individual behaviour which adapts itself very easily tothe unpredictable characteristics of the environment within which itevolved

Systems of collective decision-making

• Insect societies have developed systems of collective decision-making operating without symbolic representations, exploiting thephysical constraints of the environment in which they evolved, andusing communications between individuals, either directly when incontact, or indirectly (stigmergy) using the environment as achannel of communication

• Through these direct and indirect interactions, the society self-organises and, faced with a problem finds a solution with acomplexity far greater than that of the insects of which it iscomposed

Self-organisation

• How do insect societies manage to perform difficult tasks, in dynamicand varied environments, without any external guidance or control,and with no central coordination?

• How can a large number of entities with only partial informationabout their environment solve problems?

• How can collective cognitive capacities emerge from individuals withlimited cognitive capacities?

Collective or SwarmIntelligence

Some questions ...

•What do we mean by intelligence?• Ability to ‘solve problems’ in some abstract or real domain• Ability to produce behaviour appropriate to a situation

•What is it that has intelligence?• A natural system of one or more agents• An artificial system of one or more agents (computational

entities or robots)

•At opposite ends of the scale:

•Human intelligence vs. insect intelligence

•How useful is each of these as a model for intelligent artificialsystems?

Intelligence

• Individual human intelligence • Highly capable, extremely flexible • Consciously reason about the problem, seeking new

information where necessary, and generate and execute a plan.

• Individual artificial intelligence• Capable in niche areas, inflexible • Apply rules of logic and reason to an abstract representation of

the problem situation, seeking new information wherenecessary, and generate and execute a plan.

• Conventional robot • Incapable compared with humans, inflexible.• Build a representation of the problem situation, and apply

rules of logic and reason to it, seeking new information wherenecessary, and generate and execute a plan.

Intelligence

• Individual insect intelligence•Extremely capable and flexible within niche (specialist) situations, generallyincapable outside them. Cued to environment – incapable outside

•Specialised behaviours triggered by specialised sensing; chaining of behavioursby internal or external cues; suppression of some behaviours by others.

• Insect level (behaviour based) robots•MIT 85 “insect lab” Rod Brooks AI lab

•Excellent at low level tasks in particular environments, often flexible, easy tobuild, often robust to component failure.

•Mimic individual insect intelligence - Specialised behaviours triggered byspecialised sensing; chaining of behaviours by internal or external cues;suppression of some behaviours by others.

Intelligence

Sheepdog behavior based robot

© Richard Vaughan

• Multiplication of effort, resources etc.

• Distributed sensing

• Distributed action

• Division of labour

• Specialisation

• Extended time scales

• Redundancy

• etc

BUT

The BIG QUESTIONHow can individual efforts be coordinated to achieve the common goal?

Multiple vs. Individual Systems

• Multiple humans• The most powerful systems we know, but vulnerable to high-level

failures. Dominated by communications.

• centralized• hierarchical: information up, decisions down• highest level has global view of situation• specialists• explicit task allocation and reallocation• cognitive level communications• Etc

• Multiple AI systems• Modelled on humans; too soon to judge.

• Multiple conventional robots • Almost an empty category; a few toy systems, mostly simulated.

Multiple vs. Individual Systems

• Colonies of social insects (ants, termites, some wasps, some bees) arespectacularly capable.

• Complex lifestyles• Flexible and robust• The capabilities of the colony transcend those of the individual insects• The individual insects appear to be no more capable than solitary

insects, but many of their behaviours are directed towards affecting thebehaviour of others.

• How is this intelligence achieved? • Self organisation:

• local interactions between insects, and between insects and the environment,produce emergent patterns and configurations which ‘solve the problem’.

• Specialisation: • By morphology (soldiers etc castes)• By behaviour (identical ants do different tasks – stay with that task over

time. E.g. brood, or midden – stay with particular behaviour• By age older more dangerous )

Multiple Insects

• Ant Colony Optimisation• extremely successful at a variety of very difficult

combinatorial optimisation problems• some of the best solutions known to some problems

• Ant Based Control of telecommunications

systems• exceptionally flexible solutions• best solution for some problems

Swarm-Based AI

Multiple robots offer significant advantages over single robots:• Simultaneous sensing and action in multiple places• Task dependent reconfigurability• Enhanced system performance through work division• Robustness through redundancy• Task enabling if the task could not be solved by an individual• we can build systems of behaviour based robots with adequate

capabilities• we can demonstrate the use of self-organisation by these systems in

task achievement

Swarm-Based Collective Robotics

© Gilles Caprari

+Task enabling.+Task enabling.+Distributed sensing and action.+Distributed sensing and action.+Enhanced system performance+Enhanced system performanceby work division.by work division.

- Interference.- Interference.- Increased uncertainty and- Increased uncertainty anddynamics.dynamics.- Overall system cost.- Overall system cost.

Autonomy at the ...Autonomy at the ...

• Energetic level.• Energetic level.

• Sensory, motor, and computational• Sensory, motor, and computationallevel.level.

• Decisional level.• Decisional level.

Multiple vs. Single Robotics Solutions

• Locality and Globality• based on physical proximity.• E.g. local and global communication.• E.g. local and global information.

• Flexibility is the capacity of a society to change its collective behavior [Gordon92].• Plasticity is defined as the capability of individuals to adapt their control parameters.• Adaptation implies not only the capacity for change, but the additional requirement

that this change represents an improvement in ‘fit’[Belew96].• Centralized and Decentralized Team Control

• Centralized: central unit coordinates the group decisional processes.• Decentralized: no central coordination.

• Hierarchical and Distributed Team Control• Hierarchical control: locally centralized.• Distributed control: each teammate has full decisional power.

• An intelligent individual is able to ....• act in its environment so that a viability condition is always satisfied.• maintain its identity (in a broad sense).

• A team is provided with collective intelligence if the viability of the team is requiredin order to achieve the viability of the individual.

A Few Concepts

Robots, techniques, and tools• Probabilistic Modeling• Simulation

• Point• Embodied

• Real Robots• Moorebot• Kephera• Alice

• Tools• Overhead vision system for tracking, monitoring• KPS• Powered floor for Kephera• Charging docks for Moorebot

MooreBots

• We have built 15 robots based on an O. Holland design.• We will use these to test our theories, and to perform experiments on

collective robotics.• The Robots Feature:

• Wireless LAN communication to each other , base station, andInternet

• Linux operating system• PC 104 (386 to Pentium) Architecture• PCMCIA slots for GPS, frame grabber, etc.• 10” dia size• 2 m.p.h., 720 degrees/sec• 4 miles range, 2 hours endurance• odometry to 0.1% accuracy• wheeled or tracked chassis

MooreBots

Khepera Miniature Robot

5.5 cm

actuators

68HC11 microcontroller (slave)68331 microcontroller (master)

sensors

batteries

Alice Micro-Robot•Developed by G. Caprari, Autonomous SystemLab, EPFL, Switzerland

•We are working with the developers to put nosechips on the robot

•Main Features:

•Modular

•Dimensions: 22mm x 20mm x 19mm

•Max Speed: 35 mm/s

•Power <10mW

•Power autonomy up to 10 hours.

•4 proximity sensors

•Local IR robot-robot communications

•Low power radio comms robot-robot androbot-host PC. Range 10m.

•PIC 16F84 with 1Kb Flash memory

Simulation Tools

• Non-embodied simulator (point simulator)•Multiple-robot.

•No interference: robots simulated as points

•Acceleration ratio: up to thousands times thanthe same real robot experiment.

• Embodied simulator (Webots)•Multiple-robot.•Khepera, Alice, and soon Moorebots(customizable robot chassis, actuators, andsensors).•Acceleration ratio: up to hundred times than thesame real robot experiment.•Smooth transition between simulator and realrobots (API translator).

Some Further Tools ...

+ +_

• KPS: laser-based scanning, fully scalable(perfomances independent from group size), ±5mm @ 4 m, 25-50 Hz

• Monitoring collectiveexperiments withceiling camera

• External power supply from aspecial electrical floor: positionand orientation independence,depending on the configuration,open-end experiments

Experimental Levels

• Simulation• Analytical probabilistic models• Numerical probabilistic models• Non-embodied simulations• Embodied, sensor-based simulations

• Real robots• Partial virtual environments• Desktop/laboratory environments• Real environments

Abstraction

Reality

Probabilistic Modelling and CollectiveSystem Optimization

• Understanding and prediction ofcollective team performance based onsingle-robot features.

• Prediction of large swarm of robots .• System optimization (number of robots,

control parameters, body and actuatormorphology, sensor and communicationrange and type, …).

Real robotsEmbodied simulator Probabilistic model

Collective Robotics/SICollective Robotics/SICollective Robotics/SICollective Robotics/SIApplicationsApplicationsApplicationsApplications

Swarms - Artificial

Movies - ANTZ"If you have a film about ants, it would be"If you have a film about ants, it would bedisappointing to never have a shot with moredisappointing to never have a shot with morethan five ants. You want 5,000--or maybethan five ants. You want 5,000--or maybe50,000 ants to convey the sense of a colony to50,000 ants to convey the sense of a colony tothe audience," said Kirk. "With our crowdthe audience," said Kirk. "With our crowdsystem, the directors and animators have asystem, the directors and animators have agreat amount of creative control, yet there's agreat amount of creative control, yet there's atremendous labor savings. They're able totremendous labor savings. They're able todefine the way a crowd will look, move, anddefine the way a crowd will look, move, andbehave, and then let the computers calculatebehave, and then let the computers calculatethe specific motion for each ant in thethe specific motion for each ant in thecrowd."crowd."

Crowd Control

Khepera Stick-Pulling Experiments

IDSIA

Beckers et al. Aggregation Experiments

Real robots

Probabilistic model(cluster modifyingprobabilities)

Biological inspiration from social insect aggregation processes (e.g. clustering of dead ant corpses)

Khepera Aggregation Experiments

IDSIA

Distributed Exploration:RobotsCollaborate to find an IR Beacon

•Each robot has itself an omni-directional beaconwhich is used to signal when the robot “sees” thehoming beacon, including when it arrives at thehoming beacon.• In “collaboration” mode the omni-directionalbeacons are enabled. In non-collaboration mode theyare disabled.•The “task” is completed when all N robots arrive atthe beacon.

Collective Advantage

Non-collaborative

Collaborative

Non-collaborative

Collaborative

Collaborative

Non-collaborative

Traffic

Distributed Plume Tracing

History• Swarm Intelligence first used by Beni, Hackwood and Wang in context

of cellular robotics.• Simple agents occupy 1 or 2 D environments.• Self-organize through nearest neighbour interactions.• Collections of simple agents or automata to solve problems on graphs

or lattices in work of Butrimenko, Tsetlin, Stefanyuk and Rabin.• Rabin introduced moving automata to solve problems on graphs or

lattices by interacting with the consequences of their previous actions. • Tsetlin identified the important characteristics of biologically-inspired

automata that make the swarm-based approach potentially powerful:– Randomness– Decentralization– Indirect interactions among agents and self-organization

History

• Butrimenko applied ideas to control telecommunications networks

• Stefanyuk applied ideas to cooperation between radio stations

Lecture on Tsetlin automata would be worthwhile …

Nice talk orProject here

Emphasis

• Design of adaptive, decentralized, flexible and robust artificial systems capable of solving problems.

• Inspiration is social insects.

How to proceed?• Have to understand the mechanisms that generate

collective behaviour in insects.• Modelling is key here.• Different from designing an artificial system – modelling

tries to uncover what happens in the natural system. • Should reproduce features of the natural system and be

consistent with what is known about it:– Parameters cannot be arbitrary– Mechanisms must be plausible

• Model should be predictive:– Predictions should be testable – Variables and parameters should be experimentally verifiable

Engineering …

• Does not have to be concerned with biological plausibility:– Efficiency– Flexibility– Robustness– Cost

• Natural selection uses “survival of the fittest”– Essentially a “tinkering” process

• Social insects are remarkably successful

The use of Metaphor

• Neural Networks– Abstraction of actual brain organization

• Genetic Algorithms– Abstraction of basic evolutionary process

• Basic principles are present, but detail is unimportant.

• Ultimately, good problem-solving device does not have to be biologically relevant.

Self-Organization in Insects

• Self organization is a set of dynamical mechanisms whereby structures appear at the global level from interactions among its lower-level components.

• Rules specifying the interactions among system constituent units are executed on the basis of local information, without reference to the global pattern.

• Global pattern is emergent property of system, rather than a property imposed upon system by external controlling influence.

Graphically

Controller

System System

Self-organization (SO) in Social Insects

• SO is a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions among its lower-level components.

• Arises as a consequence of statiotemporallyorganized networks of pheromone trails (for example).

• Is often inefficient, non-optimal– Non-greedy search– Agents (ants) can get lost

Self-organization in Social Insects

• Self organization relies on 4 ingredients:– Positive feedback

• Preferential food source exploitation– Negative feedback

• Used for stabilization: saturation, exhaustion or competition; e.g. Lotka-Volterra dynamics

– Amplification of fluctuations • Random walks, errors, random task switching• Often crucial, allows discovery of new solutions to occur

– Multiple interactions• Signals from one individual have to be seen by others• Environment is key element in system• Density and signal strength are key

Important:

Agent memory is NOT a requirement for patterns to develop. Memory is

collective – it’s stored in the networks/lattices on which the agents

operate.

Figure 1.9

From Swarm IntelligenceBonabeau et al.

Figure 1.10From Swarm IntelligenceBonabeau et al.

SO Properties

• Statiotemporal structures develop in an initially homogeneous medium

• For example, characteristic well-organized pattern develops in honey bee combs

• Pattern consists of concentric regions:– Brood area– Rim of pollen– Large peripheral region of honey

Figure 1.11From Swarm IntelligenceBonabeau et al.

Bee Colonies

• 25,000 females• Few thousand dones• Single queen• Also:

– Immature brood: eggs, larvae and pupae– Honey and pollen– ~ hexagonal 100,000 cells

• Temperature maintained 33-34 degrees C– Gradient across comb cannot explain pattern formation– Isn’t a “colouring pattern” mechanism either

Bee’s … the model

• Assumptions based upon experiment:– Queen moves randomly over combs and lays (1000-

2000 per day) most eggs in neighborhood of cells occupied. Eggs remain for 21 days

– Honey and pollen are deposited “randomly” in selected cells (there’s a pattern). Proximity of food a factor.

– 4x more honey than pollen is brought back to hive than pollen

– Typical removal input rations for honey and pollen at 0.6 and 0.95, respectively

– Removal of honey and pollen is proportional to the number of surrounding cells containing the brood.

Bees are amazing!

• Colony collects 60kg of honey• 40mg per nectar load: ~ 3,000,000 trips!• Pollen load is 15mg: ~1,300,000 trips!• 25 nectar trips required to fill a cell• 15 pollen trips required to fill a pollen cell

Where do they deposit material?

• Pollen foragers select a cell to deposit load• Honey foragers regurgitate load to food

storage bees, which select cell• Pattern of deposits independent of whether

brood is present or not: food often deposited within the brood area

Chapter 16 – page 317

Self Organization in Biological Systems:Camazine et al.

Chapter 16 – page 317


~ Exponential distribution

Removal of pollen / honey


Conclusion from Experiments• Honey and pollen are preferentially removed from cells

near brood• Magnitude is 10x• Interacting processes of deposit and removal lead to

characteristic brood pattern• Queen has to return to brood area to discover cells emptied

of food and brood• Food is deposited most readily at “boundary” of brood and

is consumed there too – changes rapidly• Cells reserved for brood are infrequently turned over• Interface region emerges automatically

tony white, associate professor - carleton...

Documents