Complex is Beautiful

Professor George RzevskiInformation Systems and Computing, Brunel University

www.brunel.ac.uk/research/madira/

Magenta Corporation Ltd, London www.magenta-tecnology.com

Motivation for Research Global markets are becoming so

volatile and competitive that There is a need for adaptable

artefacts such as cars, aircraft, satellites, machine tools, robots, houses etc

Research Hypotheses Complexity is a prerequisite for

adaptation Complexity can be designed into

artefacts with a view to making them adaptive

Research Method Experimenting with large swarms

of software agents Discovering design principles from

results achieved during experimentation

Knowledge transfer from social, cultural, organisational, biological and physical complex systems

Examples of Complex Systems Global economy (Soviet economy is an example of

a disaster caused by attempts to impose centralised control on a complex system)

Street traffic in London (suffers from excessive constraints imposed on drivers)

Aids epidemics in Africa (successfully resists attacks)

Global terrorist networks (successfully resists attacks)

The Internet (successfully resists attacks) A human being (a beautiful example of distributed

decision making and adaptation)

A Mercedes Manufacturing Plant

Supplier 1

store

transporter

transporter

store

storetransporter

Autonomouscomponent

Autonomouscomponent

Autonomouscomponent

Machine-tool 1 Machine-tool 2

store

An Aircraft-Airport System

Sensors

TransmitterAircraft to airport

Service

Servicedemand

Servicedemand

Resources

Service Providers SchedulerCrew

Intelligent Geometry Compressor

Vane 1Agent

Vane 2Agent

Vane 3Agent

Vane 4 Agent

Efficiency Agent

Global Logistics NetworkSupplier 1

store

transporter

transporter

store

storetransporter

Intelligentparcels

Intelligentparcels

Intelligentparcels

Destination 1 Destination 2

store

A Family of Space Robots

robot 3

robot 4

robot 5

robot 1

robot 2

A Colony of Agricultural Machinery

mini-tractor 3

Mini-tractor 4

mini-tractor5

mini-tractor1

mini-tractor2

A Swarm of Agents Controlling a Machine Tool

Safety Agent

PerformanceAgent

BookkeepingAgent

SchedulingAgent

MaintenanceAgent

Other Intelligent Networks Fleets of communication satellites Armadas of very small spacecraft Networked road traffic system Smart matter ( sensors, actuators and agent

running processors/memories embedded in physical materials)

Common Features No central control system Distributed decision making Network configuration Rich information processing

activity Adaptation

A Tentative DefinitionA system is complex if It has a wide variety of behaviours and there is an

uncertainty which behaviour will be pursued It consists of autonomous components, Agents,

capable of competing or co-operating with each other

NOTE: Uncertainty in complex systems is due to the occurrence of unpredictable events rather than

because of our lack of understanding of the system

Complexity Space

Uncertainty

Variety

High complexity region

Low complexity region

0

1 Edge of chaos

Why is Complexity Beautiful?

The features which make Complex Systems

beautiful are:

Emergent properties – properties that do not exist in constituent Agents – these properties emerge from Agent interaction

Adaptation to unpredictable changes in their environment

The Mechanism of Adaptation

COMPLEXITY

EMERGENT INTELLIGENCE

AUTONOMY

SELF-ORGANISATION

ADAPTATION

Intelligence Intelligence is the ability to solve problems under conditions of uncertainty

Intelligence is a prerequisite for autonomy (the ability to select a behaviour without

being instructed/controlled)

Automation, in contrast, is a predictable and repeatable process performed under instruction/control

An Intelligent Agent

knowledge, skillsattitudes & values

real world objects and events

cognitive filter:

formal informationsystem

informal informationsystem

intelligent agent

Emergent Intelligence Intelligence emerges from the

interaction of Agents An Agent makes a tentative proposal to

affected Agents and they in turn suggest improvements

The quality of decisions improves in a stepwise manner

The final decision is agreed by consensus after a period of negotiations

Self-OrganisationThe ability to change own configuration autonomously

To disconnect certain nodes and connect new ones To connect previously disconnected nodes to the same

or to other nodes To acquire new nodes To discard existing nodes

Example: An aircraft broadcasts requirements to selected service

nodes at the airport which respond by scheduling required services

to be available at the touchdown

Adaptation The ability to change behaviour in order to achieve own

goals under conditions of the occurrence of unpredictable events

To react to a change in demand by autonomously rescheduling resources required to satisfy the change

To re-allocate resources to other projects To discard surplus recourses To acquire new resources

Example: a compressor autonomously reacts to a sudden change

of load by self-adjusting positions of vanes and thus moving away

from a surge/stall conditions

Performance affectingFeatures of Complexity

The number of decision making nodes Connectedness among the nodes Access to domain knowledge Skills in applying knowledge Motivation to achieve goals (pro-

activity) Acceptance of/resistance to change Risk acceptance/aversion

Designing Complexity into an Artefact means deciding:

How many decision-making Agents are required How extensive should be connectivity between

Agents How to obtain and organise domain knowledge How to build into the Agents

Skills Motivation Attitudes to risk Attitudes to change

How to guide Agent negotiation

Constructing a Virtual Market A Virtual Market is a market in which

autonomous demands and resources compete for each other without being subjected to any central control (only to certain constraints)

A large number of problems can be transformed into a resource allocation problem

Examples of Virtual Markets eCommerce – the allocation of goods/services to

demands Logistics – the allocation of resources in time

and to a location Control – the allocation of behaviours to

requirements Project management – the allocation of

resources to time slots Data mining – the allocation of records to

clusters Text understanding – the allocation of meanings

to words

A Typical Complex System

Two Paradigms

COMPLEX SYSTEMSCONVENTIONAL SYSTEMS

Two Paradigms

CONVENTIONAL SYSTEMS(complexity is controlled)Hierarchies Sequential processingCentralized decisionsInstructionsData-driven PredictabilityStability Pre-programmed

behaviour

COMPLEX SYSTEMS(taking advantage of

complexity)Networks Parallel processingDistributed decisionsNegotiationKnowledge-driven Self-organizationEvolutionEmergent behaviour