Complex is Beautiful

1
Complex is Beautiful

• Professor George Rzevski
• Information Systems and Computing, Brunel
  University
• www.brunel.ac.uk/research/madira/
• Magenta Corporation Ltd, London
• www.magenta-tecnology.com

2
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

3
Research Hypotheses

• Complexity is a prerequisite for adaptation
• Complexity can be designed into artefacts with a
  view to making them adaptive

4
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

5
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)

6
A Mercedes Manufacturing Plant
Supplier 1
Machine-tool 1
Machine-tool 2
Autonomous component
Autonomous component
transporter
Autonomous component
store
store
transporter
store
transporter
store
7
An Aircraft-Airport System
Crew
Sensors
Service Providers
Scheduler
Service demand
Service demand
Transmitter Aircraft to airport
Resources
Service
8
Intelligent Geometry Compressor
Efficiency Agent
Vane 1 Agent
Vane 2 Agent
Vane 3 Agent
Vane 4 Agent
9
Global Logistics Network
Supplier 1
Destination 1
Destination 2
Intelligent parcels
Intelligent parcels
transporter
Intelligent parcels
store
store
transporter
store
transporter
store
10
A Family of Space Robots
robot 5
robot 2
robot 1
robot 3
robot 4
11
A Colony of Agricultural Machinery
mini-tractor 5
mini-tractor 2
mini-tractor 1
mini-tractor 3
Mini-tractor 4
12
A Swarm of Agents Controlling a Machine Tool
Performance Agent
Safety Agent

Bookkeeping Agent
Scheduling Agent
Maintenance Agent
13
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)

14
Common Features

• No central control system
• Distributed decision making
• Network configuration
• Rich information processing activity
• Adaptation

15
A Tentative Definition

• A 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

16
Complexity Space
1

Edge of chaos
Uncertainty
High complexity region
Low complexity region
Variety
0
17
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

18
The Mechanism of Adaptation

• COMPLEXITY
• EMERGENT INTELLIGENCE
• AUTONOMY
• SELF-ORGANISATION
• ADAPTATION

19
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

20

An Intelligent Agent

real world objects and events
formal information system
informal information system
intelligent agent
cognitive filter
knowledge, skills attitudes values
21
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

22
Self-Organisation

• The 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

23
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

24
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

25
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

26
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

27
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

28
A Typical Complex System
29
Two Paradigms

• COMPLEX SYSTEMS

• CONVENTIONAL SYSTEMS

30
Two Paradigms

• CONVENTIONAL SYSTEMS
• (complexity is controlled)
• Hierarchies
• Sequential processing
• Centralized decisions
• Instructions
• Data-driven
• Predictability
• Stability
• Pre-programmed behaviour

• COMPLEX SYSTEMS
• (taking advantage of complexity)
• Networks
• Parallel processing
• Distributed decisions
• Negotiation
• Knowledge-driven
• Self-organization
• Evolution
• Emergent behaviour