Rzevsky agent models of large systems

KNOWLEDGE GENESIS GROUP

Modelling Large Complex Systems Using Multi-Agent Technology

George RzevskiProfessor Emeritus, Complexity and Design, The Open University, UK

Chairman, Knowledge Genesis Group, London, UK


COMPLEXITY


Seven Criteria of Complexity

1. INTERDEPENDENCE – Complex systems consist of a large number of diverse interdependent components called Agents

2. AUTONOMY – Agents are partially autonomous (no central control) but subject to certain regulations and laws

3. EMERGENCE – Global behaviour emerges from Agent interaction and is therefore unpredictable

4. NON-EQUILIBRIUM – Systems operate far from equilibrium (at the edge of chaos)

5. NON-LINEARITY – Relations between Agents are nonlinear (butterfly effects, black swans)

6. SELF-ORGANIZATION – Systems self-organize in reaction to disruptive events (adaptability) or in order to improve performance (creativity)

7. CO-EVOLUTION – Complex systems co-evolve with their environments


Classification of SystemsRANDOMSYSTEMS

COMPLEXSYSTEMS

SYSTEMS INEQUILIBRIUM

ALGORITHMS

& CLOCKS

Uncertainty Total uncertainty

Limited uncertainty

No uncertainty No uncertainty

Behaviour Random Emergent Planned, Designed

Programmed

Norms of behaviour

Total freedom

Limited freedom

No freedom Plan, design

Instructions

Organization

None Self-organizing

Organized Structured

Control None Self-control Centralized control

No need for control

Changes Random changes

Co-evolution Small deviationstemporary

None

Operatingpoint

None Far from equilibrium

Equilibrium None


Where does Complexity Come From?

As evolution takes its course the complexity of ecology, society, economy and technology increases

It seems that complex systems are better at surviving and prospering in tough environments and therefore complexity is favoured by selection


Stepwise Increase in Market Complexity

Agricultural Economy

Industrial Economy

Knowledge Economy

Agricultural Economy


Emergence of Exceedingly Complex SystemsGlobal Cloud (global network) is emerging, which

connects: All computers, personal organisers, telephones, iPods,

iPads, TV sets, DVD players, modems, film projectors, video players, hi-fi decks, radios

Most livestock and physical resources on the planet (by means of RFID tags)

Most people on the planetGlobal Cloud stores: All digital content, i.e., data, documents, images, films,

videos, broadcasts, podcasts, newspapers, magazines, books, music, emails, blogs, websites

Global interaction of people over the Internet using: email, Skype, social networks, Internet galleries, Internet

clubs, business websites, eGovernment, eLearning, downloading and sharing music, exchanging photos and videos


Co-Evolution of Society, Economy & Technology

Urban SocietyIndustrial Economy

Mass Production Technology

Global SocietyKnowledge Economy

Distributed Digital Technology

Rural SocietyAgricultural Economy

Elementary Toolsland

capital

knowledge/information

KEYRESOURCES

digital networks

motorways & railways

village roads

DISTRIBUTIONSTAGES SCOPE

local

regional

global

SUCCESSFACTORS

efficiency

economy ofscale

adaptability


Evolution of English Language

Chaucer

Shakespeare


Evolution of Science

Einstein

Prigogine

Newton


Evolution of Computing

Monolithic algorithms

Swarms of interacting agents + ontology


Modelling Complex Systems


How Can We Model Complexity?

Complexity cannot be simplified Complex systems cannot be controlled Behaviour of complex systems cannot be predicted BUT Complexity can be managed

External complexity can be neutralised by building complexity into artefacts (making them adaptable)

Internal complexity (model complexity) can be tuned


Models of Complex Systems must be Complex Complex systems change as we attempt to

construct their models and these changes must be incorporated in the model as they occur

In other words, models of complex systems must be adaptive and the adaptation must be autonomous (without waiting for instructions from the modeller), which is only possible if models have capabilities of self-organisation

Models must be able to co-evolve with situations that they model

Therefore we can postulate: Only complex models can be used to represent

complex systems.


Technology for Modelling Complexity

The only technology capable of modelling complexity is Multi-Agent Technology

Agent-based software can be defined as Complex Adaptive Software

A typical architecture of multi-agent software includes:

• Knowledge Base (Ontology + Data)• Virtual World populated by software agents• Runtime engine• interfaces


How Agent-Based Software Works?

Agents are small self-contained computational objects capable of exchanging messages among themselves

Demand Agents send messages to Resource Agents asking them to bid for demand fulfilments

Resource Agents send messages to Demand Agents with their bids

Before composing/answering a message Agents consult Enterprise Knowledge Base, where knowledge on how to conduct business is stored


Agent Based Models of Complex Systems

Ontology

Conceptualknowledge

Data

Virtual WorldSoftware Agents

Modified State

Data

Factual knowledge

Current State

Complex Real SystemComplex Real System

Current state Event Next state


Partitioning of Large Systems to Prevent Instability

Social Unit 1

Social Unit 2

Social Unit 3

Social Unit 4


Emergent Intelligence

Intelligence is capability to solve problems under conditions of uncertainty

In multi-agent systems intelligent behaviour emerges from the knowledge-driven conversation between agents


Examples


Multi-Agent Models in Commercial Use: Real-time scheduler for 2,000 taxis in London

Real-time scheduler for Avis European Operation Real-time scheduler for 10% of world capacity of seagoing

tankers Real-time scheduler for cargo for the International Space

Station Real-time scheduler for road logistics operators in the UK and

Russia Real-time scheduler for one of the largest manufacturers in

Russia Real-time system for the allocation of advertisements Real-time data mining for a London insurance company Real-time risk management system for a financial service in

London Semantic search system for research organization in the USA


Architecture of the LEGO World of Agents

Retail Agents

Product Agents

Order Agents

Warehouse Agents

Delivery Agents

P1

P2P3

P4

P5

P6

P7P8

Enterprise Agent


A Family of Space Robots

robot 3

robot 4

robot 5

robot 1

robot 2


Intelligent Geometry Compressor

Stage 1Agent

Stage 2Agent

Stage 3Agent

Stage 4 Agent

CoordinatingAgent


Intelligent Logistics NetworkSupplier 1

store

transporter

transporter

store

storetransporter

Intelligentparcels

Intelligentparcels

Intelligentparcels

Destination 1 Destination 2

store


Philosophy of Complexity


Newtonian (Deterministic) Worldview The Universe was created according to a grand design

Laws of nature are independent of time and location

Uncertainty is due to our ignorance – “God does not play dice”

The role of science is to predict

Science will sooner or later discover universal laws of nature

All knowledge will be reduced to concepts and principles of physics - reductionism

Aristotle, Kant, Newton, Einstein


Complexity Science Worldview The behaviour of the Universe emerges from

interaction of its components and is inherently uncertain

The Universe evolves in time Evolution is irreversible and leads to an increase in

complexity “The future is not given” and therefore long term

predictions are not possible We must be satisfied with discovering likely patterns

of behaviours (evolving predictions) The future is under perpetual construction within

constrains imposed by natural laws, social norms, etc Buddha, Maxwell, Darwin, Popper, Prigogine