Complexity and Emergence in Robotics Systems Design Professor George Rzevski The Open University and Magenta Corporation SERENDIPITY SYNDICATE 1 : Talk.

Complexity and Emergence in Robotics Systems Design

Professor George RzevskiThe Open University and

Magenta Corporation

SERENDIPITY SYNDICATE 1 : Talk

Magenta Corporation is my research vehicle

• Founded in 1999• Headquarters in London• 200 programmers in Samara, Russia• Develops ontology-based large-scale

multi-agent systems foroReal-time management (scheduling) oKnowledge discoveryoSemantic analysis and search

Intelligence at Work

knowledge, attitudes, values, mental skills, social skills

Real World

Cognitive/emotional filter:

formal informationsystem

informal informationsystem

Intelligent Agent(a person, a team,

a robot, a family of robots)

real world objects and events

What is the Origin of Intelligence?

Thesis 1• Intelligence is given to humans

Thesis 2• Intelligence is an emergent property of

complex systems

Complexity and Intelligence

Large-scale complex systems, such as a human being,

or a swarm of software agents, exhibit remarkable

emergent capabilities:

o Achieving goals under conditions of uncertaintyo Interpreting meaning of words and imageso Recognising patternso Learning from experience, by discovery and through

communicationo Creating ideas; designing artefacts

These capabilities are aspects of Intelligence

Multi-Layered Complexity and Intelligence

• A team of humans or a swarm of swarms of software agents (in competition and/or co-operation with each other) produce even more powerful emergent intelligence

• Note that a team is a network of networks of neurons

What is Complexity?

A situation is complex if:• It consists of a large number of diverse components, called

Agents, engaged in unpredictable interaction (Uncertainty)• Its global behaviour emerges from the interaction of local

behaviours of Agents (Emergence) and there are always many different ways (Variety) of achieving the same global result

• A small disturbance may cause large changes in its global behaviour (Self-acceleration) whilst large disturbances may be unnoticed (Butterfly Effect)

• It self-organises to accommodate unpredictable external or internal Events (Adaptability and Resilience) and therefore its global behaviour is “far from equilibrium” or “at the edge of chaos”

• It co-evolves with its environment (Irreversibility)

Examples of Complex Systems

• Molecules of air subjected to a heat input; autocatalytic chemical processes; self-reproduction of cells; brain

• Colonies of ants; swarms of bees; ecology • Cities; human communities; epidemics; terrorist

networks• Free market; global economy; supply chains;

logistics; management teams• Multi-agent systems (robot brains?)

Source of Complexity?

There exists compelling evidence that as the evolution of our Universe takes its course, the ecological, social, political, cultural and economic environments within which we live and work increase in Complexity This process is irreversible and manifests itself in a higher Diversity of emergent structures and activities and in an increased Uncertainty of outcomes

Evolution of English Language

Chaucer

Shakespeare

Constructive destructions

Evolution of Society

AgriculturalSociety

IndustrialSociety

InformationSociety

Examples of Robotics Systems Designs

In all examples that follow the intention was

to design complexity into robotics systems

to obtain emergent intelligence

A Swarm of Agents Controlling a Robot

Safety Agent

PerformanceAgent

BookkeepingAgent

SchedulingAgent

MaintenanceAgent

Intelligent Geometry Compressor

Vane 1Agent

Vane 2Agent

Vane 3Agent

Vane 4 Agent

EfficiencyAgent

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

Global Logistics Network

Supplier 1

store

transporter

transporter

store

store

transporter

Intelligentparcels

Intelligentparcels

Intelligentparcels

Destination 1 Destination 2

store

Intelligent Behaviour of Swarms of Software Agents

• If software agents are instructed exactly what to do they behave as conventional programs

• If software agents have no guidance how to behave they exhibit random behaviour

• Intelligent behaviour emerges only under certain conditions of uncertainty – when agents have an appropriate amount of freedom to experiment.

Intellectual Bandwidth and Teamwork

• Levels of emergent intelligence are affected by the Intellectual Bandwidth of Agents (humans, robots)

• Agents can exchange o Data (narrow bandwidth)o Knowledge (wide bandwidth)o Wisdom (exceedingly wide bandwidth)

Conclusions

• Intelligence is an emergent property of complex systems

• Artificial complex systems exhibit intelligent behaviour under certain conditions:o An appropriate degree of uncertainty

(freedom to Agents)o Wide Intellectual Bandwidth

(exchange of knowledge)

“Build complexity into an artefact to make it adaptable……. to have artefacts of all kind capable of adapting and being resilient…”

Professor George Rzevski