Complexity and Emergence in Robotics Systems Design

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Complexity and Emergence in Robotics Systems
Design
SERENDIPITY SYNDICATE 1 Talk

• Professor George Rzevski
• The Open University and
• Magenta Corporation

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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 for
• Real-time management (scheduling)
• Knowledge discovery
• Semantic analysis and search

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Intelligence at Work

Real World


real world objects and events
informal information system
formal information system
Intelligent Agent (a person, a team, a robot, a
family of robots)
Cognitive/emotional filter
knowledge, attitudes, values, mental skills,
social skills
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What is the Origin of Intelligence?

• Thesis 1
• Intelligence is given to humans
• Thesis 2
• Intelligence is an emergent property of complex
  systems

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Complexity and Intelligence

• Large-scale complex systems, such as a human
  being,
• or a swarm of software agents, exhibit remarkable
  
• emergent capabilities
• Achieving goals under conditions of uncertainty
• Interpreting meaning of words and images
• Recognising patterns
• Learning from experience, by discovery and
  through communication
• Creating ideas designing artefacts
• These capabilities are aspects of Intelligence

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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

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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)

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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?)

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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

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Evolution of English Language

Shakespeare
Chaucer
Constructive destructions
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Evolution of Society

Information Society
Industrial Society
Agricultural Society
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Examples of Robotics Systems Designs

• In all examples that follow the intention was
• to design complexity into robotics systems
• to obtain emergent intelligence

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A Swarm of Agents Controlling a Robot

Safety Agent
Performance Agent
Bookkeeping Agent
Scheduling Agent
Maintenance Agent
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Intelligent Geometry Compressor
Efficiency Agent
Vane 1 Agent
Vane 2 Agent
Vane 3 Agent
Vane 4 Agent
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A Family of Space Robots
robot 5
robot 2
robot 3
robot 1
robot 4
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A Colony of Agricultural Machinery
mini-tractor 5
mini-tractor 2
mini-tractor 1
mini-tractor 3
mini-tractor 4
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Global Logistics Network
Destination 1
Destination 2
Supplier 1
Intelligent parcels
Intelligent parcels
Intelligent parcels
transporter
store
store
transporter
transporter
store
store
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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.

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Intellectual Bandwidth and Teamwork

• Levels of emergent intelligence are affected by
  the Intellectual Bandwidth of Agents (humans,
  robots)
• Agents can exchange
• Data (narrow bandwidth)
• Knowledge (wide bandwidth)
• Wisdom (exceedingly wide bandwidth)

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Conclusions

• Intelligence is an emergent property of complex
  systems
• Artificial complex systems exhibit intelligent
  behaviour under certain conditions
• An appropriate degree of uncertainty (freedom to
  Agents)
• Wide Intellectual Bandwidth
• (exchange of knowledge)

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• Build complexity into an artefact to make it
  adaptable. to have artefacts of all kind
  capable of adapting and being resilient
• Professor George Rzevski