Complexity and Emergence in Robotics Systems Design Professor George Rzevski The 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 for o Real-time management (scheduling) o Knowledge discovery o Semantic analysis and search

Intelligence at Work knowledge, attitudes, values, mental skills, social skills Real World Cognitive/emotional filter: formal information system informal information system 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 uncertainty o Interpreting meaning of words and images o Recognising patterns o Learning from experience, by discovery and through communication o 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 Agricultural Society Industrial Society Information Society

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 Performance Agent Bookkeeping Agent Scheduling Agent Maintenance Agent

Intelligent Geometry Compressor Vane 1 Agent Vane 2 Agent Vane 3 Agent Vane 4 Agent Efficiency Agent

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-tractor 5 mini-tractor 1 mini-tractor 2

Global Logistics Network Supplier 1 store transporter store transporter Intelligent parcels Intelligent parcels Intelligent parcels Destination 1Destination 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