Rzevsky agent models of large systems


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Rzevsky agent models of large systems

  1. 1. Modelling Large Complex Systems Using Multi-Agent Technology George Rzevski Professor Emeritus, Complexity and Design, The Open University, UK Chairman, Knowledge Genesis Group, London, UK
  3. 3. Seven Criteria of Complexity <ul><ul><li>INTERDEPENDENCE – Complex systems consist of a large number of diverse interdependent components called Agents </li></ul></ul><ul><ul><li>AUTONOMY – Agents are partially autonomous (no central control) but subject to certain regulations and laws </li></ul></ul><ul><ul><li>EMERGENCE – Global behaviour emerges from Agent interaction and is therefore unpredictable </li></ul></ul><ul><ul><li>NON-EQUILIBRIUM – Systems operate far from equilibrium (at the edge of chaos) </li></ul></ul><ul><ul><li>NON-LINEARITY – Relations between Agents are nonlinear (butterfly effects, black swans) </li></ul></ul><ul><ul><li>SELF-ORGANIZATION – Systems self-organize in reaction to disruptive events (adaptability) or in order to improve performance (creativity) </li></ul></ul><ul><ul><li>CO-EVOLUTION – Complex systems co-evolve with their environments </li></ul></ul>
  4. 4. Classification of Systems RANDOM SYSTEMS COMPLEX SYSTEMS SYSTEMS IN EQUILIBRIUM 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 deviations temporary None Operating point None Far from equilibrium Equilibrium None
  5. 5. Where does Complexity Come From? <ul><li>As evolution takes its course the complexity of ecology, society, economy and technology increases </li></ul><ul><li>It seems that complex systems are better at surviving and prospering in tough environments and therefore complexity is favoured by selection </li></ul>
  6. 6. Stepwise Increase in Market Complexity Agricultural Economy Industrial Economy Knowledge Economy Agricultural Economy
  7. 7. Emergence of Exceedingly Complex Systems <ul><li>Global Cloud (global network) is emerging, which connects: </li></ul><ul><li>All computers, personal organisers, telephones, iPods, iPads, TV sets, DVD players, modems, film projectors, video players, hi-fi decks, radios </li></ul><ul><li>Most livestock and physical resources on the planet (by means of RFID tags) </li></ul><ul><li>Most people on the planet </li></ul><ul><li>Global Cloud stores: </li></ul><ul><li>All digital content, i.e., data, documents, images, films, videos, broadcasts, podcasts, newspapers, magazines, books, music, emails, blogs, websites </li></ul><ul><li>Global interaction of people over the Internet using: </li></ul><ul><li>email, Skype, social networks, Internet galleries, Internet clubs, business websites, eGovernment, eLearning, downloading and sharing music, exchanging photos and videos </li></ul>
  8. 8. Co-Evolution of Society, Economy & Technology Urban Society Industrial Economy Mass Production Technology Global Society Knowledge Economy Distributed Digital Technology Rural Society Agricultural Economy Elementary Tools land capital knowledge/ information KEY RESOURCES digital networks motorways & railways village roads DISTRIBUTION STAGES SCOPE local regional global SUCCESS FACTORS efficiency economy of scale adaptability
  9. 9. Evolution of English Language Chaucer Shakespeare
  10. 10. Evolution of Science Einstein Prigogine Newton
  11. 11. Evolution of Computing Monolithic algorithms Swarms of interacting agents + ontology
  12. 12. Modelling Complex Systems
  13. 13. How Can We Model Complexity? <ul><li>Complexity cannot be simplified </li></ul><ul><li>Complex systems cannot be controlled </li></ul><ul><li>Behaviour of complex systems cannot be predicted </li></ul><ul><li>BUT </li></ul><ul><li>Complexity can be managed </li></ul><ul><ul><li>External complexity can be neutralised by building complexity into artefacts (making them adaptable) </li></ul></ul><ul><ul><li>Internal complexity (model complexity) can be tuned </li></ul></ul>
  14. 14. Models of Complex Systems must be Complex <ul><li>Complex systems change as we attempt to construct their models and these changes must be incorporated in the model as they occur </li></ul><ul><li>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 </li></ul><ul><li>Models must be able to co-evolve with situations that they model </li></ul><ul><li>Therefore we can postulate: </li></ul><ul><li>Only complex models can be used to represent complex systems . </li></ul>
  15. 15. Technology for Modelling Complexity <ul><li>The only technology capable of modelling complexity is Multi-Agent Technology </li></ul><ul><li>Agent-based software can be defined as Complex Adaptive Software </li></ul><ul><li>A typical architecture of multi-agent software includes: </li></ul><ul><ul><li>Knowledge Base (Ontology + Data) </li></ul></ul><ul><ul><li>Virtual World populated by software agents </li></ul></ul><ul><ul><li>Runtime engine </li></ul></ul><ul><ul><li>interfaces </li></ul></ul>
  16. 16. How Agent-Based Software Works? <ul><li>Agents are small self-contained computational objects capable of exchanging messages among themselves </li></ul><ul><li>Demand Agents send messages to Resource Agents asking them to bid for demand fulfilments </li></ul><ul><li>Resource Agents send messages to Demand Agents with their bids </li></ul><ul><li>Before composing/answering a message Agents consult Enterprise Knowledge Base, where knowledge on how to conduct business is stored </li></ul>
  17. 17. Agent Based Models of Complex Systems Ontology Conceptual knowledge Data Virtual World Software Agents Modified State Data Factual knowledge Current State Complex Real System Current state Event Next state
  18. 18. Partitioning of Large Systems to Prevent Instability Social Unit 1 Social Unit 2 Social Unit 3 Social Unit 4
  19. 19. Emergent Intelligence <ul><li>Intelligence is capability to solve problems under conditions of uncertainty </li></ul><ul><li>In multi-agent systems intelligent behaviour emerges from the knowledge-driven conversation between agents </li></ul>
  20. 20. Examples
  21. 21. Multi-Agent Models in Commercial Use: <ul><li>Real-time scheduler for 2,000 taxis in London </li></ul><ul><li>Real-time scheduler for Avis European Operation </li></ul><ul><li>Real-time scheduler for 10% of world capacity of seagoing tankers </li></ul><ul><li>Real-time scheduler for cargo for the International Space Station </li></ul><ul><li>Real-time scheduler for road logistics operators in the UK and Russia </li></ul><ul><li>Real-time scheduler for one of the largest manufacturers in Russia </li></ul><ul><li>Real-time system for the allocation of advertisements </li></ul><ul><li>Real-time data mining for a London insurance company </li></ul><ul><li>Real-time risk management system for a financial service in London </li></ul><ul><li>Semantic search system for research organization in the USA </li></ul>
  22. 22. Architecture of the LEGO World of Agents Retail Agents Product Agents Order Agents Warehouse Agents Delivery Agents P1 P2 P3 P4 P5 P6 P7 P8 Enterprise Agent
  23. 23. A Family of Space Robots robot 3 robot 4 robot 5 robot 1 robot 2
  24. 24. Intelligent Geometry Compressor Stage 1 Agent Stage 2 Agent Stage 3 Agent Stage 4 Agent Coordinating Agent
  25. 25. Intelligent Logistics Network Supplier 1 store transporter transporter store store transporter Intelligent parcels Intelligent parcels Intelligent parcels Destination 1 Destination 2 store
  26. 26. Philosophy of Complexity
  27. 27. Newtonian (Deterministic) Worldview <ul><ul><li>The Universe was created according to a grand design </li></ul></ul><ul><ul><li>Laws of nature are independent of time and location </li></ul></ul><ul><ul><li>Uncertainty is due to our ignorance – “God does not play dice” </li></ul></ul><ul><ul><li>The role of science is to predict </li></ul></ul><ul><ul><li>Science will sooner or later discover universal laws of nature </li></ul></ul><ul><ul><li>All knowledge will be reduced to concepts and principles of physics - reductionism </li></ul></ul><ul><ul><li>Aristotle, Kant, Newton, Einstein </li></ul></ul>
  28. 28. Complexity Science Worldview <ul><li>The behaviour of the Universe emerges from interaction of its components and is inherently uncertain </li></ul><ul><li>The Universe evolves in time </li></ul><ul><li>Evolution is irreversible and leads to an increase in complexity </li></ul><ul><li>“ The future is not given” and therefore long term predictions are not po ssible </li></ul><ul><li>We must be satisfied with discovering likely patterns of behaviours (evolving predictions) </li></ul><ul><li>The future is under perpetual construction within constrains imposed by natural laws, social norms, etc </li></ul><ul><li>Buddha, Maxwell, Darwin, Popper, Prigogine </li></ul>