Managing Business Complexity using Multi-Agent Technology: Practical Applications

1. Managing Business Complexity using Multi-Agent Technology:Practical Applications George Rzevski Professor Emeritus, Design and Complexity, The Open University, UK Founder and Chief Scientist, Magenta Corporation Ltd Founder and Chairman, Rzevski Solutions Ltd www.rzevski.net

2. Agenda • Fundamental Concepts • Solving Complex Business Problems • Complexity • Multi-Agent Technology

3. 1. Fundamental Concepts Stephen Hawking, The Newton Professor of Physics at Cambridge: "I think the next century will be the century of complexity"

4. What is Complexity? A situation is complex if: • It consists of a large number of diverse components engaged in unpredictable interaction (Variety and Uncertainty) • Its global behaviour emerges from the interaction of local behaviours of its components (Emergence) • It self-organises to accommodate unpredictable external or internal events and therefore its global behaviour is “far from equilibrium” or “at the edge of chaos” (Adaptability and Resilience) • It co-evolves with its environment (Irreversibility)

5. 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 • Civilisations; human communities; epidemics; terrorist networks; language • Free market; global economy; supply chains; teams; adaptive business processes • Artificial Complex Systems: multi-agent systems; the Internet

6. Modelling of Complex Situations Complex situations change during the modelling process Therefore tools for modelling complex situations must be complex (adaptable) - they must be able to build a model in a stepwise manner, accommodating every change as it occurs with a minimum disturbance to the unaffected parts of the model

7. Multi-Agent Software An Agent is a small computer object capable of composing, sending, receiving and interpreting messages. Agents operate in Swarms Agents consult problem domain knowledge (Ontology) before acting An Agent is assigned to every Demand and Resource with a task to negotiate the best possible deal for its client Demand Agents and Resource Agents negotiatedeals until the best possible match between Demands and Resources is achieved Whenever an Event (new order, failure, delay) occurs they re-negotiatepreviously agreed deals The allocation of Resources to Demands is represented as a current Scene (a network where Resources and Demands are nodes and their matchings are links)

8. 2. Solving Complex Business Problems • Reformulate the problem situation as “Allocation of Resources to Demands” • Collect relevant problem domain knowledge including policies, rules and constraints specific to this particular problem situation and construct Ontology • Assign an Agentto each resource and each demand and let them negotiate the allocation attempting to maintain balance between goals such as maximising profit, minimising risk, and delivering a given level of service • Whenever an unpredictable Event occurs let Agents re-negotiate previously agreed allocations to accommodate the new situation

9. Scheduling of Tankers • Crude Oil Transportation • 40 Ships – but huge size 300,000 tons • 10% of World Capacity • 500 Cargoes per year • Voyage costs £1million per 45 day voyage • Revenue depends on Spot Market • Example £2.6million for voyage to U.S. Gulf

10. A Typical Voyage

11. Airport Logistics: A Model of the Delivery of Meals Resource Agents Demand Agents Meal order Meal 1, 2, …n Flight Agent Loading bay order Loading bay 1, 2, …n Food Order Agent Truck order Truck 1, 2, …n Trolley order Trolley 1, 2, …n Route order Route 1, 2, …n Luggage Order Agent

12. Road Transportation Logistics The Problem • 4000 transportation instructions • 200 trucks of different capacities and with/without trailers • operating over the whole UK business network • primary and secondary deliveries between 600 locations • 3 cross-docks • 4 secure trailer swap locations • A considerable number of very small orders • dynamic routing, cross-docking • handling location availability windows and driver breaks • frequent occurrence of events such as arrival of new orders, change of orders, truck failures, road closures, non-show of loaders and drivers etc. The Solution • For 4000 orders with dynamical routing through 3 cross docks it took a multi-agent scheduler about 4 hours to build a schedule • The schedule shows strong consolidation of small orders onto trucks • It schedules new orders in real time (a few seconds for a new order)

13. Knowledge Discovery An Agent is assigned to each new data record; agents invite other agents to join a cluster; agents negotiate clustering conditions; Record Agents of similar records form clusters; an agent is assigned to each new cluster; Cluster Agents invite Record Agents of suitable records to join As a new record arrives (an Event) it may cause re-clustering; in dynamic situations clustering is a perpetual process Clusters may be represented as rules that describe a pattern; in dynamic situations patterns have a transient character Agents are capable of autonomously discovering all inherent patterns (without user intervention)

14. Semantic Search An Agentis assigned to each word in a sentence; Word Agents through a process of negotiationconstruct: • A Syntactic Descriptor of the text, which is a network of words linked by syntactic relations representing a grammatically correct sentence. • A Semantic Descriptor of the text, which is a network of grammatically and semantically compatible words, which represents a computer readable interpretation of the meaning of a text. If semantic ontology describes all possible meanings of words in a domain, a semantic descriptor describes the meaning of a particular text. To perform a semantic search Agents compare the Semantic Descriptor of the question with Semantic Descriptors of candidate answers and select the correct answer

15. Other Applications • Operating systems • Network management systems • Support for adaptive enterprises • Security • Decision Support

16. Conflict between Complex Markets and Conventional Systems New market conditions in car industry: The frequency of market driven changes is typically 1-2 hours Current ERP systems: The time required to modify conventional production plans to accommodate changes in orders is not less than 8-10 hours In global logistics, once a pallet is assigned to a pipeline its destination cannot be altered until it emerges from the other end of the pipeline, which may take several days

17. 3. Complexity Father of Complexity Science: Nobel prize winner Prigogine Major centre of research: Santa Fe Institute

18. A System Classification

19. Examples

20. Complexity and Evolution 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

21. Evolution of Society Information Society Industrial Society Agricultural Society

22. Evolution of Society key resources: distribution: stages: Agricultural Society land village roads Industrial Society capital motorways & railways Information Society knowledge digital networks

23. Evolution of English Language Shakespeare Chaucer

24. Evolution of Science Prigogine Einstein Newton

25. Complexity is an Opportunity We have to accept that complexity, and therefore uncertainty, is a norm and that attempts to simplify complex situations and to eliminate uncertainty, which was a useful managerial and technological philosophy in industrial society when complexity was manageable and uncertainty was small, is now harmful Complexity of markets can be exploited - it offers rich opportunities for those who master the mindset, skills and tools of adaptation and resilience.

26. 4. Multi-Agent Technology Multi-agent technology provides tools for building artificial Complex Adaptive Systems

27. Agents Modify the Current Model to Accommodate Real-life Events Real World Current state The next state Events Virtual World Current Scene Modified Scene Ontology Conceptual knowledge Data Data Factual knowledge values

28. Recursive Architecture Ontology & scenes Swarm1 Engine Simulator -------- Interfaces Swarm n Engine swarm Interface swarm Ontology & scenes Ontology & scenes Engine Engine

29. Ontology Ontology is conceptual knowledge of a problem domain Ontology is structured as a network where classes of objects (characterised by attributes, and rules of behaviour) are nodes and relations between objects are links

30. Scenes A Scene is a current (perpetually changing) model of a problem situation A Scene is structured as a network where instances of objects (defined as classes in ontology) are nodes and relations between them are links

31. Engine Engine is a collection of algorithms which • Activate and deactivate Agents • Allocate roles (Demand or Resource) to Agents • Update the current Scene