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

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 negotiate deals until the best possible match between Demands and Resources is achieved Whenever an Event (new order, failure, delay) occurs they re-negotiate previously 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 Agent to 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 Agent is assigned to each word in a sentence; Word Agents through a process of negotiation construct:
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
Random
Systems (total chaos)
Complex
Systems
(far from equilibrium)
Steady-state Systems
(in stable equilibrium)
Algorithms Clocks
(pre-programmed)
Total uncertainty; no norms
Considerable uncertainty;
flexible norms of behaviour
Practically no uncertainty; elaborate rules of behaviour
Total certainty; every step prescribed
Random behaviour
Emergent behaviour
Planned behaviour
Deterministic behaviour
Disorganised
Self-organising
Organised and controlled
Rigidly structured
Random changes
Co-evolve with their environment
Small deviations possible
No changes possible

19. Examples Random Systems Complex Systems Steady-state Systems
Algorithms Clocks
Molecules at uniform temperature
Heat transfer;
Ecology; Human brain;
Language; Epidemics
Planetary system;
Atomic particles
Riot
Looting
Society;
Global economy;
Adaptive business processes;
Management team;
Command and control management
Automated production
Roulette
Dice
Multi-Agent Systems
The Internet
Car; Aircraft;
Ship
Conventional software;
clocks

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
Swarm 1
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