modeling of complex systems and environments july 17th, 2007 agenda introduction of the laisc the...

Modeling of Complex Systems and Environments July 17th, 2007

Agenda

Introduction of the LaISC

The science of Complex Systems

Decision Making in Complex Systems – The CDM Project

Agent Modeling

Conclusion

Applying Negotiation Analysis & Pretopological Concepts to study Cockpit’s

Perspective to Collaborative Decision Making

Complex Dynamics Simulation for Air Traffic Management

Matthias Groppe - Marc Bui

Laboratoire d’Informatique et des Systèmes Complexes Ecole Pratique des Hautes Etudes, Paris-Sorbonne

July 17th, 2007


Agenda

Introduction to the LaISC



Agent Modeling

Conclusion

■ Introduction to the LaISC

■ The Science of Complex Systems

■ Decision Making in Complex Systems – The Collaborative Decision Making Project

■ Agent Modeling

■ Conclusion

AGENDA


Agenda




Agent Modeling

Conclusion

The LaISC

The Laboratoire d’Informatique et des Systèmes Complexes of l’Ecole Pratique des Etudes, Paris- La Sorbonne

The area of research is concerned with the theory and modeling of adaptive complex systems, especially networks without centralized control

Some subjects of research of the laboratory

Air Traffic ControlDistributed Decision Making Complexity


Agenda




Agent Modeling

Conclusion

Research Objectives of the LaISC

Conceive methods and theoretical means to study adaptive complex systems without having centralized control

To study emergent complex behavior

To study the results of complexity and complex systems


Agenda




Agent Modeling

Conclusion

Air Transport Management Systems: Scientific Approach

Based on lateral approach to the definition of

Availability & Complexity

- Availability of resources

- Number of Interactivities

Simulation via Multi-Agent Systems

Agents as entities that can make decisions based on data available

Modeled as rule-based expert systems in a hierarchical organization to facilitate interactivities like negotiation or information sharing.


Agenda




Agent Modeling

Conclusion


Agenda




Agent Modeling

Conclusion

The Science of Complex Systems

One research thread is, how self-organization emerge through the understanding of de-centralized control mechanism.

E.g. behavior of the flight of a group of birds


Agenda




Agent Modeling

Conclusion

The Science of Complex Systems (2)

Another important research thread is an understanding of networks and how their topology impacts the properties of the system.

Network of small worldsExamples

Internet pagesSocial networksAir routes Collaborative Decision Making


Agenda




Agent Modeling

Conclusion

General Definition of a Complex System

A complex system is a network composed of mutually interacting elements, where the global behavior of the system can not be deduced from the sum of its components and their properties.

Santa Fe Institute http://www.santafe.edu

New England Complex Systems Institute http://necsi.org


Agenda




Agent Modeling

Conclusion

Properties of Complex Systems

Emergence Phenomenon

Appearance of new property in the system

Phase Transition

Brutal change in the state of the system

Transition Threshold

Critical value or key parameter


Agenda




Agent Modeling

Conclusion

•Airlines‘ Schedule

•Planning

Information

•Flight Progress

Information

•Predictions

messages

•Status messages

•Operational

Planning

Information

Airport

Decision

Making

Database

Collaboration

Source: Eurocontrol (2004)

Example of Emergence in ATM Decision Making


Agenda




Agent Modeling

Conclusion

Decision Making in ATM Systems: A real complex thing

The Air Traffic in Europe

It‘s control is executed by Eurocontrol (36 countries)


Agenda




Agent Modeling

Conclusion

Air Traffic Management: some numbers

30 000 flights per day

Average distance of 750 km or 1h 30 minutes

15.000 controllers (55.000 personnel in total)

600 sectors

Cost of ATM in Europe: 7 Billion Euros/ Year


Agenda




Agent Modeling

Conclusion

ATM at Airports: The Capacity Crunch

E.g. Frankfurt International Airport:

1980: 220.000 flights & 18mil. passengers/ year

2000: 560.000 flights & 50mil. passengers/ year


Agenda




Agent Modeling

Conclusion

Fighting the Crunch: Introduction of Collaborative Decision Making (CDM)

Information sharing & coordination among all actors involved

Increasing of Common Situational Awareness


Agenda




Agent Modeling

Conclusion

The Collaborative Decision Making Concept (simplified)

Working together at operational level of Aircraft Operators, Ground Handling Agents, Airport, ATC & CFMU

From ad-hoc culture to global collaboration in planning & air traffic management


Agenda




Agent Modeling

Conclusion

Procedure of Collaborative Decision Making

Information Management (Airport, Security, Ground Handlers, ATC,

Central Flow Management Unit, Airlines)

A-CDM PlatformA-CDM Platform

Message Manager

Airline

ATC

DMDBDecision Database

Airport

Community

Ground Handlin

g


Agenda




Agent Modeling

Conclusion

ATM at Turnaround: The Pilot’s Perspective

Information Sharing & Coordination during Turnaround of an Airbus 330

40,5

12,0

13,4

13,7

19,1

5,5

0 10 20 30 40 50 60

minutes after aircraft at position

Deboarding

Catering

Cleaning

BoardingLoading

Fueling

Turnaround


Agenda




Agent Modeling

Conclusion

Pilot’s Perspective during Flight

Critical for Common Situational Awareness & Information Sharing: From Off-Block to On-Block

AO

BT

From Off-Block to On-Block!!

AIB

T


Agenda




Agent Modeling

Conclusion

Proposal: Adding the Pilot’s Perspective to CDM

AIM

Increasing Common Situational Awareness & Information Sharing/Cooperation by Studying Cockpit’s Perspective to CDM via:

Application of a methodological approach for problems encountered with Cockpit’s perspective to Collaborative Decision Making & Information Sharing/Cooperation


Agenda




Agent Modeling

Conclusion

Analogy for Information Sharing ‘en-route’: The Moving Cogwheels

Each tooth represents an essential information

Deadlock of one wheel means inactivity in proactive sharing of essential information

Moving of all wheels necessary for ATM operation

Each single missing essential information may stall turning of the wheels or reduce wheel speed


Agenda




Agent Modeling

Conclusion

Complexity of Collaborative Decision Making: The Subsystem ‘en-route’

There are a number of problems:

1. The amount of essential information needs to be determined, because Collaborative Decision Making includes routine essential and non-routine essential information

2. The difference in the way, how decisions are made by the pilots & controllers in similar situations has to be understood in order to increase cooperation.

3. A local optimal decision making leads not necessarily in a global optimum.

4. Other uncertainty factors (e.g. weather, technical problems….)

Problem

Which is the efficiency limit for a local optimization?


Agenda




Agent Modeling

Conclusion

Aspects of Complexity

(1) Structural Complexity

qualitative characteristics and topology of ATM

systems

(2) Complexity through System Dynamic

temporal effects of its behavior & the characteristics of the system

Interaction between (1) and (2)


Agenda




Agent Modeling

Conclusion

Applying Negotiation Analysis

(1) Structural Complexity

Individuals’ perspective towards decision making and selection criteria are determined via questionnaires & semi-structured interviews

NA combines individual & interactive perspective towards decision making

NA adds third perspective: joint decision making


Agenda




Agent Modeling

Conclusion

Applying Pretopological Concepts

Finding groups of interdependent elements (e.g. information-action)

Highlight homogenous groups/ elements (minimal or elementary closed subset)

Highlight groups containing the homogenous groups/elements (non-minimal elementary closed subsets or function MinimalClosedSubsets)

Structural Analysis: aims to find inclusion relation between elements

Aim: Aligning of all actors in a common performance framework


Agenda




Agent Modeling

Conclusion

Modeling of Agents

Agents, such as aircraft, airlines, and air traffic control are defined, which represent the behavior of the actor.Further there are several event classes defined to describe the behavior of the agentsEach event class has three options:

Action is accepted (agent is e.g. providing information/ instruction), rejected, or alternative is proposedEach actor has a different type of assessing the options (e.g. satisfying or maximizing behavior)


Agenda




Agent Modeling

Conclusion

Modeling of Agents (2)

Denote A = {ai; i = 1,…,N }, where a is one of N agents The ATM system is expressed as: s(t) = f(s(t), c(t), e(t), t), where s(t) is the state, and control c(t) includes different variables (discrete, binary, mixed), e(t) denotes the environmental variables (wind, speeds etc.) at a the time t. We further write for the sum of actions of A:Ζ =(a, u, T, p), where u is a sequence of actions that an M-member subset of agents A takes over at a given time: u AM

(t1,t2) = {uam (t1,t2): m = 1,

…..,M}; T = T1x…xTn as the set of all possible agent types; p: T→[0,1], where p(i,t) is the probability that agent i is of type t.


Agenda




Agent Modeling

Conclusion

Conclusion

Our approach to model Air Transport Management Systems allows:

To simulate decision making situations in variance of availability & complexity.

To identify the emergence phenomenon of collaboration.

To include a behavioral & cognitive perspective to decision making in complex systems.


Agenda




Agent Modeling

Conclusion

THANK YOU

modeling of complex systems and environments july 17th, 2007 agenda introduction of the laisc the...

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