COMP 522 Modelling and Simulation model everything Hans Vangheluwe - - PowerPoint PPT Presentation

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COMP 522 Modelling and Simulation model everything Hans Vangheluwe - - PowerPoint PPT Presentation

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Fall Term 2006 COMP 522 Modelling and Simulation model everything Hans Vangheluwe Modelling, Simulation and Design Lab (MSDL) School of Computer Science, McGill University, Montr eal, Canada Hans Vangheluwe hv@cs.mcgill.ca

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Fall Term 2006

COMP 522 Modelling and Simulation “model everything”

Hans Vangheluwe

Modelling, Simulation and Design Lab (MSDL) School of Computer Science, McGill University, Montr´ eal, Canada

Hans Vangheluwe hv@cs.mcgill.ca Modelling and Simulation 1/37

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A Variety of Complex Systems . . .

Need to be modelled

  • at most appropriate level of abstraction
  • in most appropriate formalism(s)

Hans Vangheluwe hv@cs.mcgill.ca Modelling and Simulation 3/37

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Available Information, Questions to be Answered, . . .

⇒ choice of Abstraction Level/Formalism

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Power Window

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Power Window: need for multiple formalisms

http://www.mathworks.com/products/demos/simulink/PowerWindow/html/PowerWindow1.html

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COMP 522A: Modelling and Simulation

  • . . . to study (static/dynamic) structure and (dynamic) behaviour
  • . . . for analysis and design of complex systems
  • . . . for different application domains:

computer networks, software design, traffic control, software engineering, biology, physics, chemistry, management, . . .

  • . . . implemented using Computer Science
  • . . . focus on Software Engineering

Hans Vangheluwe hv@cs.mcgill.ca Modelling and Simulation 7/37

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Overview

  • 1. What is Modelling and Simulation ?
  • 2. Which topics does COMP 522 cover ?
  • 3. What are the pre-requisites ?
  • 4. How is evaluation done ?
  • 5. What are the assignments about ?
  • 6. Where do I get the material covered in CS522 ?

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What is Modelling and Simulation ?

  • Modelling: represent/re-use/exchange knowledge

about system structure and behaviour

  • Simulation: to accurately and efficiently emulate real behaviour
  • Why ?

– cost, danger, . . . – what-if analysis ? – optimization (do it right the first time) ! ⇒ modelling and simulation based design

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Modelling and Simulation Based Design

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M&S in action: Flight Simulation

www.flightgear.org

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M&S in action: Environment

NATO’s Sarajevo WWTP www.nato.int/sfor/cimic/env-pro/waterpla.htm

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M&S in action: Environment

www.hemmis.com/products/west/

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M&S in action: Traffic

www.engr.utexas.edu/trafficSims/

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M&S in action: Training

USC Institute for Creative Technologies www.ict.usc.edu

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M&S in action: Game AI, Physics, Narratives, . . .

www.info.ea.com/company/company tw.php

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Modelling and Simulation . . .

  • . . . is Computer Science, Artificial Intelligence
  • . . . is Numerical Analysis, Computer Algebra
  • . . . is Systems Theory, Control Theory
  • . . . is Operations Research
  • . . . is Application Domain: Mechanical Engineering, . . .

. . . or something more GENERIC ?

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Real-World entity Base Model System S

  • nly study behaviour in

experimental context experiment within context Model M Simulation Results Experiment Observed Data

within context

simulate = virtual experiment Model Base a-priori knowledge

validation

REALITY MODEL GOALS

Modelling and Simulation Process

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COMP 522 Concept Map

Hierarchy of System Specifications Finite State Automata (FSA) Petri Nets DEVS GPSS Event Scheduling Activity Scanning Process Interaction Discrete Event Statecharts Hybrid Continuous-Time Forrester System Dynamics Population Dynamics Modelica (multi-physics) CSSLs continuous-time CBDs discrete-time CBDs time-less CBDs Causal Block Diagrams Formalisms Animation is theory behind visualized using

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Which topics does the course cover ?

  • 1. Modelling formalism syntax and semantics.

The Causal Block Diagram formalisms.

I O

^−1

IC IC 4.0 K 1.0 M 1.0 V0 0.0 X0

DEP INDEP

Print

DEP INDEP

Plot TIME

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  • 2. Untimed Discrete Event Formalisms:

(a) (non)Deterministic State Automata. (b) Adding Concurrency and Synchronisation: Petri Nets (e.g., specifying network protocols).

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(c) Adding Hierarchy and Orthogonality: Statecharts (e.g., UML, specifying reactive software).

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  • 3. Communicating Sequential Processes (CSP/kiltera).

process sender[output]: seq print ("sender","sending","message") wait 0 -> send "message" to output -> print ("sender","sent message") process receiver[input]: seq print ("receiver","waiting for input") wait 2 -> receive msg from input -> print ("receiver","received",msg) main channel a in par sender[a] receiver[a] Hans Vangheluwe hv@cs.mcgill.ca Modelling and Simulation 23/37

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  • 4. Timed Discrete Event Formalisms:

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(a) Event Scheduling. (b) Activity Scanning. (c) Three Phase Approach. (d) Process Interaction for queueing systems (GPSS).

FN1 12 2 V2 V1 PH 1 LR PH1 V1 H 2 P2 NE P1 S PH1 LNKS R PH1 1 LR PH2 R PH1 LNKS 1 S PH2 FN1 120 Function: 1 LNKS 10

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(e) DEVS as a rigourous basis for hierarchical modelling.

  • 5. Deterministic Simulation of Stochastic Processes:

(a) Pseudo Random Number Generation. (b) Gathering Statistics (performance metrics).

  • 6. Animation
  • 7. Continuous-time Formalisms:

(a) Ordinary Differential Equations, Algebraic Equations, Differential Algebraic Equations. (b) CSSLs: sorting and algebraic loop detection. (c) Forrester System Dynamics, Population Dynamics.

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Predator Prey Grazing_efficiency uptake_predator loss_prey predator_surplus_DR prey_surplus_BR

2−species predator−prey system

predator prey

trajectories

10 20 30 200 400 600

(d) Object-oriented Physical Systems Modelling: non-causal modelling, Modelica (www.modelica.org).

  • 8. Hierarchy of System Specifications, Systems Theory.

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Assignments cover these topics

  • 1. A Causal Block Diagram simulation tool.
  • 2. Petri Net model and analysis.
  • 3. Statechart model and software synthesis.
  • 4. kiltera model
  • 5. GPSS (process interaction) model of a queueing system.
  • 6. A DEVS model of a traffic system.

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Project

  • For a formalism of choice (possibly construct your own):

build a modelling and/or simulation environment.

  • Using an existing modelling/simulation system:

study a specific problem.

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Exam: mini-quiz(es)

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What are the pre-requisites ?

  • COMP 251 (data structures and algorithms),
  • COMP 302 (programming languages and paradigms),
  • COMP 350 (numerical computing).

. . . or equivalent (see me). Note:

  • all assignment/project programming in Python (where

appropriate)

  • no prior knowledge required, read Tutorial at www.python.org

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How is evaluation done ?

  • 60% on assignments.
  • 30% on the project (work, correctness, presentation).
  • 10% on a mini-quiz(es).

Together, assignments, mini-quiz(es) and project cover the entire course. Hence, there is no final exam.

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Assignment/project rules of the game ?

  • Completely in HTML form: requirements, design, code, discussion.
  • Submit via WebCT.
  • All coding in Python www.python.org (where appropriate).
  • Assignments and projects in teams of 2. Clearly describe work

distribution !

  • Original work, some presented in class.
  • Respect deadlines (or do more work to compensate).
  • Alternate subjects may be proposed.

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Need help ?

  • Use the discussion forum in WebCT
  • Come and see me Monday 16:00 - 18:00 in MC328
  • See the TA (Ximeng Sun) in MC202
  • Send TA or me e-mail
  • Talk to me after class or make an appointment
  • Assignments/projects are never fully specified ! Give feedback !

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Undergraduate or Graduate course ?

  • Challenging course (work load)
  • ”graduate” flavour (independent thinking)
  • some of the highest grades ever were obtained by ugrads

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What are the project subjects ?

  • Model/simulate a particular application (e.g., traffic, biology)
  • Build a modelling/simulation/animation tool for a particular

formalism Give suggestions !

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Where do I get the material covered in CS522 ?

moncs.cs.mcgill.ca/people/hv/teaching/MS/

  • Class presentations/notes online in PDF format.
  • Some handouts during the term.
  • Links and references for background info.

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