Price: $2,999.00

Length: 3 Days
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Integrated Performance Modeling and Simulation Training

Integrated Performance Modeling and Simulation Training Course Description

Integrated Performance Modeling and Simulation Training course covers the rationale, tools, techniques, and calculations required for performance modeling.

Integrated Performance Modeling

Performance modeling captures and evaluates the dynamic behavior of human, computers, and communication systems. The magnitude and difficulty of many modern systems often lead to large, complex models, which are usually extremely difficult to be modeled. In this course, we teach you how to decompose such complex systems into subsystems that are smaller and easier to model.

TONEX Performance Modeling Training Features

  • The majority of the training (~70%) is dedicated to hands-on practices
  • Hands-on practices include labs, exercise with real-life examples, individuals/group activities, and hands-on workshops
  • Participants are encouraged to bring in their own project to model in the class under our instructor’s coaching, however, they can also use the projects provided by the instructor
  • The theoretical part of the class is delivered in the forma of dynamic, interactive presentation


Integrated Performance Modeling and Simulation Training is a 3-day course designed for the engineers and managers who are interested in modeling and simulating the performance of large, complex systems. However, the training can serve whoever interested in performance modeling.

Training Objectives

Upon the completion of Integrated Performance Modeling and Simulation Training, the attendees are able to:

  • Understand the concepts and principals of performance modeling
  • Apply appropriate tools and techniques to model performance management
  • Apply appropriate measurements in performance modeling
  • Understand and apply the Performance Evaluation Process Algebra (PEPA)
  • Conduct bisimulation
  • Model the complex systems by decomposing them first into small subsystems that are easier to model
  • Understand and apply equation laws
  • Apply equivalence concepts
  • Understand and execute Integrated Performance Modeling Environment (IPME)
  • Understand and apply Adaptive Control of Thought—Rational (ACT-R), Improved Performance Research Integration Tool (IMPRINT), Air Man-Machine Integrated Design and Analysis System (AIR MIDAS), DOMAR, and Attention-Situation Awareness (A-SA)
  • Understand and execute Human Operator Simulator (HOS)
  • Compare various models and choose what is the best for a given scenario

Course Outline

Overview of Integrated Performance Modeling and Simulation

  • Introduction
  • Performance Modeling
    • Queueing Networks
    • Stochastic Extensions of Petri Nets
  • Process Algebras
    • Timed Extensions of Process Algebras
    • Probabilistic Process Algebras
  • Process Algebra for Performance Modeling
    • Process Algebras as a Design Methodology
    • The “Cooperator” Paradigm and Hierarchical Models
    • Structure within Models

Performance Evaluation Process Algebra (PEPA)

  • Introduction
  • Design Objectives for PEPA
  • The PEPA Language
    • Informal Description Syntax
    • Execution Strategies and the Exponential Distribution
    • Examples
    • Passive Activities
    • Some Further Definitions
    • Formal Definition: Operational Semantics
    • Examples
  • Basic Properties
    • The Underlying Stochastic Model
    • Generating the Markov Process
    • Some Definitions
    • Stochastic Processes with an Equilibrium Distribution
    • PEPA Models with Equilibrium Behavior
    • Solving the Markov Process
    • Derivation of Performance Measures: Reward Structures
    • Example
  • Comparison to other Modeling Paradigms
  • Model Construction
  • Model Manipulation
  • Model Solution
  • Model of Environment
  • Operators Model (Monte Carlo Event Simulation engine)
  • Workspace models
  • Performance Shaping function Model
  • Communication Module
  • Measurement and Supporting Utilities

Human Operator Simulator (HOS)

  • Estimating The Time Of Atomic Elements Of Human Behavior
  • Human Performance Model Parameters
  • Task Time
  • Task Accuracy
  • Data Collection
  • Estimation Process
  • System’s Life Cycle
  • Human/Equipment/Environment Interaction Analysis

Concepts of Equivalence

  • Introduction
  • Process Algebras and Bisimulation
    • Bisimulation for Pure Process Algebras
    • Bisimulation for Timed Process Algebras
    • Bisimulation for Probabilistic Process Algebras
    • Bisimulation and Entity-to-Entity Equivalence
  • Performance Modelling and Equivalences
    • Performance Model Verification
    • Model-to-Model Equivalence
  • State-to-State Equivalence
    • Aggregation of Markov Processes
    • Lumpability
    • Folding in GSPNs (graphical representation of the system)
  • Notions of Equivalence for PEPA

Isomorphism and Weak Isomorphism

  • Introduction
  • Definition of Isomorphism
  • Properties of Isomorphism
  • Equation Laws for Isomorphic Components
  • The Expansion Law
  • Isomorphism as a Congruence
  • Isomorphism between System Components
  • Isomorphism and the Markov Process
  • Definition of Weak Isomorphism
  • Properties of Weak Isomorphism
  • Preservation by Combinators
  • Equational Laws for Weak Isomorphism
  • Weak Isomorphism and System Components
  • Weak Isomorphism and the Markov Process
  • Insensitivity of Reducible Sequences
  • Weak Isomorphism for Model Simplification
  • An Approach to Model Simplification
  • Simplifying a Microsoft Message Queuing (MSMQ) Model using Weak Isomorphism

Strong Bisimilarity

  • Introduction
  • Definition of Strong Bisimilarity
  • Properties of the Strong Bisimilarity Relation
  • Strong Bisimilarity as a Congruence
  • Isomorphism and Strong Bisimilarity
  • Strong Bisimilarity and System Components
  • Strong Bisimilarity and the Markov Process
  • Strong Bisimilarity for Model Simplification
  • An Approach to Model Simplification
  • Simplifying an MSMQ Model using Strong Bisimilarity

Strong Equivalence

  • Introduction
  • Definition of Strong Equivalence
  • Properties of the Strong Equivalence Relation
  • Strong Equivalence as a Congruence
  • Isomorphism and Strong Equivalence
  • Strong Bisimilarity and Strong Equivalence
  • Strong Equivalence and System Components
  • Strong Equivalence and the Markov Process
  • Strong Equivalence for Aggregation
  • Basic Application of Strong Equivalence Aggregation
  • Compositional Strong Equivalence Aggregation
  • Aggregating an MSMQ Model using Strong Equivalence

Human Performance Modeling Tools

  • Introduction
  • Adaptive Control of Thought—Rational (ACT-R)
  • Improved Performance Research Integration Tool (IMPRINT)
  • Air Man-Machine Integrated Design and Analysis System (AIR MIDAS)
  • Attention-Situation Awareness (A-SA)

An ACT-R Approach to Closing the Loop on Computational Cognitive Modeling Describing Dynamics of Interactive Decision Making and Attention Allocation

  • Introduction
  • Model vs Application
  • ACT-R Modeling
  • A Computational Cognitive Model of Taxi Navigation
    • The Model’s Environment
    • Task Analysis And Knowledge Engineering
    • Identifying Taxi Decision Heuristic
    • Detailed Description Of Dynamic Decision Modeling
    • Empirical Adequacy
    • Global Evidence Of Heuristic Reliance
    • Local Evidence Of Decision Heuristic Reliance
  • A Computational Cognitive Model Of The Impact Of Synthetic Vision Systems
    • Modeling And Task Analysis
    • The Main Empirical Findings
    • Evaluation And Validating The ACT-R Model
    • Discussion Of The Model Results

Modeling Behavior for More Complete Performance in Synthetic Environments

  • Learning
  • Expertise
  • Working Memory
  • Emotions and Behavioral Moderators
    • Further Uses of Emotions and Behavioral Moderators
    • Working Within a Cognitive Architecture
    • A Sketch of a Computational Theory of Emotions
  • Errors
    • Training About Errors
    • Models That Make Errors
  • Adversarial Problem Solving
  • Variance in Behavior
  • Information Overload

Modeling Behavior for Better Integration in Synthetic Environments

  • Perception
  • Combining Perception and Problem Solving
  • Integration of Psychology Theories
  • Integration and Reusability of Models

Modeling Behavior for Improved Usability in Synthetic Environments

  • Usability of the Models
  • Desired Accuracy of the Models
  • Aggregation and Disaggregation of Behaviors

Recent Development for Modeling

  • Data Gathering and Analysis Techniques
    • Advanced Al Approaches
    • Genetic Algorithms
    • Tabu Search
  • Multiple Criteria Heuristic Search
    • Psychologically Inspired Architectures
    • Elementary Perceiver and Memorizer
    • Neural Networks
    • Sparse Distributed Memories
    • PSI and Architectures That Include Emotions
    • COGENT
    • Hybrid Architectures
  • Knowledge-Based Systems and Agent Architectures
  • Architectural Ideas Behind the Sim_Agent Toolkit
    • Cognition and Affect
    • Sim_Agent and CogAff
  • Engineering-Based Architectures and Models
    • Apex
    • Simplified Model of Cognition and Contextual Control Model
  • Summary of Recent Developments for Modeling Behavior

Modeling Pilot Performance with an Integrated Task Network and Cognitive Architecture Approach

  • ACT-R
  • IMPRINT and ACT-R Integration
  • Human Performance Model of Pilot Navigation While Taxiing
    • IMPRINT Model
    • ACT-R Model
    • Results
  • Human Performance Model of Approach and Landing
    • IMPRINT Model
    • ACT-R Model
    • Communications Procedures
    • Findings and Implications
  • Extending the Approach and Landing Model
    • Generalizing the ACT-R Model through Learned Utility of Information Sources
    • Learning at Multiple Levels of Decomposed Tasks
    • Further Investigations Suggested by Learned Utility of Information Sources
  • Model Validation
    • Validation Model 1: Successful Task Completion
    • Validation Model 2: Assessing Subtask Correspondence

A Cross-Model Comparison

  • Introduction
  • Error Prediction
    • ACT-R
    • Air MIDAS
    • DOMAR
    • A-SA
  • External Environment
    • ACT-R
    • Air MIDAS
    • DOMAR
    • A-SA
  • Crew Communications
    • ACT-R
    • Air MIDAS
    • DOMAR
    • A-SA
  • Scheduling and Multitasking
    • Air MIDAS
    • DOMAR
    • A-SA
  • Memory
    • Air MIDAS
    • DOMAR
    • A-SA

Hands-On Activities

  • Labs
  • Individual/group activities
  • Hands-on workshops

TONEX Hands-On Workshop Sample: Multi-Server Multi-Queue Systems

  • Polling Systems
  • Solution of Polling System Models
  • APEPA Model of a Polling System
  • Multi-server Multi-queue Systems
  • Solutions of Multi-Server Multi-Queue Systems
  • PEPA Models of MSMQ Systems
  • MSMQ System with Cyclic Polling, Without Overtaking
  • Asymmetric MSMQ System with Cyclic Polling
  • Asymmetric MSMQ System with Random Polling
  • MSMQ System with Detailed Nodes

Exercise for Task Modeling:

  • Task Analysis To Calculate The Time It Takes To Perform An Action By Human
  • Calculation Of Error Rate In Task Analysis Case Study

TONEX Roundtable Discussion: Human Performance Modeling in Aviation

  • General Modeling Issues
  • Model Structures And Architectures
  • Models Used In Aviation
    • Domain Information Required
    • Modeling Errors And Performance
    • Latent Errors And Rare Unsafe Conditions
    • Aviation Issues
    • Reusing Models
  • Model Results And Validations
    • Model Assumptions
    • Model Validation
  • The Previous And Future Models
    • Successes And Failures
    • Possible Challenges
    • Future Recommendations

Integrated Performance Modeling and Simulation Training

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