Architecture of Incident Management Systems.

Ir. R. van der Krogt

Ir. J. Zutt

Contents

• Architecture

• Replanning techniques

• Simulation (Mars)

Architecture (I)Planner

Real World

PlanPlanPlanPlan

Creates

Execute in

Architecture (II)Planner

Real World Diagnosis

Replanner

PlanPlanPlanPlan

Creates

Execute in

Adapts

Calls

Watches

Strategic and Tactical level

Planner Replanner

Observations Diagnose

StrategicDrive(truck1, Adam, Rdam)Load(truck1,cargo1)Drive(truck1, Rdam, Utrecht)

...

Planner Replanner

Observations Diagnose

TacticalAccelerate(truck1,20)TurnDirection(truck1, 20°)TurnDirection(truck1, 0°)

...

Real World

Example Plans:

Architecture (revisited)

Planner

Real World Diagnosis

Replanner

PlanPlanPlanPlan

• Planner creates a plan for each agent (possibly optimized using merging).

• Diagnosis module monitors the execu-tion and starts the replanner when it detects faults.

Replanner (I)

• Is started by the diagnosis module when it detects a contingency.

• Uses specialized algorithms to adapt the current (failing) plan to one that satisfies the goals.

• Tries to make as few changes as possible to the plan to avoid breaking existing commitments.

Replanner (II)

• Add actions

Replanner (II)

• Add actions

• Remove actions

Replanner (II)

• Add actions

• Remove actions

• Replace actions

Adding skills to a graph

• Extending a plan with a plan fragment.

P la nfra gm e nt

Resource oftype “pink”

Resource oftype “blue”

Adding skills to a graph

• Extending a plan with a plan fragment.

1 Find resources that are already available in the plan.

P la nfra gm e nt

Adding skills to a graph

• Extending a plan with a plan fragment.

2 Remove skills from the plan fragment that are obsolete.

P la nfra gm e nt

Adding skills to a graph

• Extending a plan with a plan fragment.

3 Link the plan fragment to the plan.

Adding skills to a graph

• Extending a plan with a plan fragment.

4 Final result: the extended plan.

Simulation

Real-world Simulation world.

Why do we need simulation?

• Validation of new techniques.

• Possibility to introduce faults for testing.

Multi-Agent Real-Time Simulator (MARS)

• Designed by TNO-TPD.

• Written in Java, interface to Matlab/Simulink.

• Multi-Agent future: support for multiple hosts / distributed simulation.

• Principally two parts: Base simulator + Experiment.

MARS experiment (1)

Entitybehavioral

model

Behavior represented using(Timed) Finite State Machines

MARS experiment (1)

Entitybehavioral

model

Infrastructure:

Behavior represented using(Timed) Finite State Machines

Used by the mobile entities

MARS experiment (1)

Entitybehavioral

model

Infrastructure:

Scenario

Behavior represented using(Timed) Finite State Machines

Used by the mobile entities

Initial setting, simulation goals and introducting faults

MARS experiment (1)

Entitybehavioral

model

Infrastructure:

Scenario

Visual Model

Behavior represented using(Timed) Finite State Machines

Used by the mobile entities

Initial setting, simulation goals and introducting faults

Visual information to display

MARS experiment (2)

Entitybehavioral

model

Infrastructure:

Scenario (initial,goals, faults)

Visual Model

Diagnosis

Replanner

Strategic observations

Planner

Tactical observations

Real World

MARS experiment (3)

Entitybehavioral

model

Infrastructure:

Scenario (initial,goals, faults)

Visual Model

Diagnosis

Replanner

Simulation step:- t time elapses.- Update entities.- Visualisation.

Strategic observations

Planner

Tactical observations

Real World

MARS demonstration

1. Taxi-cab simulator.

MARS demonstration

1. Taxi-cab simulator.

2. Transport Planning.

MARS demonstration

1. Taxi-cab simulator.

2. Transport Planning.

Support both layers(strategic and tactical).

Incident Management techniqueswill be applied.

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Behavioral models represented with Finite State Machines