Architecture of Incident Management Systems. Ir. R. van der Krogt Ir. J. Zutt

Contents Architecture Replanning techniques Simulation (Mars)

Architecture (I) Planner Real World Plan Creates Execute in

Architecture (II) Planner Real WorldDiagnosis Replanner Plan Creates Execute in Adapts Calls Watches

Strategic and Tactical level PlannerReplanner ObservationsDiagnose Strategic Drive(truck1, Adam, Rdam) Load(truck1,cargo1) Drive(truck1, Rdam, Utrecht)... PlannerReplanner ObservationsDiagnose Tactical Accelerate(truck1,20) TurnDirection(truck1, 20°) TurnDirection(truck1, 0°)... Real World Example Plans:

Architecture (revisited) Planner Real WorldDiagnosis Replanner Plan 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. Resource of type “pink” Resource of type “blue”

Adding skills to a graph Extending a plan with a plan fragment. 1Find resources that are already available in the plan.

Adding skills to a graph Extending a plan with a plan fragment. 2Remove skills from the plan fragment that are obsolete.

Adding skills to a graph Extending a plan with a plan fragment. 3Link the plan fragment to the plan.

Adding skills to a graph Extending a plan with a plan fragment. 4Final 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) Entity behavioral model Behavior represented using (Timed) Finite State Machines

MARS experiment (1) Entity behavioral model Infrastructure: Behavior represented using (Timed) Finite State Machines Used by the mobile entities

MARS experiment (1) Entity behavioral 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) Entity behavioral 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) Entity behavioral model Infrastructure: Scenario (initial, goals, faults) Visual Model Diagnosis Replanner Strategic observations Planner Tactical observations Real World

MARS experiment (3) Entity behavioral 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 techniques will be applied.

--- The End ---

Behavioral models represented with Finite State Machines