1. Agent Based Learning Systems
Interfacing and Validating Models of the US Army TRAC Tactical War Game
Deborah Duong
Christopher Bladon
Agent Based Learning Systems
AHFE 2012

2. SIMmiddleware Translates Disparate Data between IW Models
SIMmiddleware enables analysts to apply IW data from one unique situation in the world to a different but unique situation in a model.
Input data, data traded between federated models, calibration data, and testing set data all have uncertainty of match as well as translation difficulties because of the different concepts used to arrange different data.
SIMmiddleware translates data to ensure one model “means the same thing” as another model.
If there is no exact match, SIM translates data probabilistically.
SIMmiddleware enables hybrid modeling.
Analysts can couple models “loosely” , at the level of general patterns, or tightly, at the level of details.
SIMmiddleware can integrate models at different levels of aggregation, for example, “tactical” and “operational/strategic”.
SIM enables data to be compared “apples to apples”.
Validation needs to be at the level of statistical patterns rather than single outcomes.
The real world is just one possible world, and simulations model many possible worlds.
We know a simulation is good if what is rare in the simulation is rare in the world, and what is common in the simulation is common in the world (under right circumstances).
SIMmiddleware enables analysts to express data in statistical patterns and dynamics.
After SIMmiddleware translates data to a common lexicon, SIMmiddleware compares data at the level of statistical patterns and dynamics to calculate a ‘distance’ score that measures the statistical distance between dynamic patterns.
SIMmiddleware’s distance score allows comparison of different versions of the same model, different scenarios, and models against data for a calibration or a validation score.
SIMmiddleware can validate a model against multiple real-world data sources with uncertain matches to the model.
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3. Iterative Hub and Spoke Architecture
Hybrid Model
Hybrid Model
Inference Engine
Inference Engine
Pave/CG Mediation Ontology
(Inference Engines:
Probabilistic Inference (Bayesian networks)
Logical Inference (Jena Micro OWL))
Pave/Nexus Mediation Ontology
(Inference Engines:
Probabilistic Inference (Bayesian networks)
Logical Inference (Jena Micro OWL))
Updated
Indicators
Pave
Hub
Ontology
Updated
Indicators
CG Move
CG Adjudication
Nexus Adjudication
Nexus Move
CG Ontology
(Cultural Geography Model)
Nexus Ontology
(Nexus Model)
Legend:
Input / Output
Ontology
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4. TWG 2010 Probabilistic Ontologies
CG ontology. Defines CG moves.
Nexus ontology. Defines Nexus moves.
PAVE ontology. Hub ontology for model. Contains PAVE moves and role player strategies, goals, and decision points.
PAVE CG Mediation ontology. Performs dynamic translation of PAVE tasks to CG moves.
PAVE Nexus Mediation ontology. Performs dynamic translation of PAVE tasks to Nexus moves.
Tactical Wargame 2010 ontology. Maintains states of the automated role player, such as OAB level/popularity and state of individual move.
Multi-Resolutional Bayesian ontology. Defines the macro and micro agents that are used to integrate multi-resolutional models.
TEO ontology. Defines events and outcomes.
Design of Experiment (DOE) ontology. Abstracts the concept of strategies, goals, and decision points in a doctrinal manner.
ProbOnt ontology. Holds the representation of the Bayesian networks that determine selection of events and outcomes.
Pakaf ontology. Holds the moves to the Helmand/PAKAF scenario.
PakafCgMediation. Automatically translates CG TWG moves to Helmand/PAKAF moves.
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5. Probabilistic Ontology Relationships
DOE
TEO
PAVE
ProbOnt
MultiResolutionalBayes
PaveCgMediation
ProbOnt
MultiResolutionalBayes
PaveNexusMediation CG
Nexus
Inheritance Hierarchy and Relationship Structure
of TWG Probabilistic Ontologies
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6. Implementation of Event Probabilities
The study team implemented a probabilistic translation from the moves of one model to another using Bayesian networks, from data on event likelihoods.
SIMmiddleware added Bayesian Networks, such as the one below, directly to the probabilistic ontology representation.
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7. The Hub Ontology Contains Study Specific Behaviors
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8. Crisp Mediation Ontologies Define Exact Translation Between the Hub and a Model
Cordon and Search Event Translates into ISAF Attacks Taliban Event if a Leader is Detained
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9. Unique Implementation of Probability in Ontologies
Probabilities are folded into the ontology itself in the form of Macro and Micro Agents
Macro Agents describe a group statistically
Micro Agents are individuals that may be generated from or may generate statistics
Micro Macro Agents enable Multi-resolutional model integration
Higher Level Models need only know trends in lower level models as opposed to details
Lower Level Models may be run multiple times to get trends, or trends may be taken over multiple individuals
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10. Macro Agent Defines Chances of Events that Determine an Exact Translation
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11. Each Distribution Contains Probabilities from the Bayesian Networks
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12. Micro-Agent Cordon and Search Event without a Detaining of Taliban Leader, is not an attack
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13. Micro-Agent Cordon and Search with a Detained Leader is interpreted as an Attack on the Taliban
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14. Preserving the Probabilistic Relations Between Models is important for Validation
In risk-based analysis, we want to track the chance that events will happen, proportionately filling in all possibilities to explore the state space
Once data is aligned correctly through the probabilistic ontology, it can be compared against other aligned data
Markov Processes express the output data over time and over multiple runs
Markov Processes can express what actually happened in the real world
Probabilistic Distance of Markov Processes can find how close the patterns in the simulation are to what happened in the real world
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15. Scenario 1 (TWG 2010). Blue Moves/Green Popularity
Level of Violence (blue player actions)
K = Kinetic
M = Medium Kinetic
N = Non-Kinetic
Popularity of Green
P = Popular
U = Unpopular
Level of Violence
Kinetic Medium Kinetic Non-Kinetic
.50
.43
.10
K P
M P
N P
Start Scenario
.14
.10
.20
.07
.70
Unpopular Popular
Level of Green Support
.36
.50
.40
.10
.29
.14
K U
M U
N U
.21
.40
.36
11/8/2018
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16. Scenario 2 (Excursion). Blue Moves/Green Popularity
Level of Violence (blue player actions)
K = Kinetic
M = Medium Kinetic
N = Non-Kinetic
Popularity of Green
P = Popular
U = Unpopular
Level of Violence
Kinetic Medium Kinetic Non-Kinetic
.14
.10
K P
M P
N P
Start Scenario
.10
.50
.14
.28
.70
Unpopular Popular
Level of Green Support
.50
.14
.10
.71
.14
K U
M U
N U
.57
.14
.28
.43
Probabilistic Distance from Scenario 1: 0.09
11/8/2018
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17. Validation – Markov Processes from Model and Real World
Popularity of Green
P = Popular
U = Unpopular
Level of Violence
K = Kinetic
M = Medium Kinetic
N = Non-Kinetic
State Definition
Model State Transitions
Real World (Afghan Nationwide Quarterly Assessment Review)
Apply Kullback-Leibler (KL) divergence to measure probabilistic distance between Markov processes:
Score of 0 means exact same Markov process. Score of 1 means the most different Markov process possible. Example above generated KL score = 0.21.
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18. Summary
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19. Validation depends on Proper Integration
Probabilistic Ontologies can Translate between Models, keeping track of uncertainty.
Probabilistic Ontologies can address Multi-Resolutional Model Translation.
Once translated, models are aligned. Once aligned, they can express output probabilities and can be aligned with real world data for probabilistic comparison
Markov Processes express the data dynamically for comparison at the level of patterns.
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20. Questions and Comments
POC: Deborah Duong
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