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Wright State University Biomedical, Industrial & Human Factors Eng. Bay of Biscay, Agent Modeling Study. Raymond Hill Research sponsored by:. Purpose. Update project with DMSO/AFRL presented at last year’s conference AFIT Operational Sciences Department WSU BIE Department

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wright state university biomedical industrial human factors eng bay of biscay agent modeling study

Wright State UniversityBiomedical, Industrial & Human Factors Eng. Bay of Biscay, Agent Modeling Study

Raymond Hill

Research sponsored by:

purpose
Purpose
  • Update project with DMSO/AFRL presented at last year’s conference
    • AFIT Operational Sciences Department
    • WSU BIE Department
  • Two pieces of work accomplished to date that I will discuss today
  • Some future plans
  • Suggestions and comments?
  • Sorry, I made minor changes last night
quick background on project
Quick Background on Project
  • Lots of interest in agent models
    • Project Albert work
    • Brawler modeling work
    • Next Generation Mission Model
  • Other agent model work as well
    • Adaptive interface agents
    • Intelligent software agents
    • Internet agents
  • Challenge is how to bring agent models into the higher level models?
why higher level modeling
Why Higher Level Modeling?
  • Need to better capture command and control effects
  • Need to capture “intangibles”
  • Need to model learning based on battlefield information
  • Need better representation of actual information use versus perfect use
  • Agents and agent models hold promise but bring along many issues
agent modeling challenges
Agent Modeling Challenges
  • Output analysis
    • Particularly with more complex models and models that are not necessarily replicable
  • Accurate human behavior modeling
    • In particular, command behavior modeling
  • Level of fidelity in model
    • Beyond that of bouncing dots
  • Interaction of agents and legacy modeling approaches
    • Brawler extensions into theater and campaign level modeling
agent modeling challenges cont
Agent Modeling Challenges (cont).
  • Human interaction with the models
  • The visual impact of interactions among the agents
  • “What if” analyses when human behavior is being modeled
  • Verification and Validation
the project
The Project
  • Need a “use case” for agent models
  • Dr McCue’s book great example of operational analysis
  • Bay of Biscay scenario amenable to agent modeling
    • Lots of information available
  • Forms a basis for subsequent research
efforts completed
Efforts Completed
  • Capt Ron “Greg” Carl (masters thesis)
    • Search theory focus - finished
  • Capt Joe Price (masters thesis)
    • Game theory focus - finished
  • Subhashini Ganapathy
    • Optimization study - finished
    • Entering PhD candidacy
  • Lance Champagne
    • Dissertation defense in early Fall
    • Same time twins are due!
efforts completed9
Efforts Completed
  • Capt Ron “Greg” Carl (masters thesis)
    • Search theory focus - finished
  • Capt Joe Price (masters thesis)
    • Game theory focus - finished
  • Subhashini Ganapathy
    • Optimization study - finished
    • Entering PhD candidacy
  • Lance Champagne
    • Dissertation defense in early Fall
    • Same time twins are due!
methodology game portion
Methodology - Game Portion
  • Allied search strategies
    • When to search? Day versus night?
  • German U-boat surfacing strategies
    • When to surface? Day versus night?
  • Two-person zero-sum game
    • Players: Allied search aircraft and German U-boats
    • Met rationality assumption
  • Non-perfect information
    • Neither side knows the exact strategy the other uses
  • Objective is number of U-boat detections
    • Allied goal: maximize
    • German goal: minimize
  • Zero-sum game
game formulation
Game Formulation
  • Allies: two pure search strategies
    • Only day and only night
  • Germans: two pure surfacing strategies
    • Only day and only night
  • Next step to include mixed strategies
    • Let parameter range from 0 to 1 as strategy
    • More interesting than simple pure strategy
    • Still more interesting with adaptation
  • Simple adaptation algorithm
    • Agents allowed to adapt strategy each month
results no adaptation
Results – No Adaptation
  • Response Surface Methodology model
    • Adjusted R2 = 0.947

Equilibrium Point, 0.7, 0.54

adaptation experiment
Adaptation Experiment
  • Both sides can adapt strategies (simple model)
  • Three design points chosen:
  • Adaptation occurs every month
  • Investigate results
  • 20 replications; 12-month warm-up; 12 months of statistics collection (April 1943 – February 1944)
methodology search portion
Methodology Search Portion
  • Design data compiled according to hierarchy
    • Historical fact
    • Published studies
    • Data derived from raw numbers
    • Good judgment
  • MOE is number of U-boat sightings
    • U-boat density constant between replications
    • Aircraft flight hours same between replications
    • Therefore, sightings = search efficiency
  • Two cases; search regions don’t overlap, do overlap
non overlapping search regions
Non-overlapping Search Regions

Means Comparison—All Pairs (20 Iterations)

(Similar Letters Indicate Statistical Equivalence)

non overlapping search regions21
Non-overlapping Search Regions

Means Comparison—All Pairs (30 Iterations)

(Similar Letters Indicate Statistical Equivalence)

overlapping search regions
Overlapping Search Regions

Means Comparison—All Pairs (30 Iterations)

(Similar Letters Indicate Statistical Equivalence)

future applications
Future Applications
  • Generalized architecture promotes re-use
    • Coast Guard Deep-water efforts
    • Air Force UAV search in rugged terrain or urban environments
  • Human-in-the-loop issues permeate
    • Search and rescue using UAVs
    • Reconnaissance using UAVs
    • Combat missions using UCAVs
future efforts
Future Efforts
  • Champagne completing dissertation
  • Ganapathy starting candidacy
    • Looked at simulation-based optimization
    • Examining human-mediated optimization techniques
    • Application to search and rescue or operational routing
  • Extensions planned
    • Extend game theory aspects
    • Further refinement of search results and optimization use
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