1. 1 Wright State University Biomedical, Industrial & Human Factors Eng. Bay of Biscay, Agent Modeling Study Raymond Hill Research sponsored by:

2. 2 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

3. 3 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?

4. 4 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

5. 5 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

6. 6 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

7. 7 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

8. 8 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!

9. 9 Efforts Completed

10. 10 Snapshot of AFIT Model

11. 11 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

12. 12 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

13. 13 Results – No Adaptation  Response Surface Methodology model  Adjusted R 2 = 0.947 Equilibrium Point, 0.7, 0.54

14. 14 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)

15. 15 Adaptation Convergence

16. 16 Adaptation Convergence

17. 17 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

18. 18 350 NM 2 200 NM 2 Non-overlapping Search Regions

19. 19 100 NM 2 Overlapping Search Regions

20. 20 Non-overlapping Search Regions Means Comparison—All Pairs (20 Iterations) (Similar Letters Indicate Statistical Equivalence)

21. 21 Non-overlapping Search Regions Means Comparison—All Pairs (30 Iterations) (Similar Letters Indicate Statistical Equivalence)

22. 22 Overlapping Search Regions Means Comparison—All Pairs (30 Iterations) (Similar Letters Indicate Statistical Equivalence)

23. 23 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

24. 24 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

25. 25 Questions?