Agent Modeling Study: Biomedical, Industrial & Human Factors Engineering at Wright State University

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

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. 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. 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. 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. 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. 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. 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. 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!

10. Snapshot of AFIT Model

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. 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. Results – No Adaptation • Response Surface Methodology model • Adjusted R2 = 0.947 Equilibrium Point, 0.7, 0.54

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. Adaptation Convergence

16. Adaptation Convergence

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. Non-overlapping Search Regions 200 NM2 350 NM2

19. Overlapping Search Regions 100 NM2 100 NM2

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

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

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

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. 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. Questions?