1 Wright State University Biomedical, 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

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 R 2 = 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

NM NM 2 Non-overlapping Search Regions

NM 2 Overlapping Search Regions

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?