Wright state university biomedical industrial human factors eng bay of biscay agent modeling study l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 25

Wright State University Biomedical, Industrial & Human Factors Eng. Bay of Biscay, Agent Modeling Study PowerPoint PPT Presentation


  • 86 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

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

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Wright State UniversityBiomedical, 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

  • 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

  • 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?

  • 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

  • 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).

  • 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

  • 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

  • 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 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!


Snapshot of AFIT Model


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

  • 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

  • Response Surface Methodology model

    • Adjusted R2 = 0.947

Equilibrium Point, 0.7, 0.54


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)


Adaptation Convergence


Adaptation Convergence


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

200 NM2

350 NM2


Overlapping Search Regions

100 NM2

100 NM2


Non-overlapping Search Regions

Means Comparison—All Pairs (20 Iterations)

(Similar Letters Indicate Statistical Equivalence)


Non-overlapping Search Regions

Means Comparison—All Pairs (30 Iterations)

(Similar Letters Indicate Statistical Equivalence)


Overlapping Search Regions

Means Comparison—All Pairs (30 Iterations)

(Similar Letters Indicate Statistical Equivalence)


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

  • 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


Questions?


  • Login