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Case Injected Genetic Algorithms for Affordable Human Modeling Start Date: 11/15/02. Sushil J. Louis University of Nevada, Reno. John McDonnell SPAWAR San Diego. Case Injected Genetic AlgoRithms (CIGARs) combine genetic algorithms and case-based reasoning to address three problems.

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case injected genetic algorithms for affordable human modeling start date 11 15 02

Case Injected Genetic Algorithms for Affordable Human ModelingStart Date: 11/15/02

Sushil J. Louis

University of Nevada, Reno

John McDonnell

SPAWAR

San Diego

slide2
Case Injected Genetic AlgoRithms (CIGARs) combine genetic algorithms and case-based reasoning to address three problems
  • Affordability: The system will be used for both decision support and training. Dual use saves money and familiarizes trainees with battlefield systems
  • Human Modeling: CIGAR uses cases captured from humans during decision support, war-gaming, and training to bias genetic algorithm search toward human solutions
  • Quality of opponent: CIGAR automatically acquires knowledge (generates cases) by playing against itself. This bootstrapping leads to better quality opponents and reduces knowledge acquisition cost
case injected genetic algorithms cigar s combine genetic algorithm ga search with case based memory
Case Injected Genetic AlgoRithms (CIGAR)s combine Genetic Algorithm (GA) search with Case-Based Memory

GAs adapt solutions (cases) acquired from previously attempted problems to solve subsequent problems

We think of CIGAR as an optimization engine that acquires cases from problem solving or from humans and learns to increase performance with experience

Technical Objective:Prototype and validate CIGAR techniques for more robust, more affordable human behavior modeling

Problem 1

Problem 2

Problem 3

Human

CIGAR

cigar achieves more affordable decision support and training for naval applications

Training: Provide trainee with quality opponent (strike planner)

Training: Provide trainee with quality opponent (target/threat configurator)

Decision Support: Assist decision makerin configuration

Decision Support: Assist decision maker in time-critical strike

Cigar achieves more affordable decision support and training for naval applications

The Real-time Executive Decision Support (REDS) effort at SPAWAR will use CIGAR as an optimization engine in strike force weapon-target pairing.

Platforms

Targets and Threats

Transition objective: Generate multiple weapon-target pairing options in less than 4 minutes for 20 weapon-target pairs. Include SEAD support and METOC information

cigar is affordable
CIGAR is affordable

The system uses the same graphical user interface for decision support and for training. Decision makers use the interface to specify solutions to decision problems – these solutions are cases and are acquired as a by-product of operational use. Trainees use the same interface for training/wargaming. This dual use translates into significant cost savings and acquires domain knowledge

Motto: Fight as you train,

train as you fight

Fighting

Training

CIGAR

cigar produces high quality solutions
CIGAR produces high quality solutions

System use or operation by humans acquires cases representing domain knowledge. CIGAR also acquires cases as it solves problems generated by a problem generator. This offline knowledge acquisition will lead to better performance for training and for decision support.

Replacing the problem generator with CIGAR, we can evolve quality opponents

CIGAR

Problem

Generator

Problem 1

Problem 2

Problem 3

CIGAR

cigar acquires knowledge during problem solving

Candidate solutions

Cases

Case-base

Cases

Candidate solutions

CIGAR acquires knowledge during problem solving
  • Periodically save members of the GA’s population to the case-base
    • A member of the population is a candidate solution to the problem
  • Periodically inject appropriate cases into the GA’s population replacing low-fitness members

Save best individual

CBR module

Preprocessor

Genetic Algorithm

Preprocessor

Inject closest to the best

how does cigar operate
How does CIGAR operate ?
  • Which cases do we inject?
    • Inject cases that are closest to the current best member of the population. Genetic algorithms usually use binary encodings. For these encodings, our distance metric is therefore Hamming Distance – the number of differing bits.
      • GA theory points to other injection strategies
        • Probabilistic version: The probability of injection of a case in the case base is inversely proportional to distance from the current best member of the population relative to the distances of other cases.
  • How often should we inject cases?
    • Takeover time – number of generations needed for an individual to take over the population.

P(Casei) = (l – di)/∑(l – dj)

Hamming distance from best member

Chromosome length

Sum over all cases

expected behavior versus actual behavior

Expected behavior of a learning system

Learning system/CIGAR

However, we need to deal with few cases captured from humans and obtain (1) human-like and (2) high-quality solutions

Quality

No learning

Number of problems attempted

No learning

Time

This performance is with (1) a simple problem generator and (2) a case base that grows large

Learning system/CIGAR

Number of problems attempted

Expected behavior versus actual behavior

CIGAR behavior on 50 design problems

Avg. best fitness found within a max of 30 generations

Number of problems attempted

Avg. time taken to find best fitness

Number of problems attempted

RIGA = Randomly Initialized Genetic Algorithm

cigar solutions are similar to injected cases

Can injecting a fewcases captured from humans result in (1) high-quality and (2) timely solutions?

CIGAR solutions are similar to injected cases
  • The graph below displays hamming distance as a function of chromosome location.
    • At a number of locations, CIGAR solutions are more similar to each other
  • Other analysis shows that CIGAR solutions are descendants of injected cases

 When injected cases come from humans, CIGAR will tend to produce solutions similar to humans

Avg. hamming distance

Chromosome position (locus)

performance on weapon target pairing

Note that in this case CIGAR takes decreasing time but there is little difference in quality ?

Performance on weapon-target pairing

Objective function to maximize

effectiveness

value & risk

Given allocation X, U(X) depends on pilot proficiency with weapon & weapon’s effectiveness on target.

V(X) describes marginal effect of using multiple weapons

Y(X) depends on routing, SEAD, METOC…

we have built a foundation for delivering on research objectives year 1 deliverables
We have built a foundation for delivering on research objectives. Year 1 deliverables:
  • Deliverables related to technical objective
    • Affordability
      • Deploy a prototype GUI for weapon-target pairing support on the web
      • Demonstrate dual use for decision support and training/war-gaming
    • Human Modeling
      • A set of tools for case-base analysis
      • Analysis of empirical results from injecting cases acquired from a human expert. Techniques for dealing with few human cases
      • Techniques for combining CIGAR and human cases
  • Deliverables related to transitioning objective
    • Provide an optimization engine that integrates into the REDS-KSA architecture
years 2 and 3
Years 2 and 3
  • Related to the technical objective
    • Prototype and deploy a CIGAR system as the red-force against weapon-target pairing
    • Demonstrate competent red-force scenario generation against weapon-target pairing
    • Demonstrate techniques for co-evolving blue-red forces
    • Test and validate approaches to combining human generated cases with automatically acquired cases
  • Related to the transitioning objective
    • Demonstrate < 4 minutes for 20 weapon-target pairs with SEAD support/routing and METOC data
    • Other military applications
system architecture
System Architecture

Simulation

Comm.

Hub

Physics

Gfx

Defense

CIGAR

Defense

Planning

Decision Support

Battle

Authoring

Offense

CIGAR

Offense

Planning

Decision Support

GUIs

questions
Questions?

Tools being developed

http://gaslab.cs.unr.edu