Evolving Agents in a Hostile Environment
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Transcript of Evolving Agents in a Hostile Environment
Evolving Agents in a Hostile Environment
Alex J. Berry
Overview
Motivation Background The Approach Map Generation Evolutionary Algorithm Experiments
Training First Responders
VEnOM Labs is developing a suite to train First Responders
Is the training effective? How can we make the training more
effective?
Goal
To develop a system to allow for friendly and hostile AI agents in the training environment.
To develop a system to create better agents for training First Responders.
Simulation of Adaptive Agents in a Hostile Environment[HW95]
Thomas Haynes Simple Agents Mines and Energy Experiments
Single Agent, Static and Random Environment Multiple Agent, Static and Random Environment
The Complicator
Aliases: Dr. T, Dr. Tauritz
Input: 2+2=4 Output:
0,45
%7
2)84)%42118798452((9303
162
The Approach
Randomly Generated Grid Environment Three Types of Agents:
First Responders Terrorists Victims
Genetic Programming to Evolve the Agents
Randomly Generated Maps
Any Dimension Percentage walls Bit Array to Hold the
Data
Demo
What’s an Agent to do?
Victims Move Randomly Remember Things Forget Things Survive
Terrorists Kill Victims Kill First Responders Lay Traps Disguise Themselves Not Get Caught
First Responders Help Victims Find and Disarm Traps Survive Catch Terrorists
Evolutionary Algorithm
Two Agents to Evolve First Responder Terrorist
Two Competing Evolving Populations Genetic Programming for the Evolutionary
Implementation
What An Individual Looks Like
Terminals Current Grid Location (C) Surrounding Grid Locations
(S) Rand (R)
Non-Terminals If-Then-Else
Threat And, Or, Not Victim, First Responder,
Terrorist, Trap Valid Move
Actions Save Kill Move Place Trap Remove Trap Not (Action) to invert an
Action
Sample Individuals
Move
Genetic Programming Evaluation
First Responder Victims Helped Terrorists Caught Traps Found Traps Removed Survival Amount of the Map
explored
Terrorist Kills using Traps Kills on Contact Effective Disguises Amount of Time
Survived
Experiments
Static Environment Evolution Random Environment Evolution Varying Ratios of First Responders, Victims, and
Terrorists Evolving one Population at a Time
Summary
Looking at agents operating in a hostile environment. First Responders, Terrorists, and Victims
Evolving first responders and terrorist using genetic programming techniques.
Future Work and Questions
Other Evolutionary Approaches LCS GP-LCS Hybrid
Integration into a 3D environment Playable Human Mode
Representations of Real Buildings Test effectiveness of adding this to Affective Intensity
Experiment Integration of other types of Traps and sensors to detect
those traps