1 / 13

Evolving Agents in a Hostile Environment

Evolving Agents in a Hostile Environment. Alex J. Berry. 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?

emory
Download Presentation

Evolving Agents in a Hostile Environment

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evolving Agents in a Hostile Environment Alex J. Berry

  2. 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? • Environment lacks atonomous agents that can interact with trainees in the environment.

  3. Goal • Long Term • To develop a system to allow for friendly and hostile AI agents in the training environment. • Short Term • To develop a system to evolve agents in a hostile environment.

  4. Simulation of Adaptive Agents in a Hostile Environment[HW95] • Thomas Haynes • Used Genetic Programming • Simple Agents • Mines and Energy • Experiments • Single Agent, Static and Random Environment • Multiple Agent, Static and Random Environment

  5. The Approach • Randomly Generated Map Environment • Three Types of Agents: • First Responders • Terrorists • Victims • Genetic Programming to Evolve the Agents

  6. Maps • Any Dimension • Percentage walls • Bit Array to Hold the Data • Used for memory storage in the Agents

  7. Victims Move Randomly Remember Things Forget Things Survive Terrorists Kill Victims Kill First Responders Lay Traps Not Get Caught First Responders Help Victims Find and Disarm Traps Survive Catch Terrorists What’s an Agent to do?

  8. Evolutionary Algorithm • Two Agents to Evolve • First Responder • Terrorist • Mutation and Crossover are the only operators changed. • The individuals consist of expression and decision trees. • Initialization was based on both random and created individuals. • Rank Based Selection was used. • Elitist Competition was used.

  9. Terminals Current Grid Location (C) Surrounding Grid Locations (S) Rand (R) Memory (M) Non-Terminals If-Then-Else Threat And, Or, Not Victim, First Responder, Terrorist, Trap Valid Move Actions Save Kill Move Place Trap Remove Trap What An Individual Looks Like

  10. Sample Individual MOVE

  11. First Responder Victims Helped Terrorists Caught Traps Removed Survival Time Amount of the Map explored Terrorist Kills using Traps Kills on Contact Survival Time Deduction for killing other Terrorists Genetic Programming Evaluation

  12. Experiments • Static Environment Evolution • Random Environment Evolution • Varying Ratios of First Responders, Victims, and Terrorists • Evolving one Population at a time

More Related