Effective Agent Participation in Rescue Operations Brent Dutson CS7100 Fall 2009

Introduction In a disaster situation Rescuers need as much information as possible. Many search areas are inaccessible or too dangerous for humans to enter. The speed and power of hardware and its supporting software is continually improving.

Introduction (continued) Today we present a system architecture that uses computer agents to: – Search a disaster area – Collect large amounts of data – Analyze the collected data Use human agents to: – Direct the general activities – Analyze interesting data – Make key decisions The specific focus of the presentation deals with effective methods of communication between the various agents.

General Agent Architecture

Search Agent This agent performs the actual search of the disaster area. Capable of moving over rough terrain. Can perform the following: – Take pictures – Play audio – Record sound Remains with any survivors that are found. Each mobile agent has a corresponding computational agent that directs its movement and analyzes the collected data.

Search Agent (Continued) Begins in an IDLE state Requests work from the computational agent Moves to the search location Performs the search (photos, alert, audio) In Range of a communication agent – Sends data – Requests new work Out of range of a communication agent – Moves toward the main communication hub – When a communication agent is found Send data Becomes a communication agent

Area Communication Agent This agent provides a mobile communications platform. Capable of moving over rough terrain. Can transmit and receive multiple message simultaneously Operates as follow: – Begins in an IDLE state – Requests work – Moves to a location – Replaces the communication agent in the target location

Main Communication Agent This agent provides a main communication platform. Has limited mobility. Is hard wired to the command post. Can transmit and receive virtually unlimited messages simultaneously.

Computational Agent This agent provides the processing power behind the mobile agents. Has access to the master database. Sends work requests to the mobile agent. Receives search data and updates the database. Analyzes the search data for anything interesting.

Human Agent The human agent has ultimate control of the search. Can view all data. Analyzes data marked as interesting by other agents. Can prioritize specific regions of the search area. Can communicate with survivors through a search agent.

The Simulation The purpose of the simulation was to determine the effectiveness of Area Communication Agents. These agents are optional, but are included with the expectation that they will help speed up the search process. Search area was defined as a 200x200 cell grid. Total of 40,000 locations. A single main communication agent was placed at a random location in the grid. Each configuration was run 5 times with the main communication agent placed at a different random location for each run. Manhattan distance was used to measure distances between locations. Communication range for agents was set at 20.

The Simulation (continued) The number of mobile agents was varied from 20 to 100 including every value in-between. The percentage of total mobile agents that were assigned as area communication agents varied from 0% to 20% in 5% increments. Used a concept of time steps which is the length of time for a mobile agent to move from one location to the next. Communications were measured in time steps with: – Request = 1 step – Command = 1 step – Data = 3 steps

The Simulation (continued) Messages were sent by adding them to the queue of the next agent. The number of channels supported by area communication agents was either 5 or 20. The length of time that a communication agent would wait when there was no message traffic was either 20 or 100 time steps.

Results – Data Set 1

Results – Data Set 2

Results – Data Set 3

Best Results

Conclusions The results were unexpected The area communication agent didn’t help at all. In fact, it produced worse results. This was true both with 5 channels and with 20 channels. – Search agents may have been too spread out to take advantage of the greater communication capacity offered. – Area communication agents don’t perform any searches which reduces the overall search capacity Reducing the persistence time from 100 to 20 time steps did help the results significantly.

Recommendations Try the experiment using a more proactive area communication agent. Eliminate area communication agents altogether and use larger numbers of search agents.

Questions?