A multiagent simulator for teaching police allocation.: An article from: AI Magazine

This digital document is an article from AI Magazine, published by Thomson Gale on September 22, 2006. The length of the article is 7770 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

From the author: This article describes the ExpertCop tutorial system, a simulator of crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the results with the help of an intelligent tutor, the pedagogical agent, observing how crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities as criminals and police teams drives the simulation. ExpertCop induces students to reflect on resource allocation. The pedagogical agent implements interaction strategies between the student and the geosimulator, designed to make simulated phenomena better understood. In particular, the agent uses a machine-learning algorithm to identify patterns in simulation data and to formulate questions to the student about these patterns.

Citation Details
Title: A multiagent simulator for teaching police allocation.
Author: Vasco Furtado
Publication:AI Magazine (Magazine/Journal)
Date: September 22, 2006
Publisher: Thomson Gale
Volume: 27 Issue: 3 Page: 63(12)

Distributed by Thomson Gale