A Multi-Agent Simulator for Teaching Police Allocation

Abstract

This article describes the ExpertCop tutorial system, a simulator of the 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 the 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 implents 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 on simulation data and to formulate questions to the student about these patterns. Moreover, it explores the reasoning process of the domain agents by providing explanations that help the student to understand simulation events.