Bridging the Gap between ABM and MAS: A Disaster-Rescue Simulation Using Jason and NetLogo

Abstract

An agent is an autonomous computer system situated in an environment to fulfill a design objective. Multi-Agent Systems aim to solve problems in a flexible and robust way by assembling sets of agents interacting in cooperative or competitive ways for the sake of possibly common objectives. Multi-Agent Systems have been applied to several domains ranging from many industrial sectors, e-commerce, health and even entertainment. Agent-Based Modeling, a sort of Multi-Agent Systems, is a technique used to study complex systems in a wide range of domains. A natural or social system can be represented, modeled and explained through a simulation based on agents and interactions. Such a simulation can comprise a variety of agent architectures like reactive and cognitive agents. Despite cognitive agents being highly relevant to simulate social systems due their capability of modelling aspects of human behaviour ranging from individuals to crowds, they still have not been applied extensively. A challenging and socially relevant domain are the Disaster-Rescue simulations that can benefit from using cognitive agents to develop a realistic simulation. In this paper, a Multi-Agent System applied to the Disaster-Rescue domain involving cognitive agents based on the Belief–Desire–Intention architecture is presented. The system aims to bridge the gap in combining Agent-Based Modelling and Multi-Agent Systems approaches by integrating two major platforms in the field of Agent-Based Modeling and Belief-Desire Intention multi-agent systems, namely, NetLogo and Jason.