Building multi-agent systems using Jason

Abstract

We provide a detailed description of the Jason-DTU system, including the used methodology, tools as well as team strategy. We also discuss the experience gathered in the contest. In spring 2009 the course "Artificial Intelligence and MultiAgent Systems" was held for the first time on the Technical University of Denmark (DTU). A part of this course was a short introduction to the multi-agent framework Jason, which is an interpreter for AgentSpeak, an agent-oriented programming language. As the final project in this course a solution to the Multi-Agent Programming Contest from 2007, the Gold Miners scenario, was implemented. Finally we decided to participate in this year's contest with an implementation made in Jason as well.

Engineering a multi-agent system in Jason and CArtAgO

“…The name of our team is Jason-DTU. We participated in the Multi-Agent Programming Contest (MAPC) in 2009 and 2010 as the Jason-DTU team [2,6], in 2011 and 2012 as the Python-DTU team [3,9], in 2013 and 2014 as the GOAL-DTU team [8] and in 2015/2016 as the Python-DTU team [7]. 1 The MAPC 2017 scenario consists of two teams of agents, each moving through the streets of a realistic city as vehicles.…”

“…Jobs comprise the acquisition, assembling and delivering of items, utilizing certain facilities placed randomly on the map. 2 To earn more money than the opposing team, our multi-agent system uses centralized planning, deciding which jobs to solve; problem decomposition, dividing jobs into several tasks, which in turn are delegated to individual agents; and hierarchical planning, defining high-order plans on how to complete their assigned tasks.…”

Multi-agent programming contest 2016 - the Python-DTU team

“…The name of our team is Python-DTU. We participated in the contest in 2009 and 2010 as the Jason-DTU team (Boss et al, 2010;Vester et al, 2011) in 2011 and 2012 as the Python-DTU team (Ettienne et al, 2012;Villadsen et al, 2013b) and in 2013 and 2014 as the GOAL-DTU team (Villadsen et al, 2013a).…”

Design and application of a CA-BDI model to determine farmers’ land-use behavior

“…As one of the most popular models of agent decision making (Georgeff et al 1999 ), the belief-desire-intention (BDI) model has been widely used to construct reasoning systems for complex tasks in dynamic environments (BoSS et al 2010 ). There exist two major criticisms of traditional BDI: the one is that it assumes agents to behave in line with rationality (Wooldridge 2000 ); the other is that it does not provide any specification of agent communication, which it facilitated learning (Phung et al 2005 ).…”