Multi-Agent Programming Contest 2013: The Teams and the Design of Their Systems

Ahlbrecht, Tobias; Bender-Saebelkampf, Christian; Brito, Maiquel de; Christensen, Nicolai Christian; Dix, Jürgen; Franco, Mariana Ramos; Heller, Hendrik; Hess, Andreas; Heßler, Axel; Hübner, Jomi Fred; Jensen, Andreas Schmidt; Johnsen, Jannick Boese; Köster, Michael; Li, Chengqian; Liu, Lu; Morato, Marcelo Menezes; Ørum, Philip Bratt; Schlesinger, Federico; Schmitz, Tiago Luiz; Sichman, Jaime Simão; Souza, Kaio Siqueira De; Uez, Daniela Maria; Villadsen, Jørgen; Werner, Sebastian; Woller, Øyvind Grønland; Zatelli, Maicon Rafael

doi:10.1007/978-3-642-45343-4_22

Cited by 5 publications

(4 citation statements)

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“…The purpose of the contest is twofold: (1) to find out for which applications agent-oriented features pay off, as opposed to features available in classical programming languages, and (2) to compare and test the versatility and suitability of agent languages or platforms. The main questions guiding us for the last decade were: To which extent, if any, are agent-oriented programming languages superior to classical languages and for which problem instances do we gain the most?…”

Section: Goals and Purpose Of The Mapcmentioning

confidence: 99%

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The Multi-Agent Programming Contest: A résumé

Ahlbrecht,

Dix,

Fiekas

et al. 2020

Preprint

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The Multi-Agent Programming Contest, MAPC, is an annual event organized since 2005 out of Clausthal University of Technology. Its aim is to investigate the potential of using decentralized, autonomously acting intelligent agents, by providing a complex scenario to be solved in a competitive environment. For this we need suitable benchmarks where agent-based systems can shine. We present previous editions of the contest and also its current scenario and results from its use in the 2019 MAPC with a special focus on its suitability. We conclude with lessons learned over the years.

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Section: Goals and Purpose Of The Mapcmentioning

confidence: 99%

“…In order to better understand the underlying strategies of the teams, we worked out a standardized questionnaire [1] (which was further improved over the years). This did not only help to learn about the systems and the results they produced, but also to understand the whole development process.…”

Section: Generalmentioning

confidence: 99%

The Multi-Agent Programming Contest: A résumé

Ahlbrecht,

Dix,

Fiekas

et al. 2020

Preprint

Self Cite

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“…Além disto, certos sistemas de simulação usados em competições como o da RoboCup 1 que foi analisado por Pereira et al (2012) e do MAPC 2 , descrito por Ahlbrecht et al (2013) se tornam mais complexos a cada ano, fazendo com que seja necessária uma maior capacidade de tomada de decisão para efetuar ações no ambiente.…”

Section: Motivaçãounclassified

“…Este aumento tem grande impacto quando se deseja integrar tais agentes a sistemas de simulação discretos externos, que demandam dos agentes respostas num limite de tempo pré-determinado. Em alguns casos, pode-se até mesmo inviabilizar o uso de agentes BDI em cenários muito competitivos, tais como RoboCup abordada por Pereira et al (2012) e MAPC abordado por Ahlbrecht et al (2013) Como forma de reduzir a ocorrência deste problema e dar maior segurança aos desenvolvedores no uso do Jason em sistemas de simulação, foi proposto neste trabalho a inclusão de um mecanismo de filtragem das percepções para o interpretador do Jason que visa remover percepções desnecessárias, de forma a reduzir o tempo de processamento dos agentes.…”

Section: Considerações Finaisunclassified

Melhorando o desempenho de agentes BDI Jason através de filtros de percepção

Junior¹,

Fernando²

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AgradecimentosAo professor Jaime Simão Sichman, pela orientação, confiança e por todo o tempo dedicado a este trabalho.Ao professor Fabio Kon, pelo apoio que possibilitou toda a análise estatística presente neste trabalho.Ao CNPq por ter possibilitado e financiado esta pesquisa.A todos os professores que depositaram em mim sua confiança, acreditando que eu poderia ir mais longe.A todos os meus familiares que me incentivaram para que eu continuasse sempre aprendendo mais.À minha companheira Bárbara, que sempre acreditou em mim, mesmo quando eu não acreditava.Por último e mais importante, a Deus, por ter iluminado meu caminho e tornado tudo isso When agents are supposed to be integrated to virtual environments virtual or simulators, one of the BDI paradigm's major concerns is the lack of control over the agents' perceptions. Without having any form of goal directed perceptions, the agent may be flooded by irrelevant information thus causing an unjustified increase in processing time. In order to provide greater control on the agent's perceptions and to reduce its time response, this work presents a filtering perception mechanism for the Jason interpreter, aimed at eliminating those perceptions that can be ignored. To this end, some types of pre-defined filters have been proposed, implemented, and applied to three different scenarios. Through appropriate statistical validation methods, it was shown that applying perception filters can reduce up to 80 % of an agent's processing time, without significantly affecting its performance measured in terms of its utility function.

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