ACoPla: a Multiagent Simulator to Study Individual Strategies in Dynamic Situations

  • Ana Cristina Bicharra Garcia
    Universidade Federal do Estado do Rio de Janeiro cristina.bicharra[at]
  • Adriana Santarosa Vivacqua
    Universidade Federal do Rio de Janeiro


One important issue in multi-agent systems is how to define agents’ interaction strategies in dynamic open environments. Generally, agents’ behaviors, such as being cooperative/altruistic or competitive/adversarial, are defined a priori by their creators. However, this is a weak premise when considering interaction among anonymous self-interested agents. Whenever agents meet, there is always a decision to be made: what is the best group interaction strategy? We argue that the answer depends on the amount of information required to make a decision and on the deadline proximity for accomplishing the task in hand. In certain situations, it is to the agents’ advantage to exchange information with others, while in other situations there are no incentives for them to spend time doing so. Understanding effective behaviors according to the decision- making scenario is still an open issue in multi-agent systems. In this paper, we present a multi-agent simulator (ACoPla) to understand the correlations between agents’ interaction strategy, decision-making context and successful task accomplishment rate. Additionally, we develop a case study in the domain of site evacuation to exemplify our findings. Through this study, we detect the types of conditions under which cooperation becomes the preferred strategy, as the environment changes.
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Bicharra Garcia, A. C., & Vivacqua, A. S. (2018). ACoPla: a Multiagent Simulator to Study Individual Strategies in Dynamic Situations. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7(2), 81–91.


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Author Biography

Ana Cristina Bicharra Garcia

Universidade Federal do Estado do Rio de Janeiro
I am a Full Professor at the Applied Informatics Department at the Federal University of the Sate of Rio de Janeiro, in Brazil. My background includes a PhD in Engineering, specialized in Artificial Intelligence, from the Stanford University (1987-1992), a research experience as a visiting scholar at Stanford (2002/2003) and MIT (2013/2014).I do research at the intersection of artificial intelligence and social computing, studying how computers can enhance human decision-making. I developed about 21 decision-support systems using machine learning, fuzzy logic, case-based reasoning, recommendation systems and ontology techniques.  I have a profound understanding of the specifics of real-world applications.I have over 200 publications (among journals, book chapters and conference papers), advised 8 PhD and 30 MSc students, founded and coordinated for over 20 years a research lab based on the technology I created during my PhD (ADDLabs) in which I coordinated 21 R&D projects funded by petroleum companies from 1994 till 2017. My approach is multi-disciplinary, and I am deeply committed to having a direct impact on solving real-world problems.