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]uniriotec.br
  • Adriana Santarosa Vivacqua
    Universidade Federal do Rio de Janeiro

Abstract

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.
  • Referencias
  • Cómo citar
  • Del mismo autor
  • Métricas
B. Baster, J. Duda, A. Maciol, and B. Rbiasz. Rule-based approach to human-like decision simulating in agent-based modeling and simulation. In 2013 17th International Conference on System Theory, Control and Computing (ICSTCC), pages 739-743, Oct 2013. - https://doi.org/10.1109/ICSTCC.2013.6689049

Michael Bratman. Intention, plans, and practical reason. 1987.

Adriana Braun, Soraia Raupp Musse, Luiz Paulo Luna de Oliveira, and Bardo EJ Bodmann. Modeling individual behaviors in crowd simulation. In Computer Animation and Social Agents, 2003. 16th International Conference on, pages 143-148. IEEE, 2003.

Davide Brugali and Katia Sycara. Towards agent oriented application frameworks. ACM Computing Surveys (CSUR), 32(1):21, 2000. - https://doi.org/10.1145/351936.351957

Ana C. B. Garcia, Nayat Sanchez-Pi, Luis Correia and José M. Molina. Multi-agent simulations for emergency situations in an airport scenario. Advances in Distributed Computing and Artificial Intelligence (1):69-78, 2012.

Victor R Lesser. Multiagent systems: An emerging subdiscipline of ai. ACM Computing Surveys (CSUR), 27(3):340-342, 1995. - https://doi.org/10.1145/212094.212121

Mei Ling Chu and Kincho Law. Computational framework incorporating human behaviors for egress simulations. 27:699-707, 11 2013. - https://doi.org/10.1061/(ASCE)CP.1943-5487.0000313

Charles M Macal. Everything you need to know about agent-based modelling and simulation. Journal of Simulation, 10(2):144-156, 2016. - https://doi.org/10.1057/jos.2016.7

Ram Meshulam, Ariel Felner, and Sarit Kraus. Utility-based multi-agent system for performing repeated navigation tasks. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, pages 887-894. ACM, 2005. - https://doi.org/10.1145/1082473.1082608

Y. Murakami, K. Minami, T. Kawasoe, and T. Ishida. Multi-agent simulation for crisis management. In Proceedings. IEEE Workshop on Knowledge Media Networking, pages 135-139, 2002.

Sanguk Noh and P. J. Gmytrasiewicz. Flexible multi-agent decision making under time pressure. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(5):697-707, Sept 2005. - https://doi.org/10.1109/TSMCA.2005.851797

Juliane Noveanu, Felix Amsler, Wolfgang Ummenhofer, Thomas von Wyl, and Mathias Zuercher. Assessment of simulated emergency scenarios: Are trained observers necessary? Prehospital Emergency Care, 21(4):511-524, 2017. - https://doi.org/10.1080/10903127.2017.1302528

Xiaoshan Pan, Charles S. Han, Ken Dauber, and Kincho H. Law. Human and social behavior in computational modeling and analysis of egress. 15:448-461, 07 2006. - https://doi.org/10.1016/j.autcon.2005.06.006

Ameya Shendarkar, Karthik Vasudevan, Seungho Lee, and Young-Jun Son. Crowd simulation for emergency response using BDI agents based on immersive virtual reality. Simulation Modelling Practice and Theory, 16(9):1415-1429, 2008. - https://doi.org/10.1016/j.simpat.2008.07.004

Young-Jun Son. An integrated human decision making model under extended belief-desire-intention framework. In Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pages 39-39. ACM, 2017.

David Szpilman, Billy Doyle, Jenny Smith, Rachel Griffiths, and Mike Tipton. Challenges and feasibility of applying reasoning and decision making for a lifeguard undertaking a rescue. International Journal of Emergency Mental Health and Human Resilience, 19(4):1-9, 2018. - https://doi.org/10.4172/1522-4821.1000379

Ashutosh Trivedi and Shrisha Rao. Agent-based modeling of emergency evacuations considering human panic behavior. IEEE Transactions on Computational Social Systems, 2018. - https://doi.org/10.1109/TCSS.2017.2783332

William L Waugh. Terrorism as hazard and disaster. In Handbook of Disaster Research, pages 123-143. Springer, 2018. - https://doi.org/10.1007/978-3-319-63254-4_7

Chao Zhang, Jiansong Wu, Chao Huang, and Bo Jiang. A model for the representation of emergency cases. Natural Hazards, 91(1):337-351, 2018. - https://doi.org/10.1007/s11069-017-3131-9

Yaying Zhang, Yuefeng Fu, and Wei Qiang. Modeling and control of urban expressways with emergency using hybrid petri nets. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 001483-001489, Oct 2016. - https://doi.org/10.1109/SMC.2016.7844447
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. https://doi.org/10.14201/ADCAIJ2018728191

Downloads

Download data is not yet available.

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.
+