Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-26T06:26:33.041Z Has data issue: false hasContentIssue false
  • English
  • Français

BDI agents in social simulations: a survey

Published online by Cambridge University Press:  11 August 2016

Carole Adam  and
Benoit Gaudou
Carole Adam
Affiliation:
Grenoble Informatics Laboratory, University Grenoble Alpes IMAG, 700 avenue Centrale, campus universitaire, 38401 Saint-Martin d’HèresFrance e-mail: carole.adam@imag.fr
Benoit Gaudou
Affiliation:
Toulouse Institute of Computer Science Research, University Toulouse 1 Capitole 2 rue du Doyen Gabriel Marty, 31042 Toulouse Cedex 9, France e-mail: benoit.gaudou@ut-capitole.fr
Get access Rights & Permissions [Opens in a new window]

Abstract

Modelling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: ‘Keep It Simple, Stupid’. But the community is divided and a new principle appeared: KIDS, ‘Keep It Descriptive, Stupid’. Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as belief, desire and intention (BDI) agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Issue 3 , June 2016 , pp. 207 - 238
Copyright
© Cambridge University Press, 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adam, C. 2007. The Emotions: From Psychological Theories to Logical Formalisation and Implementation in a BDI Agent. PhD thesis, IRIT.Google Scholar
Adam, C., Beck, E. & Dugdale, J. 2015. SWIFT: simulations with intelligence for fire training. In ISCRAM, Poster.Google Scholar
Adam, C., Gaudou, B., Herzig, A. & Longin, D. 2006. OCC’s emotions: a formalization in a BDI logic. In AIMSA, Euzenat J. & Domingue J. (eds), LNAI 4183, 24–32. Springer.Google Scholar
Adam, C., Gaudou, B., Longin, D. & Lorini, E. 2011. Logical modeling of emotions for ambient intelligence. In Handbook of Research on Ambient Intelligence: Trends and Perspectives, Mastrogiovanni F. & Chong N.-Y. (eds). IGI Global, 108–127.Google Scholar
Adam, C., Herzig, A. & Longin, D. 2009. A logical formalization of the OCC theory of emotions. Synthese 168(2), 201–248.Google Scholar
Adam, C. & Longin, D. 2007. Endowing emotional agents with coping strategies: from emotions to emotional behaviour. In 7th Intelligent Virtual Agents (IVA), 348–349.Google Scholar
Adam, C. & Lorini, E. 2014. A BDI emotional reasoning engine for an artificial companion. In Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection, Demazeau, Y., Zambonelli, F., Corchado, J.M. & Bajo Pérez, J. (eds). Springer, 66–78.Google Scholar
Anderson, J. 1983. The Architecture of Cognition. Harvard University Press.Google Scholar
Anderson, J. 1993. Rules of the Mind. Erlbaum.Google Scholar
Anderson, J. & Lebiere, C. 1998. The Atomic Components of Thought. Erlbaum.Google Scholar
Axelrod, R. 1997. The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.Google Scholar
Axelrod, R. 2005. Advancing the art of simulation in the social sciences. In Handbook of Research on Nature Inspired Computing for Economy and Management, Rennard J.-P. (ed.). Idea Group, 21–40.Google Scholar
Axtell, R., Epstein, J., Dean, J., Gumerman, G., Swedlund, A., Harburger, J., Chakravarty, S, Hammond, R., Parker, J. & Parker, M. 2002. Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. In Proceedings of the National Academy of Sciences of the United States of America.CrossRefGoogle Scholar
Balasubramanian, V., Massguer, D. & Mehrotra, S. 2006. Drillsim: a simulation framework for emergency response drills. In Proceedings of ISCRAM.Google Scholar
Balke, T. & Gilbert, N. 2014. How do agents make decisions? A survey. Journal of Artificial Societies and Social Simulation 17, http://jasss.soc.surrey.ac.uk/17/4/13.html Google Scholar
Baptista, M. L., Martinho, C. R., Lima, F., Santos, P. A. & Prendinger, H. 2014. An agent-based model of consumer behaviour based on the BDI architecture and neoclassical theory. Developments in Business Simulation and Experiential Learning 41, 170–178.Google Scholar
Bazzan, A. L. C., Wahle, J. & Klügl, F. 1999. Agents in traffic modelling: from reactive to social behaviour. In KI-99: Advances in AI, Burgard W., Christaller T. & Cremers A. (eds), LNAI 1701, 303–306. Springer-Verlag.Google Scholar
Bordini, R., Hubner, J. & Wooldridge, M. 2007. Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley-Interscience.Google Scholar
Bosse, T., Gerritsen, C. & Treur, J. 2007a. Cognitive and social simulation of criminal behaviour: the intermittent explosive disorder case. In AAMAS.Google Scholar
Bosse, T., Gerritsen, C. & Treur, J. 2007b. Integrating rational choice and subjective biological and psychological factors in criminal behaviour models. In ICCM’07, 181–186.Google Scholar
Bosse, T., Gerritsen, C. & Treur, J. 2009. Towards integration of biological, psychological and social aspects in agent-based simulation of violent offenders. Simulation 85(10), 635–660.Google Scholar
Bosse, T., Hoogendoorn, M., Klein, M. C., Treur, J., Van Der Wal, C. N. & Van Wissen, A. 2013. Modelling collective decision making in groups and crowds: integrating social contagion and interacting emotions, beliefs and intentions. Autonomous Agents and Multi-Agent Systems 27(1), 52–84.CrossRefGoogle Scholar
Bosse, T., Memon, Z. A. & Treur, J. 2007. A two-level BDI-agent model for theory of mind and its use in social manipulation. In AISB.Google Scholar
Bratman, M. 1987. Intentions, Plans, and Practical Reason. Harvard University Press.Google Scholar
Broersen, J., Dastani, M., Hulstijn, J., Huang, Z. & van der Torre, L. 2001. The BOID architecture: conflicts between beliefs, obligations, intentions and desires. In AGENTS’01, ACM.CrossRefGoogle Scholar
Buford, J., Jakobson, G., Lewis, L., Parameswaran, N. & Ray, P. 2006. D-AESOP: a situation aware BDI agent system for disaster situation. In Agent Technology for Disaster Management.Google Scholar
Busetta, P., Ronnquist, R., Hodgson, A. & Lucas, A. 1999. Jack intelligent agents-components for intelligent agents in java. AgentLink News Letter 2, 2–5.Google Scholar
Caballero, A., Botla, J. & Gomez-Skarmeta, A. 2011. Using cognitive agents in social simulations. Engineering Applications of Artificial Intelligence 24(7), 1098–1109.Google Scholar
Caillou, P., Gaudou, B., Grignard, A., Truong, C. Q. & Taillandier, P. 2015. A simple-to-use BDI architecture for agent-based modeling and simulation. In 11th Conference of the European Social Simulation Association (ESSA).Google Scholar
Campenn, M., Andrighetto, G., Cecconi, F. & Conte, R. 2009. Normal = normative? The role of intelligent agents in norm innovation. In Normative Multi-Agent Systems.Google Scholar
Castelfranchi, C., Dignum, F., Jonker, C. M. & Treur, J. 2000. Deliberative normative agents: principles and architecture. In ATAL’99, LNCS 1757, 364–378. Springer-Verlag.Google Scholar
Cecconi, F. & Parisi, D. 1998. Individual versus social survival strategies. Journal of Artificial Societies and Social Simulation 1(2), http://jasss.soc.surrey.ac.uk/1/2/1.html Google Scholar
Cho, K., Iketani, N., Kikuchi, M., Nishimura, K., Hayashi, H. & Hattori, M. 2008. BDI model-based crowd simulation. In Intelligent Virtual Agents, 364–371. Springer.Google Scholar
Cirillo, R., Thimmapuram, P., Veselka, T., Koritarov, V., Conzelmann, G., Macal, C., Boyd, G., North, M., Overbye, T. & Cheng, X. 2006. Evaluating the potential impact of transmission constraints on the operation of a competitive electricity market in Illinois. Report ANL-06/16 for the Illinois Commerce Commission.Google Scholar
Cohen, P. R. & Levesque, H. J. 1990. Intention is choice with commitment. Artificial Intelligence Journal 42(2–3), 213–261.Google Scholar
Conte, R. & Castelfranchi, C. 1995. Understanding the functions of norms in social groups through simulation. In Artificial Societies: The Computer Simulation of Social Life, Gilbert N. & Conte R. (eds). Taylor & Francis, 252–267.Google Scholar
Cossentino, M., Chella, A., Lodato, C., Lopes, S., Ribino, P. & Seidita, V. 2012. A notation for modeling Jason-like BDI agents. In Complex, Intelligent and Software Intensive Systems (CISIS), 12–19. IEEE.Google Scholar
Criado, N., Argente, E. & Botti, V. 2010. A BDI architecture for normative decision making. In AAMAS, 1383–1384. IFAAMAS.Google Scholar
Criado, N., Argente, E., Noriega, P. & Botti, V. 2010. Towards a normative BDI architecture for norm compliance. In COIN@MALLOW, Fornara, N. & Vouros, G. (eds).Google Scholar
DeAngelis, D. & Gross, L. 1992. Individual-Based Models and Approaches in Ecology. Chapman and Hall.CrossRefGoogle Scholar
Dennett, D. 1989. The Intentional Stance. The MIT Press.Google Scholar
de Rosis, F., Pelachaud, C., Poggi, I., Carofiglio, V. & De Carolis, B. 2003. From Greta’s mind to her face: modelling the dynamics of affective states in a conversational embodied agent. International Journal of Human-Computer Studies 59(1–2, Special Issue on Application of Affective Computing in HCI), 81–118.Google Scholar
de Silva, L., Sardina, S. & Padgham, L. 2009. First principles planning in BDI systems. In AAMAS, 1105–1112.Google Scholar
Dignum, F., Morley, D., Sonenberg, E. & Cavedon, L. 2000. Towards socially sophisticated BDI agents. In ICCCN, 0111. IEEE Computer Society.Google Scholar
D’Inverno, M., Luck, M., Georgeff, M., Kinny, D. & Wooldridge, M. 2004. The dMARS architecture: a specification of the distributed multi-agent reasoning system. In AAMAS, 9, 5–53. Kluwer Academic Publishers..Google Scholar
Drogoul, A., Vanbergue, D. & Meurisse, T. 2002. Multi-agent based simulation: where are the agents?. In MABS, 1–15.Google Scholar
Edmonds, B. & Moss, S. 2005. From kiss to kids: an anti-simplistic modelling approach. In MABS, Davidsson P. (ed.), LNAI 3415, 130–144. Springer.Google Scholar
Elliott, C., Rickel, J. & Lester, J. 1999. Lifelike pedagogical agents and affective computing: an exploratory synthesis. In AI Today, Wooldridge M. & Veloso M. (eds), LNCS 1600, 195–212. Springer-Verlag.Google Scholar
Etienne, M., Du Toit, D. R. & Pollard, S. 2011. ARDI: a co-construction method for participatory modeling in natural resources management. Ecology and Society 16(1), 44.CrossRefGoogle Scholar
Evertsz, R., Thangarajah, J., Yadav, N. & Li, T. 2014. Tactics development framework (demo). In AAMAS, 1639–1640.Google Scholar
Evertsz, R., Thangarajah, J., Yadav, N. & Li, T. 2015. Agent oriented modelling of tactical decision making. In AAMAS, Bordini, R. H., Elkind, E., Weiss, G. & Yolum, P. (eds), 1051–1060, IFAAMAS.Google Scholar
Fähndrich, J., Ahrndt, S. & Albayrak, S. 2013. Self-explaining agents. Jurnal Teknologi 63(3), 147–154.Google Scholar
Farias, G. P., Dimuro, G. P. & da Rocha Costa, A. C. 2010. BDI agents with fuzzy perception for simulating decision making in environments with imperfect information. In MALLOW.Google Scholar
Farmer, J. D. & Foley, D. 2009. The economy needs agent-based modelling. Nature 460, 685–686.Google Scholar
Fernandes, P. & Nunes, U. 2008. Multi-agent architecture for simulation of traffic with communications. In International Conference on Informatics in Control, Automation and Robotics (ICINCO).Google Scholar
Finin, T., Fritzson, R., McKay, D. & Robin, M. 1994. KQML as an agent communication language. In Proceedings of the Third International Conference on Information and Knowledge Management.Google Scholar
FIPA 2002a. FIPA communicative act library specification. Foundation for Intelligent Physical Agents. http://www.fipa.org/specs/fipa00037/ Google Scholar
FIPA 2002b. FIPA contract net interaction protocol specification. Foundation for Intelligent Physical Agents. http://www.fipa.org/specs/fipa00029/ Google Scholar
Gardner, M. 1970. Mathematical Games. The fantastic combinations of John Conway’s new solitaire game ‘life’. Scientific American 223, 120–123.Google Scholar
Gasmi, N., Grignard, A., Drogoul, A., Gaudou, B., Taillandier, P., Tessier, O. & An, V. D. 2015. Reproducing and exploring past events using agent-based geo-historical models. In Multi-Agent-Based Simulation XV, 151–163. Springer.Google Scholar
Gaudou, B. 2008. Formalizing Social Attitudes in Modal Logic. PhD thesis, IRIT.Google Scholar
Gaudou, B., Marilleau, N. & Ho, T. V. 2011. Toward a methodology of collaborative modeling and simulation of complex systems. In Intelligent Networking, Collaborative Systems and Applications, 27–53. Springer.Google Scholar
Gaudou, B., Sibertin-Blanc, C., Therond, O., Amblard, F., Auda, Y., Arcangeli, J.-P., Balestrat, M., Charron-Moirez, M.-H., Gondet, E., Hong, Y., Lardy, R., Louail, T., Mayor, E., Panzoli, D., Sauvage, S., Sanchez-Pérez, J-M., Taillandier, P., Nguyen, V. B., Vavasseur, M. & Mazzega, P. D. 2013. The MAELIA multi-agent platform for integrated assessment of low-water management issues. In MABS, Multi-Agent-Based Simulation XIV-International Workshop (to appear, 2013).Google Scholar
Gilbert, N. & Troitzsch, K. G. 2005. Simulation for the Social Scientist—Second Edition. Open University Press.Google Scholar
Gil-Quijano, J., Piron, M. & Drogoul, A. 2007. Mechanisms of automated formation and evolution of social-groups: a multi-agent system to model the intra-urban mobilities of Bogota city. In Social Simulation : Technologies, Advances and New Discoveries, Chapter 12, Edmonds B., Hernandez C. & Troitzsch K. (eds). Idea Group Inc., 151–168.Google Scholar
Goldman, A. I. 2012. Theory of mind. In The Oxford Handbook of Philosophy of Cognitive Science, Margolis, E., Samuels, R. & Stich, S. P. (eds). Oxford Handbooks Online, http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780195309799.001.0001/oxfordhb-9780195309799-e-17 Google Scholar
Gomboc, D., Solomon, S., Core, M. G., Lane, H. C. & Lent, M. V. 2005. Design recommendations to support automated explanation and tutoring. In BRIMS05.Google Scholar
Gratch, J. & Marsella, S. 2004. A domain-independent framework for modeling emotion. Journal of Cognitive Systems Research 5(4), 269–306.Google Scholar
Gratch, J. & Marsella, S. 2005. Some lessons from emotion psychology for the design of lifelike characters. Journal of Applied Artificial Intelligence 19(3–4), Special Issue on Educational Agents -Beyond Virtual Tutors), 215–233.CrossRefGoogle Scholar
Gratch, J., Rickel, J., Andre, E., Badler, N., Cassell, J. & Petajan, E. 2002. Creating interactive virtual humans: some assembly required. IEEE Intelligent Systems 17(4), 54–63.Google Scholar
Grignard, A., Taillandier, P., Gaudou, B., Vo, D. A., Huynh, N. Q. & Drogoul, A. 2013. Gama 1.6: advancing the art of complex agent-based modeling and simulation. In Principles and Practice of Multi-Agent Systems, 117–131. Springer.Google Scholar
Guiraud, N., Longin, D., Lorini, E., Pesty, S. & Rivière, J. 2011. The face of emotions: a logical formalization of expressive speech acts. In The 10th International Conference on Autonomous Agents and Multiagent Systems, 3, 1031–1038. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Gunderson, L. & Brown, D. 2000. Using a multi-agent model to predict both physical and cyber criminal activity. In IEEE International Conference on Systems, Man, and Cybernetics 4, 2338–2343.Google Scholar
Harbers, M. 2011. Self-explaining agents. PhD thesis, Utrecht University.Google Scholar
Harbers, M., van den Bosch, K. & Meyer, J.-J. 2010. Explaining simulations through self-explaining agents. Journal of Artificial Societies and social simulation 13(1), http://jasss.soc.surrey.ac.uk/13/1/4.html Google Scholar
Heckbert, S. 2013. MayaSim: an agent-based model of the ancient Maya social-ecological system. Journal of Artificial Societies and Social Simulation 16(4), 11.Google Scholar
Heinze, C., Goss, S., Josefsson, T., Bennett, K., Waugh, S., Lloyd, I., Murray, G. & Oldfield, J. 2001. Interchanging agents and humans in military simulation. In IAAI.Google Scholar
Helbing, D., Farkas, I. & Vicsek, T. 2000. Simulating dynamical features of escape panic. Nature 407(6803), 487–490.Google Scholar
Helman, D. H. & Bahuguna, A. 1986. Explanation systems for computer simulations. In Proceedings of the 18th Conference on Winter Simulation, 453–459. ACM.Google Scholar
Hindriks, K., De Boer, F., Van der Hoek, W. & Meyer, J. 1999. Agent programming in 3APL. Autonomous Agents and Multi-Agent Systems 2(4), 357–401.Google Scholar
Hindriks, K. V., van Riemsdijk, M. B., Behrens, T., Korstanje, R., Kraaijenbrink, N., Pasman, W. & de Rijk, L. 2010. Unreal goal bots. connecting agents to complex dynamic environments. In AGS 2010.Google Scholar
Jones, H., Saunier, J. & Lourdeaux, D. 2009. Personality, emotions and physiology in a BDI agent architecture: the PEP—BDI model. In Web Intelligence and Intelligent Agent Technologies.Google Scholar
Karim, S. & Heinze, C. 2005. Experiences with the design and implementation of an agent-based autonomous UAV controller. In AAMAS, 19–26. ACM.Google Scholar
Kashif, A., Le, X. H. B., Dugdale, J. & Ploix, S. 2011. Agent based framework to simulate inhabitants’ behaviour in domestic settings for energy management. In ICAART, 190–199.Google Scholar
Koster, A., Schorlemmer, M. & Sabater-Mir, J. 2012. Opening the black box of trust: reasoning about trust models in a BDI agent. Journal of Logic and Computation 23(1), 25–58.Google Scholar
Kravari, K. & Bassiliades, N. 2015. A survey of agent platforms. Journal of Artificial Societies and Social Simulation 18(1), 11.Google Scholar
Laperriere, V., Badariotti, D., Banos, A. & Muller, J.-P. 2009. Structural validation of an individual-based model for plague epidemics simulation. Ecological Complexity 6(2), 102–112.Google Scholar
Lazarus, R. S. 1991. Emotions and Adaptation. Oxford University Press.Google Scholar
Lorini, E., Longin, D., Gaudou, B. & Herzig, A. 2009. The logic of acceptance: grounding institutions on agents attitudes. Journal of Logic and Computation 19(6), 901–940.Google Scholar
Lui, F., Connell, R. & Vaughan, J. 2002. An architecture to support autonomous command agents for onesaf testbed simulations. In SimTecT Conference.Google Scholar
Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K. & Balan, G. 2005. MASON: a multiagent simulation environment. Simulation 81(7), 517–527.Google Scholar
Macal, C. M. & North, M. J. 2005. Tutorial on agent-based modeling and simulation. In 37th Winter Simulation Conference. Introductory Tutorials: Agent-Based Modeling, 2–15.Google Scholar
Mcilroy, D. & Heinze, C. 1996. Air combat tactics implementation in the smart whole air mission model. In First International SimTecT Conference.Google Scholar
Minh, L. V., Adam, C., Canal, R., Gaudou, B., Vinh, H. T. & Taillandier, P. 2012. Simulation of the emotion dynamics in a group of agents in an evacuation situation. In Principles and Practice of Multi-Agent Systems, LNCS 7057, 604–619. Springer.Google Scholar
Molyneux, P. 2001. Postmortem: Lionhead Studios’ Black and White. Game Developer.Google Scholar
Moss, S., Pahl-Wostl, C. & Downing, T. 2001. Agent-based integrated assessment modelling: the example of climate change. Integrated Assessment 2(1), 17–30.Google Scholar
Murata, S., Arie, H., Ogata, T., Sugano, S. & Tani, J. 2014. Learning to generate proactive and reactive behavior using a dynamic neural network model with time-varying variance prediction mechanism. Advanced Robotics 28(17), 1189–1203.Google Scholar
Nagel, K. & Schreckenberg, M. 1992. A cellular automaton model for freeway traffic. Journal de physique 12(12), 2221–2229.Google Scholar
Nair, R., Tambe, M. & Marsella, S. 2005. The role of emotions in multiagent teamwork. In Who Needs Emotions: The Brain Meets the Robot, Fellous J.-M. & Arbib M. (eds), 311–329. Oxford University Press.Google Scholar
Neumann, M. 2010. Norm internalisation in human and artificial intelligence. Journal of Artificial Societies and Social Simulation 13(1), 12.Google Scholar
Newell, A. 1990. Unified Theories of Cognition. Harvard University Press.Google Scholar
Norling, E. 2003. Capturing the quake player: using a BDI agent to model human behaviour. In AAMAS, 1080–1081.Google Scholar
Norling, E. 2004. Folk psychology for human modeling: extending the BDI paradigm. In AAMAS.Google Scholar
Norling, E. J. 2009. Modelling Human Behaviour with BDI Agents. PhD thesis, University of Melbourne. http://cfpm.org/~emma/pubs/thesis.pdf Google Scholar
Noroozian, A., Hindriks, K. V. & Jonker, C. M. 2014. Towards simulating heterogeneous drivers with cognitive agents. In ICAART.Google Scholar
North, M. J., Collier, N. T., Ozik, J., Tatara, E. R., Macal, C. M., Bragen, M. & Sydelko, P. 2013. Complex adaptive systems modeling with repast simphony. Complex Adaptive Systems Modeling 1(1), 1–26.Google Scholar
Novák, P., Komenda, A., Cap, M., Voknnek, J. & Pechoucek, M. 2013. Simulated multi-robot tactical missions in urban warfare. In Multiagent Systems and Applications, 147–183. Springer.Google Scholar
Ortony, A., Clore, G. & Collins, A. 1988. The Cognitive Structure of Emotions. Cambridge University Press.Google Scholar
Ostrom, E. 2007. A general framework for analyzing sustainability of social-ecological systems. Proceedings of the Royal Society of London, Series B 274, 1931.Google Scholar
Oulhaci, M. A., Tranvouez, E., Fournier, S. & Espinasse, B. 2013. A multi-agent architecture for collaborative serious game applied to crisis management training: Improving adaptability of non played characters. In European Conference on Games Based Learning, 465.Google Scholar
Padgham, L., Scerri, D., Jayatilleke, G. & Hickmott, S. 2011. Integrating BDI reasoning into agent based modeling and simulation. In Proceedings of the Winter Simulation Conference, 345–356.Google Scholar
Padgham, L., Thangarajah, J. & Winikoff, M. 2008. Prometheus design tool. In 23rd AAAI Conference on AI, 1882–1883. AAAI Press.Google Scholar
Padgham, L. & Winikoff, M. 2002. Prometheus: a methodology for developing intelligent agents. In AOSE @ AAMAS.Google Scholar
Palazzo, L., Dolcini, G., Claudi, A., Biancucci, G., Sernani, P., Ippoliti, L., Salladini, L. & Dragoni, A. F. 2013. Spyke3d: a new computer games oriented BDI agent framework. In 2013 18th International Conference on Computer Games: AI, Animation, Mobile, Interactive Multimedia, Educational & Serious Games (CGAMES), 49–53. IEEE.Google Scholar
Paquet, S., Bernier, N. & Chaib-draa, B. 2004. DAMAS-rescue description paper. In Proceedings of RoboCup-2004: Robot Soccer World Cup VIII, 12. Springer-Verlag.Google Scholar
Park, S. I. 2013. Modeling Social Group Interactions For Realistic Crowd Behaviors. PhD thesis, Virginia Polytechnic Institute and State University.Google Scholar
Peinado, F., Cavazza, M. & Pizzi, D. 2008. Revisiting character-based affective storytelling under a narrative BDI framework. In Interactive Storytelling, 83–88. Springer.Google Scholar
Pereira, D., Oliveira, E., Moreira, N. & Sarmento, L. 2005. Towards an architecture for emotional BDI agents. In Twelfth Portuguese Conference on AI, 40–47.Google Scholar
Pokahr, A., Braubach, L. & Lamersdorf, W. 2005. Jadex: a BDI reasoning engine. In Multi-Agent Programming, 149–174. Springer.Google Scholar
Posada, M., Hernaandex, C. & Laopez-Paredes, A. 2008. Emissions permits auctions: an agent based model analysis. In Social Simulation: Technologies, Advances and New Discoveries, Chapter 14 Edmonds B., Troitzsch K. G. & Iglesias C. H. (eds). IGI Global, 180–191.Google Scholar
Rafael, H. B. & Jomi, F. H. 2009. Agent-Based Simulation Using BDI Programming in Jason. In Multi-Agent Systems: Simulation and Applications, Uhrmacher, A. M. & Weyns, D. (eds), 451–476. Taylor and Francis Group.Google Scholar
Rao, A. S. & Georgeff, M. P. 1991. Modeling rational agents within a BDI-architecture. In KR’91, Allen J. A., Fikes R. & Sandewall E. (eds). Morgan Kaufmann, 473–484.Google Scholar
Reynolds, C. 1987. Flocks, herds, and schools: a distributed behavior model. In SIGGRAPH.Google Scholar
Rickel, J., Gratch, J., Hill, R., Marsella, S. & Swartout, W. 2001. Steve goes to Bosnia: towards a new generation of virtual humans for interactive experiences. In AAAI Spring Symposium on Artificial Intelligence and Interactive Entertainment, Stanford University.Google Scholar
Rivière, J., Adam, C. & Pesty, S. 2012. A reasoning module to select ECAs communicative intention. In Intelligent Virtual Agents, 447–454. Springer.Google Scholar
Rizzo, A., Kenny, P. & Parsons, T. D. 2011. Intelligent virtual patients for training clinical skills. Journal of Virtual Reality & Broadcasting 8(3), https://www.jvrb.org/past-issues/8.2011/2902 Google Scholar
Ronald, N., Sterling, L. & Kirley, M. 2006. Evaluating Jack Sim for agent-based modelling of pedestrians. In Intelligent Agent Technology (IAT), 81–87. IEEE/WIC, ACM.CrossRefGoogle Scholar
Rönnquist, R. 2008. The goal oriented teams (Gorite) framework. In Programming Multi-Agent Systems, 27–41. Springer.Google Scholar
Rosenbloom, P. S., Laird, J. E. & Newell, A. 1993. The SOAR Papers: Research on Integrated Intelligence. MIT Press.Google Scholar
Sakellariou, I., Kefalas, P. & Stamatopoulou, I. 2008. Enhancing netlogo to simulate BDI communicating agents. AI: Theories, Models and Applications 5138, 263–275.Google Scholar
Sardina, S., de Silva, L. & Padgham, L. 2006. Hierarchical planning in BDI agent programming languages: a formal approach. In AAMAS’06, 1001–1008. ACM.Google Scholar
Savarimuthu, B. T. R. & Cranefield, S. 2009. A categorization of simulation works on norms. In Dagstuhl Seminar Proceedings 09121: Normative Multi-Agent Systems, 39–58.Google Scholar
Schattenberg, B. & Uhrmacher, A. 2001. Planning agents in James. Proceedings of the IEEE 89(2), 158–173.Google Scholar
Schelling, T. C. 1971. Dynamic models of segregation. Journal of Mathematical Sociology 1, 143–186.Google Scholar
Shendarkar, A., Vasudevan, K., Lee, S. & Son, Y.-J. 2006. Crowd simulation for emergency response using BDI agents based on immersive virtual reality. In Winter Simulation Conference, L. F. Perrone et al. (ed.), 545–553. ACM.Google Scholar
Shendarkar, A., Vasudevan, K., Lee, S. & Son, Y.-J. 2008. Crowd simulation for emergency response using BDI agents based on immersive virtual reality. Simulation Modelling Practice and Theory 16(9), 1415–1429.Google Scholar
Silverman, B. G., Badler, N. I., Pelechano, N. & O’Brien, K. 2005. Crowd simulation incorporating agent psychological models, roles and communication. Retrieved from http://repository.upenn.edu/hms/29 Google Scholar
Simoes, J. A. 2012. An agent-based/network approach to spatial epidemics. In Agent-Based Models of Geographical Systems, 591–610. Springer.Google Scholar
Singh, D., Sardina, S., Padgham, L. & James, G. 2011. Integrating learning into a BDI agent for environments with changing dynamics. In 22nd IJCAI.Google Scholar
Small, R. K. 2008. Agent smith: a real-time game-playing agent for interactive dynamic games. In Genetic and Evolutionary Computation Conference, 1839–1842.Google Scholar
Sokolova, M. V. & Fernández-Caballero, A. 2007. An agent-based decision support system for ecological-medical situation analysis. In Nature Inspired Problem-Solving Methods in Knowledge Engineering, 511–520. Springer.Google Scholar
Staller, A. & Petta, P. 2001. Introducing emotions into the computational study of social norms: a first evaluation. Journal of Artificial Societies and Social Simulation 4(1), http://jasss.soc.surrey.ac.uk/4/1/2.html Google Scholar
Sterman, J. D. 2006. Learning from evidence in a complex world. Journal of Public Health 96(3), 505–514.Google Scholar
Steunebrink, B., Dastani, M. & Meyer, J.-J. 2008. A formal model of emotions: integrating qualitative and quantitative aspects. In ECAI’08, 256–260. IOS Press.Google Scholar
Sun, R. 2002. Duality of the Mind. Lawrence Erlbaum Associates.Google Scholar
Sun, R. 2004. Desiderata for cognitive architectures. Philosophical Psychology 17(3), 341–373.Google Scholar
Sun, R. (ed.) 2006. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press.Google Scholar
Sun, R. 2007. Cognitive social simulation incorporating cognitive architectures. IEEE Intelligent Systems 22(5), 33–39.Google Scholar
Sun, R., Merrill, E. & Peterson, T. 2001. From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science 25(2), 203–244.Google Scholar
Sun, R. & Naveh, I. 2007. Social institution, cognition, and survival: a cognitive-social simulation. Mind & Society 6(2), 115–142.Google Scholar
Swartout, W., Gratch, J., Hill, R., Hovy, E., Marsella, S., Rickel, J. & Traum, D. 2006. Toward virtual humans. AI Magazine 27(2), 96–108.Google Scholar
Swartout, W., Paris, C. & Moore, J. 1991. Explanations in knowledge systems: design for explainable expert systems. IEEE Expert 6(3), 58–64.Google Scholar
Swarup, S., Eubank, S. G. & Marathe, M. V. 2014. Computational epidemiology as a challenge domain for multiagent systems. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, 1173–1176. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Taibi, T. 2010. Incorporating trust into the BDI architecture. International Journal of Artificial Intelligence and Soft Computing 2(3), 223–230.Google Scholar
Taillandier, P., Thrond, O. & Gaudou, B. 2012. A new BDI agent architecture based on the belief theory. Application to the modelling of cropping plan decision-making. In International Environmental Modelling and Software Society (iEMSs), Seppelt R., Voinov A. A., Lange S. & Bankamp D. (eds), 2463–2470. International Environmental Modelling and Software Society.Google Scholar
Tesfatsion, L. 2002. Agent-based computational economics: growing economies from the bottom up. Artificial Life 8, 55–82.Google Scholar
Thabet, I., Hanachi, C. & Ghaedira, K. 2009. Towards an adaptive grid scheduling: architecture and protocols specification. In Agent and Multi-Agent Systems: Technologies and Applications, 599–608. Springer.Google Scholar
Thierry, H., Vialatte, A., Choisis, J.-P., Gaudou, B. & Monteil, C. 2014. Managing agricultural landscapes for favouring ecosystem services provided by biodiversity: a spatially explicit model of crop rotations in the GAMA simulation platform. In International Environmental Modelling and Software Society (iEMSs), Ames D. P. & Quinn N. (eds). http://www.iemss.org/sites/iemss2014/papers/iemss2014_submission_37.pdf Google Scholar
Traum, D., Rickel, J., Gratch, J. & Marsella, S. 2003. Negotiation over tasks in hybrid human-agent teams for simulation-based training. In 2nd International Conference on Autonomous Agents and Multiagent Systems.Google Scholar
Traum, D., Swartout, W., Marsella, S. & Gratch, J. 2005. Fight, flight, or negotiate: believable strategies for conversing under crisis In 5th International Conference on Interactive Virtual Agents.Google Scholar
Urlings, P., Sioutis, C., Tweedale, J., Ichalkaranje, N. & Jain, L. 2006. A future framework for interfacing BDI agents in a real-time teaming environment. Journal of Network and Computer Applications 29(2-Innovations in agent collaboration), 105–123.Google Scholar
Van Truong, H., Beck, E., Dugdale, J. & Adam, C. 2013. Developing a model of evacuation after an earthquake in Lebanon. In ISCRAM-Vietnam.Google Scholar
von Wright, G. H. 1963. Norm and Action. Routledge and Kegan.Google Scholar
Wan, Y., Zhang, D.-y. & Jiang, Z.-h. 2013. Decision-making algorithm of an agent’s internal behavior facing artificial market. Soft Computing with Applications(SCA) 1(1), 20–27.Google Scholar
Wilensky, U. 1999. NetLogo. http://ccl.northwestern.edu/netlogo/ Google Scholar
Wilks, Y. (ed.) 2010. Close Engagements With Artificial Companions: Key Social, Psychological, Ethical and Design Issues, Natural Language Processing. John Benjamins Pub Co.Google Scholar
Wolfe, S. R., Sierhuis, M. & Jarvis, P. A. 2008. To BDI, or not to BDI: design choices in an agent-based traffic flow management simulation. In Spring Simulation Multiconference, 63–70. International Society for Computer Simulation.Google Scholar
Wooldridge, M. 2009. An Introduction to MultiAgent Systems - Second Edition. John Wiley & Sons.Google Scholar
Ye, L. R. & Johnson, P. E. 1995. The impact of explanation facilities on user acceptance of expert systems advice. MIS Quaterly 19(2), 157–172.Google Scholar
Zhao, X. & Son, Y.-J. 2007. BDI-based human decision-making model in automated manufacturing systems. International Journal of Modeling and Simulation 28(3), 347–356.Google Scholar