Dynamic simulation of action at operations level

François Guerrin1,2
1Biometrics and Artificial Intelligence Research Unit, INRA, Toulouse, France
2Environmental Risks of Recycling Research Unit, CIRAD, Saint-Denis Cedex 09, France

Tóm tắt

The attempt of using lumped or agent-based simulation models to support operations management in production systems puts action modelling to the fore. To fill the gap of classical decision-support systems ignoring human agents’ practices, a modelling framework of action at operations level is proposed. This framework aims at answering two questions: How to represent action? How to represent the management of action? Every action (i.e., what is actually done by an agent) is represented by a binary function of time governed by events detected upon processes of various kinds: artefacts (clocks or schedules), external processes occurring in the environment, other actions. In turn, every action exerts its effect on target processes. This modelling framework allows one to simulate the interpretation of ongoing actions by using temporal or propositional logics and operations management behaviors through plan specification and execution, action composition, and resource allocation to concurrent actions. It enables complex activity systems to be represented and management options to be tested by simulation. These capacities are illustrated on the example of a farming system. The main benefits and issues raised by this dynamical system approach close to the ‘situated’ (vs. ‘planned’) action paradigm are discussed in the light of related works in Artificial intelligence. Future directions of research are drawn, namely that of how to scale up this lower-level representation of action to the higher-level representation of agents endowed with skills relevant at the level of the individual (e.g., anticipation).

Từ khóa


Tài liệu tham khảo

Agre P. (1995). Computational research on interaction and agency. Artificial Intelligence 72: 1–52

Allen J. (1984). Towards a general theory of action and time. Artificial Intelligence 23: 123–154

Antsaklis P., Koutsoukos X. and Zaytoon J. (1998). On hybrid control of complex systems: A survey. APII-JESA 32(9–10): 1023–1045

Aubry C., Paillat J.-M. and Guerrin F. (2006). A conceptual model of animal wastes management at the farm scale. The case of the Reunion Island. Agricultural Systems 88: 294–315

Beer R.D. (1997). The dynamics of adaptive behavior: A research program. Robotics and Autonomous Systems 20: 257–289

Brooks R.A. (1991). Intelligence without representation. Artificial Intelligence 47: 139–159

Chau P.Y. (1993). Decision support using traditional simulation and visual interactive simulation. Information and Decision Technologies 19: 63–76

Checkland P. (1993). Systems thinking, systems practice. Wiley, Chichester, UK

Chittaro L. and Montanari A. (2000). Temporal representation and reasoning in Artificial Intelligence: Issues and approaches. Annals of Mathematics and Artificial Intelligence 28: 47–106

Clancey W. (1997). Situated cognition: On human knowledge and computer representation. Cambridge University Press, Cambridge, MA, USA

Clancey W. (2002). Simulating activities: Relating motives, deliberation and attentive coordination. Journal of Cognitive Systems Research 3: 471–499

Courdier, R., Guerrin, F., Andriamasinoro, F.-H., & Paillat, J.-M. (2002). Agent-based simulation of complex systems: Application to collective management of animal wastes. Journal of Artificial Societies and Social Simulation, 5(3), http://jasss.soc.surrey.ac.uk/5/3/4.html.

Gibson J. (1979). The ecological approach to visual perception. Houghton Mifflin, Boston, MA, USA

Gr[["u]]ninger, M., & Pinto, J. (1995). A theory of complex actions for enterprise modelling. In Proc. Working Notes AAAI Spring Symposium 1995, Extending Theories of Action: Formal Theory and Practical Applications. Stanford, CA.

Guerrin F. (2001). Magma: A model to help manage animal wastes at the farm level. Computers and Electronics in Agriculture 33(1): 35–54

Guerrin F. (2004). Simulation of stock control policies in a two-stage production system—application to pig slurry management involving multiple farms. Computers and Electronics in Agriculture 45(1–3): 27–50

Guerrin, F. (2005). Simulation of action in production systems. In Proc. Modsim 2005, International Congress on Modelling and Simulation, Advances and Application for Management and Decision-Making (pp. 210–216). Melbourne, Australia, December 12–15, 2005.

Guerrin, F., & Médoc, J.-M. (2005). A simulation approach to evaluate supply policies of a pig slurry treatment plant by multiple farms. In Proc. Efita-WCCA Joint 5th Conference of the European Federation for Information Technology in Agriculture, Food and Environment and 3rd World Congress on Computers in Agriculture and Natural Resources, Vila Real, Portugal, July 25–28, 2005, Paper B03.3/PA305.

Helleboogh A., Vizzari G., Uhrmacher A. and Michel F. (2007). Modeling dynamic environments in multi-agent simulation. Autonomous Agents and Multi-Agent Systems 14: 87–116

Hirose N. (2002). An ecological approach to embodiment and cognition. Cognitive Systems Research 3: 289–299

Hélias A., Guerrin F. and Steyer J.-P. (2008). Using timed automata and model-checking to simulate material flow in agricultural production systems-Application to animal waste management. Computers and Electronics in Agriculture 63(2): 183–192

Jennings N., Sycara K. and Wooldridge M. (1998). A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems 1: 7–38

Johnston, R. (1998). The problem with planning: The significance of theories of activity for operations management. PhD thesis, School of Business Systems, Monash University, Australia.

Johnston R. and Brennan M. (1996). Planning or organizing: The implications of theories of activity for management of operations. Omega International Journal of Management Science 24(4): 367–384

Johnston R., Waller V. and Milton S. (2005). Situated information systems: Supporting routine activity in organisations. International Journal of Business Information Systems 1(1–2): 53–82

Kowalski R. and Sergot M. (1986). A logic-based calculus of events. New Generation Computing 4: 67–95

Lejeune M. and Yakova N. (2005). On characterizing the 4 C’s in supply chain management. Journal of Operations Management 23: 81–100

Martin-Clouaire, R., & Rellier, J.-P. (2005). Representing and interpreting flexible production management plans. In Proc. CMS’05, International Conference on Conceptual Modeling and Simulation, Marseille, France, 2005.

McCown R. (2002). Locating agricultural decision support systems in the troubled past and socio- technical complexity of ‘models for management’. Agricultural Systems 74(1): 11–25

O’Neill R., Angelis D.D., Waide J. and Allen T. (1986). A hierarchical concept of ecosystems. Princeton University Press, Princeton, NJ, USA

Peterson, D., & Parke, V. (1998). Ecological scale; theory and applications. Complexity in ecological systems. Columbia University Press.

Platon E., Mamei M., Sabouret N., Honiden S. and van Dyke Parunak H. (2007). Mechanisms for environments in multi-agent systems: Survey and opportunities. Autonomous Agents and Multi-Agent Systems 14: 31–47

Rao, A., & Georgeff, M. (1995). BDI agents: From theory to practice. In Proc. ICMAS, 1st International Conference on Multi-agent Systems, San Francisco, CA, 1995.

Reed E. (1996). Encountering the world: Toward an ecological psychology. Oxford University Press, New York, USA

Schmidt K. and Simone C. (1996). Coordination mechanisms: Towards a conceptual foundation of CSCW systems design. Computer Supported Cooperative Work: The Journal of Collaborative Computing 5: 155–200

Sierhuis, M. (2001). Modeling and Simulating Work Practice, SIKS Dissertation Series No. 2001-10. The Netherlands: University of Amsterdam.

Suchman L. (1987). Plans and situated actions: The problem of human–machine communication. Cambridge University Press, Cambridge, MA, USA

Susi T. and Ziemke T. (2001). Social cognition, artefacts and stigmergy: A comparative analysis of theoretical frameworks for the understanding of artefact-mediated collaborative activity. Journal of Cognitive Systems Research 2: 273–290

Thornton P. and Herrero M. (2001). Integrated crop-livestock simulation models for scenario analysis and impact assessment. Agricultural Systems 70: 581–602

Tsoukis A. (2008). From decision theory to decision aiding methodology. European Journal of Operational Research 187: 138–161

Vayssiéres J., Lecomte P., Guerrin F. and Nidumolu U.-B. (2007). Modelling farmers’ action: Decision rules capture methodology and formalisation structure. A case of biomass flow operations in dairy farms of a tropical island. Animal 1: 716–733

Whang S. (1996). Coordination in operations: A taxonomy. Journal of Operations Management 12: 413–422

Zeigler, B. (2003). DEVS today: Recent advances in discrete event-based information technology. In Proc. Mascots’03, 11th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, Orlando, FL, USA, October 12–15, 2003.