Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-13T07:19:43.771Z Has data issue: false hasContentIssue false
  • English
  • Français

A grammar-based multiagent system in dynamic design

Published online by Cambridge University Press:  14 March 2008

Grażyna Ślusarczyk
Grażyna Ślusarczyk
Affiliation:
Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Kraków, Poland
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper deals with the system of agents treated as a concurrent modular system, which is able to support the designer in solving complex design tasks. The behavior of design agents is modeled by sets of grammar rules. Each agent manages a graph grammar and a database of facts concerning the subtask for which it is responsible. The course of designing is determined by the interaction between cooperating specialized agents. The design context is expressed by the environment in which agents act and predicates describing design criteria. The organization, design methodology, and a semantic model of a grammar-based multiagent design system are presented. The notions of a valid design solution and a design solution consistent with the design criteria are also introduced. The proposed approach is illustrated by the example of designing a house estate.

Keywords

Computer-Aided DesignGraph GrammarMultiagent System
Type
Research Article
Information
AI EDAM , Volume 22 , Issue 2 , Spring 2008 , pp. 129 - 145
Copyright
Copyright © Cambridge University Press 2008

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

REFERENCES

Agarwal, M., Cagan, J., & Constantine, G.C. (1999). Influencing generative design through continuous evaluation: associating costs with the coffeemaker shape grammar. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, 253–275.CrossRefGoogle Scholar
Borkowski, A., & Grabska, E. (1995). Representing design by composition graphs. In IABSE Colloquium, Knowledge Support Systems in Civil Engineering, IABSE Reports, pp. 27–36, Zurich.Google Scholar
Cagan, J. (2001). Engineering shape grammars. In Formal Engineering Design Synthesis (Antonsson, E.K., & Cagan, J., Eds.). Cambridge: Cambridge University Press.Google Scholar
Cagdas, G. (1996). A shape grammar: the language of traditional Turkish houses. Environment and Planning B 23, 443–464.CrossRefGoogle Scholar
Campbell, M., Cagan, J., & Kotovsky, K. (1998). A-design: theory and implementation of an adaptive agent-based method of conceptual design. In Artificial Intelligence in Design'98 (Gero, J.S., & Sudweeks, F., Eds.), pp. 579–598. Dordrecht: Kluwer.Google Scholar
Campbell, M., Cagan, J., & Kotovsky, K. (1999). A-design: an agent-based approach to conceptual design in a dynamic environment. Research in Engineering Design 11, 172–192.CrossRefGoogle Scholar
Canamero, D. (1997). Modeling motivations and emotions as a basis for intelligent behavior. Proc. 1st Int. Conf. Autonomous Agents, pp. 148–155, Marina del Rey, CA.CrossRefGoogle Scholar
Carlson, C., Woodbury, R., & McKelvey, R. (1993). An introduction to structure and structure grammars. Environment and Planning B 18, 417–426.Google Scholar
Drewes, F., Hoffmann, D., & Plump, D. (2000). Hierarchical graph transformation. Proc. FOSSACS 2000, Lecture Notes in Computer Science, Vol. 1784, pp. 98–113.Google Scholar
Dzeroski, S. (2002). Relational reinforcement learning for agents in worlds with objects. Proc. Symp. Adaptive Agents and Multi-Agent Systems (AISB'02), pp. 1–8.Google Scholar
Fagin, R., Halpern, J.Y., Moses, Y., & Vardi, M.Y. (1995). Reasoning About Knowledge. Cambridge, MA: MIT Press.Google Scholar
Gaborit, P., Potet, A., & Sayettat, C. (1990). Semantics and validation procedures of a multi-modal logic for formalization of multi-agent universes. Proc. 9th European Conf. Artificial Intelligence, pp. 289–291. Stockholm: Pitman.Google Scholar
Gero, J.S., & Kannengiesser, U. (2002). The situated function–behaviour–structure framework. In Artificial Intelligence in Design'02 (Gero, J.S., Ed.), pp. 89–104. Dordrecht: Kluwer.Google Scholar
Gero, J.S., & Kannengiesser, U. (2003). Towards a framework for agent-based product modelling. Int. Conf. Engineering Design, ICED'03, pp. 1621–1622, Stockholm.Google Scholar
Grabska, E., Ślusarczyk, G., & Papiernik, K. (2003). Interpretation of objects represented by hierarchical graphs. Proc. Conf. Computer Recognition Systems, KOSYR'03, pp. 287–293, Wrocław, Poland.Google Scholar
Grabska, E., Ślusarczyk, G., & Grześ, P. (2005). Dynamic design with the use of intelligent agents. Proc. 4th Int. Conf. Computer Recognition Systems, CORES'05, pp. 827–834. Berlin: Springer.CrossRefGoogle Scholar
Grabska, E., Grzesiak-Kopeć, K., Lembas, J., Łachwa, A., & Ślusarczyk, G. (2006 a). Hypergraphs in diagrammatic design. In Computer Vision and Graphics (Wojciechowski, K., et al. , Eds.), pp.111–117. Berlin: Springer.CrossRefGoogle Scholar
Grabska, E., Grzesiak-Kopeć, K., & Ślusarczyk, G. (2006 b). Designing floor-layouts with the assistance of curious agents. In ICCS 2006 Part III, Lecture Notes in Computer Science (Alexandrov, V.A., et al. , Eds.), Vol. 3993, pp. 883–886. Berlin: Springer.CrossRefGoogle Scholar
Grabska, E., Grzesiak-Kopeć, K., & Ślusarczyk, G. (2006 c). Visual creative design with the assistance of curious agents. In Proc. 4th Int. Conf. Diagrammatic Representation on Inference, Diagrams 2006, Lecture Notes in Computer Science (Baker-Plummer, D., et al. , Eds.), Vol. 4045, pp. 218–220. Stanford, CA: Springer.Google Scholar
Grecu, D.L., & Brown, D.C (2000). Expectation formation in multi-agent design systems. In Artificial Intelligence in Design'00 (Gero, J.S., Ed.), pp. 651–671. Dordrecht: Kluwer.Google Scholar
Habel, A., & Kreowski, H.J. (1987). Some structural aspects of hypergraph languages generated by hyperedge replacement. In Lecture Notes in Computer Science, Vol. 247, pp. 207–219. Berlin: Springer–Verlag.Google Scholar
Kokoszka, A., Bielecki, A., & Holas, P. (2001). Mental organization according to metabolism of information and its mathematical description. International Journal of Neuroscience 107, 173–184.CrossRefGoogle ScholarPubMed
Lander, S.E. (1997). Issues in multiagent design systems. IEEE Expert 12, 18–26.CrossRefGoogle Scholar
Longenecker, S.N., & Fitzhorn, P.A. (1991). A shape grammar for non-manifold modeling. Research in Engineering Design 3, 159–170.CrossRefGoogle Scholar
McCormack, J.P., & Cagan, J. (2002). Designing inner hood panels through a shape grammar based framework. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, 273–290.CrossRefGoogle Scholar
Myers, L., & Pohl, J. (1994). ICDM: integrated cooperative decision making-in practice. Tools With Artificial Intelligence. Proc. 6th Int. Conf., pp. 608–614.CrossRefGoogle Scholar
Paun, Gh., & Salomaa, A. (Eds.). (1999). Grammatical Models of Multi-Agent Systems. Amsterdam: Gordon & Breach.Google Scholar
Rosenman, M., & Gero, J.S (1999). Evolving designs by generating useful complex gene structures. In Evolutionary Design by Computers (Bentley, P.J., Ed.), pp. 345–364. San Francisco, CA: Morgan Kaufmann.Google Scholar
Saunders, R. (2001). Curious design agents and artificial creativity. PhD Thesis. University of Sydney.Google Scholar
Ślusarczyk, G. (2003). Hierarchical hypergraph transformations in engineering design. Journal of Applied Computer Science 11, 67–82.Google Scholar
Ślusarczyk, G. (2004). Heuristic methods and hierarchical graph grammars in design. Visual and Spatial Reasoning in Design III, pp. 45–66, University of Sydney, Key Centre of Design Computing and Cognition.Google Scholar
Soman, A., & Campbell, M. (2002). A grammar-based approach to sheet metal design. Proc. DETC'02 ASME Design Engineering Technical Conf., pp. 1–9, Montreal.CrossRefGoogle Scholar
Sosa, R., & Gero, J.S. (2004). A computational framework for the study of creativity and innovation in design: effects of social ties. In Design Computing and Cognition'04 (Gero, J.S., Ed.), pp. 499–517. Dordrecht: Kluwer.CrossRefGoogle Scholar
Stiny, G., & Mitchell, W.J. (1980). The grammar of paradise: on the generation of Mughul gardens. Environment and Planning B 7, 209–226.CrossRefGoogle Scholar
Suh, N.P. (1990). The Principles of Design. New York: Oxford University Press.Google Scholar
Traverso, P., & Spalazzi, L. (1995). A logic for acting, sensing and planning. Proc. 14th Int. Joint Conf. Artificial Intelligence, pp. 1941–1949, Montreal.Google Scholar
Wooldridge, M.J (1995). This is MYWORLD: the logic of an agent oriented DAI testbed. Intelligent Agents: Proc. ECAI'94 Workshop on Agent Theories, Architectures and Languages, Lecture Notes in Artificial Intelligence (Wooldridge, M., & Jennings, N.R., Eds.), Vol. 890, pp. 160–178. Amsterdam: Springer–Verlag.CrossRefGoogle Scholar
Wooldridge, M.J. (1999). Intelligent agents. In Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence (Weiss, G., Ed.), pp. 27–77. Cambridge, MA: MIT Press.Google Scholar
Wooldridge, M.J., & Lomuscio, A. (2000). Multi-agent VSK logic. Proc. 7th European Workshop on Logics in Artificial Intelligence (JELIAI-2000). Berlin: Springer–Verlag.Google Scholar
Velasquez, J.D., & Maes, P. (1997). Cathexis: a computation model of emotions. Proc. 1st Int. Conf. Autonomous Agents, pp. 518–519, Marina del Rey, CA.CrossRefGoogle Scholar