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Monitoring Agents using Declarative Planning

Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
In this paper we consider the following problem: Given a particular description of a multi-agent system (MAS), is it implemented properly? We assume that we are given (possibly incomplete) information about the system and aim at refuting its proper implementation. In our approach, agent collaboration is described as an action theory. Action sequences reaching the collaboration goal are computed by a planner, whose compliance with the actual MASbehaviour allows to detect possible collaboration failures. The approach can be fruitfully applied to aid in offline testing of a MASimplementation, as well as in online monitoring.
Wydawca
Rocznik
Strony
345--370
Opis fizyczny
Bibliogr. 29 poz.
Twórcy
autor
  • Institut für Informatik, Technische Universität Clausthal, Julius-Albert-Strasse 4, D-38678 Clausthal, Geermany
autor
  • Institut f¨ur Informationssysteme, TU Wien A-1040 Wien, Austria
autor
  • Institut f¨ur Informationssysteme, TU Wien A-1040 Wien, Austria
autor
  • Institut f¨ur Informationssysteme, TU Wien A-1040 Wien, Austria
autor
  • Department of Computer Science, University of Manchester M13 9PL, UK
Bibliografia
  • [1] Devaney, M., Ram, A.: Needles in a Haystack: Plan Recognition in Large Spatial Domains Involving Multiple Agents, Proceedings 15th National Conference on Artificial Intelligence and 10th Innovative Applications of Artificial Intelligence Conference, AAAI 98, 1998.
  • [2] Dix, J., Kuter, U., Nau, D.: HTN Planning in Answer Set Programming, Technical Report CS-TR-4332, CS Department, Univ. Maryland, 2002, Submitted to Theory and Practice of Logic Programming.
  • [3] Dix, J., Munoz-Avila, H., Nau, D., Zhang, L.: Theoretical and Empirical Aspects of a Planner in a Multi-Agent Environment, Proceedings of Journees Europeens de la Logique en Intelligence artificielle (JELIA’02) (G. Ianni, S. Flesca, Eds.), LNCS 2424, Springer, 2002.
  • [4] Eiter, T., Faber, W., Leone, N., Pfeifer, G.: Declarative Problem-Solving Using the DLV System, in: Logic-Based Artificial Intelligence (J. Minker, Ed.), Kluwer Academic Publishers, 2000, 79-103.
  • [5] Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: A Logic Programming Approach to Knowledge-State Planning, II: The DLVk System, Artificial Intelligence, 144(1-2), 2002, 157-211.
  • [6] Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: Answer Set Planning under Action Costs, Journal of Artificial Intelligence Research, 19, 2003, 25-71.
  • [7] Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity, ACM Transactions on Computational Logic, 2003, To appear.
  • [8] Erol, K., Hendler, J. A., Nau, D. S.: UMCP: A Sound and Complete Procedure for Hierarchical Task-network Planning, Proceedings Second International Conference on Artificial Intelligence Planning Systems (AIPS-94) (K. J. Hammond, Ed.), AAAI Press, June 1994.
  • [9] Fikes, R. E., Nilsson, N. J.: STRIPS: A new Approach to the Application of Theorem Proving to Problem Solving, Artificial Intelligence, 2(3-4), 1971, 189-208.
  • [10] Gelfond, M., Lifschitz, V.: Representing Action and Change by Logic Programs, Journal of Logic Programming, 17, 1993, 301-321.
  • [11] Ghallab, M., Howe, A., Knoblock, C., McDermott, D., Ram, A., Veloso, M., Weld, D., Wilkins, D.: PDDL — The Planning Domain Definition language, Technical report, Yale Center for Computational Vision and Control, October 1998, Available at http://www.cs.yale.edu/pub/mcdermott/software/pddl.tar.gz.
  • [12] Giacomo, G. D., Reiter, R., Soutchanski, M.: Execution Monitoring of High-Level Robot Programs, Proceedings Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR 98), 1998.
  • [13] Giunchiglia, E., Lifschitz, V.: An Action Language Based on Causal Explanation: Preliminary Report, Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI ’98), 1998.
  • [14] Goldman, R., Boddy, M.: Expressive Planning and Explicit Knowledge, Proceedings Third International Conference on Artificial Intelligence Planning Systems (AIPS-96), AAAI Press, 1996.
  • [15] Huber, M. J., Durfee, E. H.: On Acting Together: Without Communication, in: Spring Symposium Working Notes on Representing Mental States and Mechanisms, American Association for Artificial Intelligence, Stanford, California, 1995, 60-71.
  • [16] Intille, S. S., Bobick, A. F.: A Framework for Recognizing Multi-Agent Action from Visual Evidence, Proceedings 16th National Conference on Artificial Intelligence and 11th Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI 99), 1999.
  • [17] Jennings, N. R.: Commitments and Conventions: The Foundation of Coordination in Multi-Agent Systems, The Knowledge Engineering Review, 8(3), 1993, 223-250.
  • [18] Kaminka, G. A., Pynadath, D. V., Tambe, M.: Monitoring Deployed Agent Teams, Proceedings of the Fifth International Conference on Autonomous Agents (Agents-2001), ACM, 2001.
  • [19] Kaminka, G. A., Pynadath, D. V., Tambe, M.: Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach, Journal of Artificial Intelligence Research, 17, 2002, 83-135.
  • [20] Kaminka, G. A., Tambe, M.: Robust Agent Teams via Socially-Attentive Monitoring, Journal of Artificial Intelligence Research, 12, 2000, 105-147.
  • [21] Levesque, H. J., Reiter, R., Lesperance, Y., Lin, F., Scherl, R. B.: GOLOG: A Logic Programming Language for Dynamic Domains, Journal of Logic Programming, 31(1-3), 1997, 59-83.
  • [22] Luck, M., McBurney, P., Preist, C., Guilfoyle, C.: The AgentLink Agent Technology Roadmap Draft, 2002, AgentLink, available at http://agentlink.org/roadmap/index.html
  • [23] Myers, K.: FPEF: A Continuous Planning and Execution Framework, AI Magazine, 20(4), 1999, 63-69.
  • [24] Peot, M. A., Smith, D. E.: Conditional Nonlinear Planning, Proceedings 1st International Conference on Artificial Intelligence Planning Systems (AIPS-92, AAAI Press, 1992.
  • [25] Soutchanski, M.: Execution Monitoring of High-Level Temporal Programs, Proc. IJCAI 99 Workshop on Robot Action Planning, July 31, 1999, Stockholm, Sweden, 1999.
  • [26] Subrahmanian, V., Bonatti, P., Dix, J., Eiter, T., Kraus, S., Ozcan, F., Ross, R.: Heterogeneous Agent Systems: Theory and Implementation, MIT Press, 2000.
  • [27] Tambe, M.: Tracking dynamic team activity, Proceedings 13th National Conference on Artificial Intelligence (AAAI-96), 1996.
  • [28] Veloso, M. M., Pollack, M. E., Cox, M. T.: Rationale-Based Monitoring for Planning in Dynamic Environments, Proceedings 4th International Conference on Artificial Intelligence Planning Systems (AIPS-98), 1998.
  • [29] Wilkins, D., Lee, T., Berry, P.: Interactive Execution Monitoring of Agent Teams, Journal of Artificial Intelligence Research, 18, 2003, 217-261.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0004-0154
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