Skip to main content
SpringerLink
Log in

Augmenting Agent Computational Environments with Quantitative Reasoning Modules and Customizable Bridge Rules

  • Conference paper
  • First Online:
Autonomous Agents and Multiagent Systems (AAMAS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10003))

Included in the following conference series:

Abstract

There are many examples where large amount of data might be potentially accessible to an agent, but the agent is constrained by the available budget since access to knowledge bases is subject to fees. There are also several activities that an agent might perform on the web where one or more stages imply the payment of fees: for instance, buying resources in a cloud computing context where the objective of the agent is to obtain the best possible configuration of a certain application withing given budget constraints. In this paper we consider the software-engineering problem of how to practically empower agents with the capability to perform such kind of reasoning in a uniform and principled way. To this aim, we enhance the ACE component-based agent architecture by means of a device for practical and computationally affordable quantitative reasoning, whose results actually determine one or more courses of agent’s actions, also according to policies/preferences.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Raspberry, the grounder gringo (v.3.0.5), and the solver clasp (v.3.1.3) are used as follows: .

References

  1. Alviano, M., Dodaro, C., Faber, W., Leone, N., Ricca, F.: WASP: a native ASP solver based on constraint learning. In: Cabalar, P., Son, T.C. (eds.) LPNMR 2013. LNCS (LNAI), vol. 8148, pp. 54–66. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40564-8_6

    Chapter  Google Scholar 

  2. Bienvenu, M., Lang, J., Wilson, N.: From preference logics to preference languages, and back. In: Proceedings of KR 2010, pp. 414–424 (2010)

    Google Scholar 

  3. Bordini, R.H., Braubach, L., Dastani, M., Fallah-Seghrouchni, A.E., Gómez-Sanz, J.J., Leite, J., O’Hare, G.M.P., Pokahr, A., Ricci, A.: A survey of programming languages and platforms for multi-agent systems. Informatica (Slovenia) 30(1), 33–44 (2006)

    MATH  Google Scholar 

  4. Bordini, R.H., Hübner, J.F.: BDI agent programming in AgentSpeak using Jason. In: Toni, F., Torroni, P. (eds.) CLIMA 2005. LNCS (LNAI), vol. 3900, pp. 143–164. Springer, Heidelberg (2006). doi:10.1007/11750734_9

    Chapter  Google Scholar 

  5. Bracciali, A., Demetriou, N., Endriss, U., Kakas, A., Lu, W., Mancarella, P., Sadri, F., Stathis, K., Terreni, G., Toni, F.: The KGP model of agency for global computing: computational model and prototype implementation. In: Priami, C., Quaglia, P. (eds.) GC 2004. LNCS (LNAI), vol. 3267, pp. 340–367. Springer, Heidelberg (2005). doi:10.1007/978-3-540-31794-4_18

    Chapter  Google Scholar 

  6. Brewka, G., Delgrande, J.P., Romero, J., Schaub, T.: asprin: Customizing answer set preferences without a headache. In: Bonet, B., Koenig, S. (eds.) Proceedings of AAAI 2015, pp. 1467–1474. AAAI Press (2015)

    Google Scholar 

  7. Brewka, G., Eiter, T.: Equilibria in heterogeneous nonmonotonic multi-context systems. In: Proceedings of AAAI 2007, pp. 385–390. AAAI Press (2007)

    Google Scholar 

  8. Brewka, G., Eiter, T., Fink, M.: Nonmonotonic multi-context systems: a flexible approach for integrating heterogeneous knowledge sources. In: Balduccini, M., Son, T.C. (eds.) Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning. LNCS (LNAI), vol. 6565, pp. 233–258. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20832-4_16

    Chapter  Google Scholar 

  9. Brewka, G., Eiter, T., Fink, M., Weinzierl, A.: Managed multi-context systems. In: Walsh, T. (ed.) Proceedings of IJCAI 2011, pp. 786–791. IJCAI/AAAI (2011)

    Google Scholar 

  10. Brewka, G., Ellmauthaler, S., Pührer, J.: Multi-context systems for reactive reasoning in dynamic environments. In: Schaub, T. (ed.) Proceedings of ECAI 2014. IJCAI/AAAI (2014)

    Google Scholar 

  11. Brewka, G., Niemelä, I., Truszczyński, M.: Preferences and nonmonotonic reasoning. AI Mag. 29(4), 69–78 (2008)

    Google Scholar 

  12. Brik, A.: Extensions of answer set programming. Ph.D. thesis, University of California, San Diego (2012)

    Google Scholar 

  13. Costantini, S.: ACE: a flexible environment for complex event processing in logical agents. In: Baldoni, M., Baresi, L., Dastani, M. (eds.) EMAS 2015. LNCS (LNAI), vol. 9318, pp. 70–91. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26184-3_5

    Chapter  Google Scholar 

  14. Costantini, S.: Knowledge acquisition via non-monotonic reasoning in distributed heterogeneous environments. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS (LNAI), vol. 9345, pp. 228–241. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23264-5_20

    Chapter  Google Scholar 

  15. Costantini, S., De Gasperis, G.: Exchanging data and ontological definitions in multi-agent-contexts systems. In: Paschke, A., Fodor, P., Giurca, A., Kliegr, T. (eds.) Proceedings of RuleML 2015 Challenge. CEUR Workshop Proceedings (2015). CEUR-WS.org

    Google Scholar 

  16. Costantini, S., Formisano, A.: Modeling preferences and conditional preferences on resource consumption and production in ASP. J. Algorithms Cogn. Inf. Logic 64(1), 3–15 (2009)

    MathSciNet  MATH  Google Scholar 

  17. Costantini, S., Formisano, A.: Answer set programming with resources. J. Logic Comput. 20(2), 533–571 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  18. Costantini, S., Formisano, A.: RASP and ASP as a fragment of linear logic. Journal of Applied Non-Classical Logics 23(1–2), 49–74 (2013)

    Article  MathSciNet  Google Scholar 

  19. Costantini, S., Formisano, A., Petturiti, D.: Extending and implementing RASP. Fundam. Inform. 105(1–2), 1–33 (2010)

    MathSciNet  MATH  Google Scholar 

  20. Costantini, S., Tocchio, A.: A logic programming language for multi-agent systems. In: Flesca, S., Greco, S., Ianni, G., Leone, N. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 1–13. Springer, Heidelberg (2002). doi:10.1007/3-540-45757-7_1

    Chapter  Google Scholar 

  21. Costantini, S., Tocchio, A.: The DALI logic programming agent-oriented language. In: Alferes, J.J., Leite, J. (eds.) JELIA 2004. LNCS (LNAI), vol. 3229, pp. 685–688. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30227-8_57

    Chapter  Google Scholar 

  22. Dal Palù, A., Dovier, A., Pontelli, E., Rossi, G.: GASP: answer set programming with lazy grounding. Fundam. Inform. 96(3), 297–322 (2009)

    MathSciNet  MATH  Google Scholar 

  23. Dao-Tran, M., Eiter, T., Fink, M., Krennwallner, T.: Distributed evaluation of nonmonotonic multi-context systems. JAIR 52, 543–600 (2015)

    MathSciNet  MATH  Google Scholar 

  24. Dastani, M., van Riemsdijk, M.B., Meyer, J.C.: Programming multi-agent systems in 3APL. In: Bordini, R.H., Dastani, M., Dix, J., Fallah-Seghrouchni, A.E. (eds.) Multi-Agent Programming. Multiagent Systems, Artificial Societies, and Simulated Organizations, vol. 15, pp. 39–67. Springer, New York (2005)

    Chapter  Google Scholar 

  25. Delgrande, J., Schaub, T., Tompits, H., Wang, K.: A classification and survey of preference handling approaches in nonmonotonic reasoning. Comput. Intell. 20(12), 308–334 (2004)

    Article  MathSciNet  Google Scholar 

  26. Dovier, A., Formisano, A., Pontelli, E., Vella, F.: A GPU implementation of the ASP computation. In: Gavanelli, M., Reppy, J. (eds.) PADL 2016. LNCS, vol. 9585, pp. 30–47. Springer, Heidelberg (2016). doi:10.1007/978-3-319-28228-2_3

    Chapter  Google Scholar 

  27. Fisher, M.: MetateM: the story so far. In: Bordini, R.H., Dastani, M.M., Dix, J., Fallah Seghrouchni, A. (eds.) ProMAS 2005. LNCS (LNAI), vol. 3862, pp. 3–22. Springer, Heidelberg (2006). doi:10.1007/11678823_1

    Chapter  Google Scholar 

  28. Gebser, M., Kaminski, R., Kaufmann, B., Romero, J., Schaub, T.: Progress in clasp series 3. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS (LNAI), vol. 9345, pp. 368–383. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23264-5_31

    Chapter  Google Scholar 

  29. Gelfond, M.: Answer sets. In: Handbook of Knowledge Representation. Elsevier, 2007

    Google Scholar 

  30. Hindriks, K.V., Hoek, W., Meyer, J.-J.C.: GOAL agents instantiate intention logic. In: Artikis, A., Craven, R., Kesim Çiçekli, N., Sadighi, B., Stathis, K. (eds.) Logic Programs, Norms and Action. LNCS (LNAI), vol. 7360, pp. 196–219. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29414-3_11

    Chapter  Google Scholar 

  31. Liu, G., Janhunen, T., Niemelä, I.: Answer set programming via mixed integer programming. In: Proceedings of KR 2012 (2012)

    Google Scholar 

  32. Maratea, M., Pulina, L., Ricca, F.: A multi-engine approach to answer-set programming. TPLP 14(6), 841–868 (2014)

    MathSciNet  Google Scholar 

  33. Naumov, P., Tao, J.: Budget-constrained knowledge in multiagent systems. In: Weiss, G., Yolum, P., Bordini, R.H., Elkind, E. (eds.) Proceedings of AAMAS 2015, pp. 219–226. ACM (2015)

    Google Scholar 

  34. Simons, P., Niemelä, I., Soininen, T.: Extending and implementing the stable model semantics. Artif. Intell. 138(1–2), 181–234 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  35. Web-references. Some ASP solvers. Clasp: www.potassco.sourceforge.net; Cmodels: www.cs.utexas.edu/users/tag/cmodels; DLV: www.dlvsystem.com; Smodels: www.tcs.hut.fi/Software/smodels

Download references

Author information

Authors and Affiliations

  1. DISIM, Università di L’Aquila, L’Aquila, Italy

    Stefania Costantini

  2. DMI, Università di Perugia, GNCS-INdAM, Perugia, Italy

    Andrea Formisano

Authors
  1. Stefania Costantini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Formisano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Formisano .

Editor information

Editors and Affiliations

  1. Campus de la UAB, IIIA-CSIC, Bellaterra, Spain

    Nardine Osman

  2. Campus de la UAB, IIIA-CSIC, Cerdanyola, Spain

    Carles Sierra

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Costantini, S., Formisano, A. (2016). Augmenting Agent Computational Environments with Quantitative Reasoning Modules and Customizable Bridge Rules. In: Osman, N., Sierra, C. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2016. Lecture Notes in Computer Science(), vol 10003. Springer, Cham. https://doi.org/10.1007/978-3-319-46840-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46840-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46839-6

  • Online ISBN: 978-3-319-46840-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation