Skip to main content
Log in

MASE-BDI: agent-based simulator for environmental land change with efficient and parallel auto-tuning

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper presents an agent-based simulator for environmental land change that includes efficient and parallel auto-tuning. This simulator extends the Multi-Agent System for Environmental simulation (MASE) by introducing rationality to agents using a mentalistic approach—the Belief-Desire-Intention (BDI) model—and is thus named MASE-BDI. Because the manual tuning of simulation parameters is an error-prone, labour and computing intensive task, an auto-tuning approach with efficient multi-objective optimization algorithms is also introduced. Further, parallelization techniques are employed to speed up the auto-tuning process by deploying it in parallel systems. The MASE-BDI is compared to the MASE using the Brazilian Cerrado biome case. The MASE-BDI reduces the simulation execution times by at least 82 × and slightly improves the simulation quality. The auto-tuning algorithms, by evaluating less than 0.00115 % of a search space with 6 million parameter combinations, are able to quickly tune the simulation model, regardless of the objective used. Moreover, the experimental results show that executing the tuning in parallel leads to speedups of approximately 11 × compared to sequential execution in a hardware setting with 16-CPU cores.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Visit the MASE website: http://mase.cic.unb.br

References

  1. Ellis E (2013) Land-use and land-cover change. Retrieved from http://www.eoearth.org/view/article/154143

  2. BL Turner II, WC Clark, RW Kates, JF Richards, JT Mathews, WB Meyer (eds) (1993) The Earth as transformed by human action: global and regional changes in the biosphere over the past 300 years. Cambridge University Press. ISBN 052144 6309

  3. Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, Helkowski JH, Holloway T, Howard EA, Kucharik CJ, Monfreda C, Patz JA, Prentice CI, Ramankutty N, Snyder PK (2005) Global consequences of land use. Science 309 (5734):570–574. doi:10.1126/science.1111772. ISSN 1095-9203

    Article  Google Scholar 

  4. Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. Simulation 82(9):609–623. doi:10.1177/0037549706073695. ISSN 0037-5497

    Article  Google Scholar 

  5. Bratman ME (1987) Intention, plans, and practical reason. Harvard University Press, Cambridge. ISBN 1575861925

    Google Scholar 

  6. Rao AS, Georgeff MP (1995) Bdi agents: from theory to practice. In: Proceedings of the 1st international conference on multi-agent systems (ICMAS-95), pp 312–319

  7. Wooldridge M (2009) An introduction to multiagent systems, 2nd edn. Wiley Publishing. ISBN 978-0-470-51946-2

  8. Braubach L, Lamersdorf W, Pokahr A (2003) Jadex: implementing a BDI-Infrastructure for JADE agents. EXP 3(3):76–85

    Google Scholar 

  9. Ţăpuş C, Chung I-H, Hollingsworth JK (2002) Active harmony: towards automated performance tuning. In: Proceedings of the 2002 ACM/IEEE conference on supercomputing, SC ’02. IEEE Computer Society Press, Los Alamitos, pp 1–11

  10. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313. doi:10.1093/comjnl/7.4.308

    Article  MATH  Google Scholar 

  11. Tabatabaee V, Tiwari A, Hollingsworth J K (2005) Parallel parameter tuning for applications with performance variability. In: Proceedings of the 2005 ACM/IEEE conference on supercomputing, SC ’05. doi:10.1109/SC.2005.52. ISBN 1-59593-061-2. IEEE Computer Society, Washington, DC, p 57

  12. Eastman JR (2015) IDRISI 32.2—guide to GIS and image processing. Clark Labs, Clark University, Worcester. Retrieved from https://clarklabs.org/

    Google Scholar 

  13. Esri (2015) ArcGIS. Retrieved from http://www.esri.com/software/arcgis

  14. Soares-Filho B S, Rodrigues H O, Souza Costa W L (2009) Modeling envitonmental dynamics with dinamica ego. Belo Horizonte, Minas Gerais, Brazil. Dinamica EGO guidebook, ISBN 978-85-910119-0-2

  15. Ralha CG, Abreu CG, Coelho CGC, Zaghetto A, Macchiavello B, Machado RB (2013) A multi-agent model system for land-use change simulation. Environ Model Softw 42:30–46. doi:10.1016/j.envsoft.2012.12.003

    Article  Google Scholar 

  16. Tisue S, Wilensky U (2004) Netlogo: a simple environment for modeling complexity. In: International conference on complex systems, pp 16–21

  17. Wilensky U (1999) NetLogo. Retrieved from http://ccl.northwestern.edu/netlogo/

  18. North MJ, Collier NT, Ozik J, Tatara ER, Macal CM, Bragen M, Sydelko P (2013) Complex adaptive systems modeling with Repast Simphony. Complex Adapt Syst Model 1:3. doi:10.1186/2194-3206-1-3. ISSN 2194-3206

    Article  Google Scholar 

  19. C Le Page, F Bousquet, I Bakam, A Bah, C Baron (2000) Cormas: a multiagent simulation toolkit to model natural and social dynamics at multiple scales. In: Workshop “The ecology of scales”—Wageningen (The Netherlands)

  20. Weiss G (ed) (2013) Multiagent systems, 2nd edn. MIT Press, Cambridge. ISBN 978-0-262-01889-0

  21. Pontius RG, Boersma W, Castella J-C, Clarke K, de Nijs T, Dietzel C, Duan Z, Fotsing E, Goldstein N, Kok K, Koomen E, Lippitt CD, McConnell W, Sood AM, Pijanowski B, Pithadia S, Sweeney S, Trung TN, Veldkamp AT, Verburg PH (2008) Comparing the input, output, and validation maps for several models of land change. Ann Reg Sci 42:11–37

    Article  Google Scholar 

  22. Eldred M S, Giunta A A, Van Bloemen Waanders B G, Wojtkiewicz S F Jr, Hart W E, Alleva M P (2002) DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis - version 3.0 users manual

  23. Vuduc R, Demmel JW, Yelick K A (2005) OSKI: a library of automatically tuned sparse matrix kernels. J Phys Conf Ser 16(1):521

    Article  Google Scholar 

  24. Tiwari A, Hollingsworth JK (2011) Online adaptive code generation and tuning. In: 2011 IEEE international parallel distributed processing symposium (IPDPS). doi:10.1109/IPDPS.2011.86, pp 879–892

  25. Christen M, Schenk O, Burkhart H (2011) PATUS: a code generation and autotuning framework for parallel iterative stencil computations on modern microarchitectures. In: Proceedings of the 2011 IEEE international parallel & distributed processing symposium, IPDPS ’11. doi:10.1109/IPDPS.2011.70. ISBN 978-0-7695-4385-7. IEEE Computer Society, Washington, DC, pp 676–687

  26. Ansel J, Kamil S, Veeramachaneni K, Ragan-Kelley J, Bosboom J, O’Reilly U-M, Amarasinghe S (2014) OpenTuner: an extensible framework for program autotuning. In: Proceedings of the 23rd international conference on parallel architectures and compilation, PACT ’14. doi:10.1145/2628071.2628092. ISBN 978-1-4503-2809-8. ACM, New York, pp 303–316

  27. Munz U, Papachristodoulou A, Allgower F (2008) Delay-dependent rendezvous and flocking of large scale multi-agent systems with communication delays. In: 47th IEEE conference on decision and control, 2008. CDC 2008. doi:10.1109/CDC.2008.4739023, pp 2038–2043

  28. Lendek Z, Babuska R, De Schutter B (2009) Stability of cascaded fuzzy systems and observers. IEEE Trans Fuzzy Syst 17(3):641–653. doi:10.1109/TFUZZ.2008.924353. ISSN 1063-6706

    Article  Google Scholar 

  29. Daneshfar F, Bevrani H (2010) Load-frequency control: a ga-based multi-agent reinforcement learning. IET Gener Transm Distrib 4(1):13–26. doi:10.1049/iet-gtd.2009.0168. ISSN 1751-8687

    Article  Google Scholar 

  30. Jarraya Y, Bouaziz S, Alimi AM, Abraham A (2014) Multi-agent evolutionary design of beta fuzzy systems. In: 2014 IEEE international conference on fuzzy systems (FUZZ-IEEE). doi:10.1109/FUZZ-IEEE.2014.6891722, pp 1234–1241

  31. Yang S, Gechter F, Koukam A (2008) Application of reactive multi-agent system to vehicle collision avoidance. In: 20th IEEE international conference on tools with artificial intelligence, 2008. ICTAI ’08. doi:10.1109/ICTAI.2008.134, vol 1, pp 197–204

  32. Balaji PG, German X, Srinivasan D (2010) Urban traffic signal control using reinforcement learning agents. IET Intell Transp Syst 4(3):177–188. doi:10.1049/iet-its.2009.0096. ISSN 1751-956X

    Article  Google Scholar 

  33. Abdelhameed MM, Abdelaziz M, Hammad S, Shehata OM (2014) A hybrid fuzzy-genetic controller for a multi-agent intersection control system. In: 2014 international conference on engineering and technology (ICET), pp 1–6. doi:10.1109/ICEngTechnol.2014.7016755

  34. Klein C E, Bittencourt M, Coelho LS (2015) Wavenet using artificial bee colony applied to modeling of truck engine powertrain components. Eng Appl Artif Intell 41:41–55. doi:10.1016/j.engappai.2015.01.009. ISSN 0952-1976

    Article  Google Scholar 

  35. Oliveira GG, Pedrollo OC, Castro NMR (2015) Simplifying artificial neural network models of river basin behaviour by an automated procedure for input variable selection. Eng Appl Artif Intell 40:47–61. doi:10.1016/j.engappai.2015.01.001. ISSN 0952-1976

    Article  Google Scholar 

  36. Shahraiyni H T, Sodoudi S, Kerschbaumer A, Cubasch U (2015) A new structure identification scheme for {ANFIS} and its application for the simulation of virtual air pollution monitoring stations in urban areas. Eng Appl Artif Intell 41:175–182. doi:10.1016/j.engappai.2015.02.010. ISSN 0952-1976

    Article  Google Scholar 

  37. Bellifemine FL, Caire G, Greenwood D (2007) Developing multi-agent systems with JADE. Wiley, New York. ISBN 0470057475

    Book  Google Scholar 

  38. Braubach L, Pokahr A (2011) Jadex active components framework—BDI agents for disaster rescue coordination. In: Essaaidi M, Ganzha M, Paprzycki M (eds) Software agents, agent systems and their applications, chap. 3, vol 32. IOS Press, pp 57–84

  39. Message P Forum (1994) MPI: a message-passing interface standard. Technical report, Knoxville

  40. Jepson W (2005) A disappearing biome? Reconsidering land-cover change in the Brazilian savanna. Geogr J 171(2):99–111. doi:10.1111/j.1475-4959.2005.00153.x. ISSN 1475-4959

  41. Marquis RJ, Oliveira PS (2002) The Cerrados of Brazil: ecology and natural history of a Neotropical Savana, 1st edn. Columbia University Press, New York

    Google Scholar 

  42. Klink C A, Machado R B (2005) A conservação do Cerrado brasileiro. Megadiversidade 1(1):147–155. doi:10.1590/S0100-69912009000400001

    Google Scholar 

  43. Sano E E, Rosa R, Silva Brito J L, Ferreira L G (2008) Mapeamento semidetalhado do uso da terra do Bioma Cerrado/Semidetailed land use mapping in the Cerrado. Pesqui Agropecu Bras 43(1):153–156

    Article  Google Scholar 

  44. Killeen T J (2007) A perfect storm in the amazon wilderness: development and conservation in the context of the initiative for the Integration of the Regional Infrastructure of South America (IIRSA), vol 7. Advances in Applied Biodiversity Science (AABS). ISBN 1-934151- 07-6

Download references

Acknowledgments

This work was supported in part by the Brazilian National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Célia G. Ralha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

C. Coelho, C.G., Abreu, C.G., Ramos, R.M. et al. MASE-BDI: agent-based simulator for environmental land change with efficient and parallel auto-tuning. Appl Intell 45, 904–922 (2016). https://doi.org/10.1007/s10489-016-0797-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-016-0797-8

Keywords

Navigation