iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s11227-014-1307-6
Multicore and FPGA implementations of emotional-based agent architectures | The Journal of Supercomputing Skip to main content
Springer Nature Link
Log in

Multicore and FPGA implementations of emotional-based agent architectures

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Control architectures based on Emotions are becoming promising solutions for the implementation of future robotic agents. The basic controllers of the architecture are the emotional processes that decide which behaviors of the robot must activate to fulfill the objectives. The number of emotional processes increases (hundreds of millions/s) with the complexity level of the application, reducing the processing capacity of the main processor to solve complex problems (millions of decisions in a given instant). However, the potential parallelism of the emotional processes permits their execution in parallel on FPGAs or Multicores, thus enabling slack computing in the main processor to tackle more complex dynamic problems. In this paper, an emotional architecture for mobile robotic agents is presented. The workload of the emotional processes is evaluated. Then, the main processor is extended with FPGA co-processors through Ethernet link. The FPGAs will be in charge of the execution of the emotional processes in parallel. Different Stratix FPGAs are compared to analyze their suitability to cope with the proposed mobile robotic agent applications. The applications are set up taking into account different environmental conditions, robot dynamics and emotional states. Moreover, the applications are run also on Multicore processors to compare their performance in relation to the FPGAs. Experimental results show that Stratix IV FPGA increases the performance in about one order of magnitude over the main processor and solves all the considered problems. Quad-Core increases the performance in 3.64 times, allowing to tackle about 89 % of the considered problems. Quad-Core has a lower cost than a Stratix IV, so more adequate solution but not for the most complex application. Stratix III could be applied to solve problems with around the double of the requirements that the main processor could support. Finally, a Dual-Core provides slightly better performance than stratix III and it is relatively cheaper.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

References

  1. Malfaz M, Salichs MA (2010) Using MUDs as an experimental platform for testing a decision making system for self-motivated autonomous agents. Artif Intell Simul Behav J 2(1):21–44

    Google Scholar 

  2. Damiano L, Cañamero L (2010) Constructing emotions. Epistemological groundings and applications in robotics for a synthetic approach to emotions. In: Proceedings of international symposium on aI-inspired biology, The Society for the Study of Artificial Intelligence, pp 20–28

  3. Hawes N, Wyatt J, Sloman A (2009) Exploring design space for an integrated intelligent system. Knowl Based Syst 22(7):509–515

    Article  Google Scholar 

  4. Sloman A (2009) Some requirements for human-like robots: why the recent over-emphasis on embodiment has held up progress. Creat Brain Like Intell 2009:248–277

    Article  Google Scholar 

  5. Arkin RC, Ulam P, Wagner AR (2012) Moral decision-making in autonomous systems: enforcement, moral emotions, dignity, trust and deception. In: Proceedings of the IEEE, Mar 2012, vol 100, no 3, pp 571–589

  6. iRobot industrial robots website. http://www.irobot.com/gi/ground/. Accessed 22 Sept 2014

  7. Moravec H (2009) Rise of the robots: the future of artificial intelligence. Scientific American, March 2009. http://www.scientificamerican.com/article/rise-of-the-robots/. Accessed 14 Oct 2014.

  8. Thu Bui L, Abbass HA, Barlow M, Bender A (2012) Robustness against the decision-maker’s attitude to risk in problems with conflicting objectives. IEEE Trans Evolut Comput 16(1):1–19

    Article  Google Scholar 

  9. Pedrycz W, Song M (2011) Analytic hierarchy process (AHP) in group decision making and its optimization with an allocation of information granularity. IEEE Trans Fuzzy Syst 19(3):527–539

    Article  Google Scholar 

  10. Lee-Johnson CP, Carnegie DA (2010) Mobile robot navigation modulated by artificial emotions. IEEE Trans Syst Man Cybern Part B 40(2):469–480

    Article  Google Scholar 

  11. Daglarli E, Temeltas H, Yesiloglu M (2009) Behavioral task processing for cognitive robots using artificial emotions. Neurocomputing 72(13):2835–2844

    Article  Google Scholar 

  12. Ventura R, Pinto-Ferreira C (2009) Responding efficiently to relevant stimuli using an emotion-based agent architecture. Neurocomputing 72(13):2923–2930

    Article  Google Scholar 

  13. Arkin RC, Ulam P, Wagner AR (2012) Moral decision-making in autonomous systems: enforcement, moral emotions, dignity, trust and deception. Proc IEEE 100(3):571–589

    Article  Google Scholar 

  14. Salichs MA, Malfaz M (2012) A new approach to modeling emotions and their use on a decision-making system for artificial agents. Affect Comput IEEE Trans 3(1):56–68

    Article  Google Scholar 

  15. Altera Corporation (2011) Stratix III device handbook, vol 1–2, version 2.2. http://www.altera.com/literature/lit-stx3.jsp. Accessed 14 Oct 2014.

  16. Altera Corporation (2014) Stratix IV device handbook, vol 1–4, version 5.9. http://www.altera.com/literature/lit-stratix-iv.jsp. Accessed 14 Oct 2014.

  17. Naouar MW, Monmasson E, Naassani AA, Slama-Belkhodja I, Patin N (2007) FPGA-based current controllers for AC machine drives: a review. IEEE Trans Ind Electr 54(4):1907–1925

    Article  Google Scholar 

  18. Intel Corporation (2014) Desktop 4th generation Intel Core Processor Family, Desktop Intel Pentium Processor Family, and Desktop Intel Celeron Processor Family, Datasheet, vol 1, 2

  19. March JL, Sahuquillo J, Hassan H, Petit S, Duato J (2011) A new energy-aware dynamic task set partitioning algorithm for soft and hard embedded real-time systems. Comput J 54(8):1282–1294

    Article  Google Scholar 

  20. Del Campo I, Basterretxea K, Echanobe J, Bosque G, Doctor F (2012) A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments. IEEE Trans Syst Man Cybern Part B 42(2):501–512

    Article  Google Scholar 

  21. Pedraza C, Castillo J, Martínez JI, Huerta P, Bosque JL, Cano J (2011) Genetic algorithm for Boolean minimization in an FPGA cluster. J Supercomput 58(2):244–252

    Article  Google Scholar 

  22. Orlowska-Kowalska T, Kaminski M (2011) FPGA implementation of the multilayer neural network for the speed estimation of the two-mass drive system. IEEE Trans Ind Inf 7(3):436–445

    Article  Google Scholar 

  23. Cassidy AS, Merolla P, Arthur JV, Esser SK, Jackson B, Alvarez-icaza R, Datta P, Sawada J, Wong TM, Feldman V, Amir A, Ben-dayan D, Mcquinn E, Risk WP, Modha DS (2013) Cognitive computing building block: a versatile and efficient digital neuron model for neurosynaptic cores. In: Proceedings of international joint conference on neural networks, IEEE (IJCNN’2013)

  24. IBM Cognitive Computing and Neurosynaptic chips website. http://www.research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml. Accessed 22 Sept 2014

  25. Seo E, Jeong J, Park S, Lee J (2008) Energy efficient scheduling of real-time tasks on multicore processors. IEEE Trans Parallel Distrib Syst 19(11):1540–1552

    Article  Google Scholar 

  26. Lehoczky J, Sha L, Ding Y (1989) The rate monotonic scheduling algorithm: exact characterization and average case behavior. In: Proceedings of real time systems symposium, IEEE 1989, pp 166–171

  27. Ng-Thow-Hing V, Lim J, Wormer J, Sarvadevabhatla RK, Rocha C, Fujimura K, Sakagami Y (2008) The memory game: creating a human-robot interactive scenario for ASIMO. In: Proceedings of intelligent robots and systems, 2008, IROS 2008, IEEE/RSJ international conference, pp 779–786

Download references

Acknowledgments

This work was supported in part under Spanish Grant PAID/2012/325 of “Programa de Apoyo a la Investigación y Desarrollo. Proyectos multidisciplinares”, Universitat Politécnica de Valencia, Spain.

Author information

Authors and Affiliations

  1. Department of Computer Engineering (DISCA), Universitat Politecnica de Valencia (UPV), Valencia, Spain

    Carlos Domínguez, Houcine Hassan, Alfons Crespo & José Albaladejo

Authors
  1. Carlos Domínguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Houcine Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alfons Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Albaladejo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Houcine Hassan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Domínguez, C., Hassan, H., Crespo, A. et al. Multicore and FPGA implementations of emotional-based agent architectures. J Supercomput 71, 479–507 (2015). https://doi.org/10.1007/s11227-014-1307-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-014-1307-6

Keywords

Search

Navigation