Abstract
Two forms of knowledge are considered: declarative and procedural. The former is easy to extend but it is equipped with expensive deduction mechanisms, while the latter is efficiently executable but it can hardly anticipate all the special cases. In the first part of this chapter (Sections 2 and 3), we first define a syntactic representation of Soft Constraint Satisfaction Problems (SCSPs), which allows us to express dynamic programming (DP) strategies. For the e-mobility case study of ASCENS, we use Soft Constraint Logic Programming (SCLP) to program (in CIAO Prolog) and solve local optimization problems of single electric vehicles. Then we treat the global optimization problem of finding optimal parking spots for all the cars. We provide: (i) a Java orchestrator for the coordination of local SCLP optimizations; and (ii) a DP algorithm, which corresponds to a local to global propagation and back. In the second part of this chapter (Section 4) we assume that different subjects are entitled to decide. The case study concerns a smart grid model where various prosumers (producers-consumers) negotiate (in real time, according to the DEZENT approach) the cost of the exchanged energy. Then each consumer tries to plan an optimal consumption profile (computed via DP) where (s)he uses less energy when it is expensive and more energy when it is cheap, conversely for a producer. Finally, the notion of an aggregator is introduced, whose aim is to sell flexibility to the market.
This research was supported by the European project IP 257414 (ASCENS).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
European power exchange, http://www.epexspot.com
Gestore mercati elettrici, http://www.mercatoelettrico.org
ASCENS: Requirement specification and scenario description of the ascens case studies, deliverable 7.1 (2011)
Barroso, L.A., Cavalcanti, T.H., Giesbertz, P., Purchala, K.: Classification of electricity market models worldwide. In: IEEE PES, International Symposium, pp. 9–16. IEEE, Los Alamitos (2005)
Belhomme, R., Real de Asua, R.C., Valtorta, G., Paice, A., Bouffard, F., Rooth, R., Losi, A.: Address - active demand for the smart grids of the future. In: CIRED Seminar: Smart Grids for Distribution, pp. 1–4.
Belhomme, R., Sebastian, M., Diop, A., Entem, M., Bouffard, F., Valtorta, G., De Simone, A., Cerero, R., Yuen, C., Karkkainen, S., Fritz, W.: Address technical and commercial architecture, deliverable ADDRESS D1.1 (2010), http://www.addressfp7.org/
Bertelé, U., Brioschi, F.: On non-serial dynamic programming. Journal of Combinatorial Theory, Series A 14(2), 137–148 (1973)
Bistarelli, S., Montanari, U., Rossi, F.: Constraint solving over semirings. In: IJCAI, pp. 624–630 (1995)
Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based constraint satisfaction and optimization. J. ACM 44(2), 201–236 (1997)
Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based contstraint logic programming: syntax and semantics. ACM Trans. Program. Lang. Syst. 23(1), 1–29 (2001)
Bistarelli, S., Montanari, U., Rossi, F.: Soft constraint logic programming and generalized shortest path problems. J. Heuristics 8(1), 25–41 (2002)
Bistarelli, S., Montanari, U., Rossi, F.: Soft concurrent constraint programming. ACM Trans. Comput. Log. 7(3), 563–589 (2006)
Bistarelli, S., Montanari, U., Rossi, F., Santini, F.: Unicast and multicast qos routing with soft-constraint logic programming. ACM Trans. Comput. Log. 12(1), 5 (2010)
Bueno, F., Cabeza, D., Carro, M., Hermenegildo, M.V., López-García, P., Puebla, G.: The ciao prolog system. Reference manual. Tech. Rep. CLIP3/97.1, School of Computer Science, Technical University of Madrid, UPM (1997)
Buscemi, M.G., Montanari, U.: CC-pi: A constraint-based language for specifying service level agreements. In: De Nicola, R. (ed.) ESOP 2007. LNCS, vol. 4421, pp. 18–32. Springer, Heidelberg (2007)
Challet, D., Zhang, Y.C.: Emergence of cooperation and organization in an evolutionary game. Physica A: Statistical Mechanics and its Applications 246(3–4), 407–418 (1997)
Dechter, R.: Bucket elimination: A unifying framework for reasoning. Artif. Intell. 113(1-2), 41–85 (1999)
Gadducci, F., Miculan, M., Montanari, U.: About permutation algebras (pre)sheaves and named sets. Higher-Order and Symbolic Computation 19(2-3), 283–304 (2006)
Hewitt, C.: PLANNER: A language for proving theorems in robots. In: IJCAI, pp. 295–302 (1969)
Hoch, N., Monreale, V., Montanari, U., Sammartino, M.: Declarative vs procedural approach for scsp with an application to an e-mobility optimization problem. Internal Report (2014)
Hoch, N., Zemmer, K., Werther, B., Siegwart, R.: Electric vehicle travel optimization-customer satisfaction despite resource constraints. In: 2012 IEEE Intelligent Vehicles Symposium, pp. 172–177 (2012)
Hölzl, M., Gabor, T.: Reasoning and Learning for Awareness and Adaptation. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 249–290. Springer, Heidelberg (2015)
Jaffar, J., Lassez, J.: Constraint logic programming. In: POPL, pp. 111–119 (1987)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. J. Artif. Intell. Res. (JAIR) 4, 237–285 (1996)
Kohlas, J., Pouly, M.: Generic Inference: A Unifying Theory for Automated Reasoning. John Wiley, Chichester (2011)
Monreale, G.V., Montanari, U., Hoch, N.: Soft constraint logic programming for electric vehicle travel optimization. In: 26th Workshop on Logic Programming (2012)
Montanari, U., Siwe, A.T.: Real time market models and prosumer profiling. In: IEEE INFOCOM Workshops. pp. 7–12 (2013)
Montanari, U., Siwe, A.T.: Prosumers as aggregators in the dezent context of regenerative power production. In: IEEE SASO Workshops (2014)
Peters, E., Belhomme, R., Battle, C., Bouffard, F., Karkkainen, S., Six, D., Hommelberg, M.: Address: Scenarios and architecture for the active demand development in the smart grids of the future. In: CIRED 20th International Conference on Electricity Distribution, pp. 1–4 (2009)
Pitts, A.M.: Nominal Sets: Names and Symmetry in Computer Science. Cambridge Tracts in Theoretical Computer Science, vol. 57. Cambridge University Press, Cambridge (2013)
Rossi, F., van Beek, P., Walsh, T.: Handbook of Constraint Programming. Foundations of Artificial Intelligence. Elsevier, Amsterdam (2006)
Siwe, A.T.: Prosumer planning in the DEZENT context of regenerative power production. Ph.D. thesis (2013)
Sutton, R.S.: Learning to predict by the methods of temporal differences. Machine Learning 3, 9–44 (1988)
Tsang, E.P.K.: Foundations of constraint satisfaction. Computation in cognitive science. Academic Press, London (1993)
Wedde, H.F., Lehnhoff, S., Moritz, K.M., Handschin, E., Krause, O.: Distributed learning strategies for collaborative agents in adaptive decentralized power systems. In: IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS ’08), pp. 26–35 (2008)
Wedde, H.F., Lehnhoff, S., Rehtanz, C., Krause, O.: Bottom-up self-organization of unpredictable demand and supply under decentralized power management. In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO ’08), pp. 74–83 (2008)
Whitehead, D.: The el farol bar problem revisited: Reinforcement learning in a potential game. ESE Discussion Papers 186, Edinburgh School of Economics, University of Edinburgh (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Hoch, N., Monreale, G.V., Montanari, U., Sammartino, M., Siwe, A.T. (2015). From Local to Global Knowledge and Back. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds) Software Engineering for Collective Autonomic Systems. Lecture Notes in Computer Science, vol 8998. Springer, Cham. https://doi.org/10.1007/978-3-319-16310-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-16310-9_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16309-3
Online ISBN: 978-3-319-16310-9
eBook Packages: Computer ScienceComputer Science (R0)