From Local to Global Knowledge and Back

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.

[1]  Brian Campbell,et al.  Amortised Memory Analysis Using the Depth of Data Structures , 2009, ESOP.

[2]  Francesca Rossi,et al.  Constraint Solving over Semirings , 1995, IJCAI.

[3]  Ugo Montanari,et al.  Soft Constraint Logic Programming for Electric Vehicle Travel Optimization , 2012, ArXiv.

[4]  Francesca Rossi,et al.  Unicast and multicast QoS routing with soft-constraint logic programming , 2007, TOCL.

[5]  Carl Hewitt,et al.  PLANNER: A Language for Proving Theorems in Robots , 1969, IJCAI.

[6]  D. Whitehead,et al.  The El Farol Bar Problem Revisited: Reinforcement Learning in a Potential Game , 2008 .

[7]  Horst F. Wedde,et al.  Distributed Learning Strategies for Collaborative Agents in Adaptive Decentralized Power Systems , 2008, 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ecbs 2008).

[8]  Alain Tcheukam Siwe.  Prosumer planning in the DEZENT context of regenerative power production , 2013 .

[9]  Nora Koch,et al.  Software Engineering for Collective Autonomic Systems , 2015, Lecture Notes in Computer Science.

[10]  A. Paice,et al.  ADDRESS - Active Demand for the smart grids of the future , 2008 .

[11]  Yi-Cheng Zhang,et al.  Emergence of cooperation and organization in an evolutionary game , 1997 .

[12]  Umberto Bertelè,et al.  On Non-serial Dynamic Programming , 1973, J. Comb. Theory, Ser. A.

[13]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[14]  Horst F. Wedde,et al.  Bottom-Up Self-Organization of Unpredictable Demand and Supply under Decentralized Power Management , 2008, 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[15]  Fabio Gadducci,et al.  About permutation algebras, (pre)sheaves and named sets , 2006, High. Order Symb. Comput..

[16]  Manuel V. Hermenegildo,et al.  The ciao prolog system , 2002 .

[17]  Marc Pouly,et al.  Generic Inference: A Unifying Theory for Automated Reasoning , 2011 .

[18]  D. Kirsh.  Foundations of Artificial Intelligence , 1991 .

[19]  Rina Dechter,et al.  Bucket Elimination: A Unifying Framework for Reasoning , 1999, Artif. Intell..

[20]  Ugo Montanari,et al.  Prosumers as Aggregators in the DEZENT Context of Regenerative Power Production , 2014, 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems.

[21]  Joxan Jaffar,et al.  Constraint logic programming , 1987, POPL '87.

[22]  Thomas Gabor,et al.  Reasoning and Learning for Awareness and Adaptation , 2015, The ASCENS Approach.

[23]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[24]  Ugo Montanari,et al.  CC-Pi: A Constraint-Based Language for Specifying Service Level Agreements , 2007, ESOP.

[25]  Toby Walsh,et al.  Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.

[26]  Ugo Montanari,et al.  Real time market models and prosumer profiling , 2013, 2013 Proceedings IEEE INFOCOM.

[27]  Francesca Rossi,et al.  Semiring-based constraint satisfaction and optimization , 1997, JACM.

[28]  Andrew M. Pitts,et al.  Nominal Sets: Names and Symmetry in Computer Science , 2013 .

[29]  Francesca Rossi,et al.  Soft concurrent constraint programming , 2002, TOCL.

[30]  Roland Siegwart,et al.  Electric vehicle travel optimization-customer satisfaction despite resource constraints , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[31]  Francesca Rossi,et al.  Semiring-based constraint logic programming: syntax and semantics , 2001, TOPL.

[32]  K. Purchala,et al.  Classification of electricity market models worldwide , 2005, International Symposium CIGRE/IEEE PES, 2005..

[33]  Richard S. Sutton,et al.  Learning to predict by the methods of temporal differences , 1988, Machine Learning.

[34]  Francesca Rossi,et al.  Soft Constraint Logic Programming and Generalized Shortest Path Problems , 2002, J. Heuristics.