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Distributed Ledger Technology and Normative Multi-Agent for Smart Energy Management

Advisors – Olivier Boissier, Philippe Calvez (ENGIE R&D)
Contact – send application to,
Location – ENGIE CRIGEN 361, avenue du Président Wilson BP 33 9321 Saint-Denis La Plaine Cedex
Team – Connected Intelligence & Computer science and Intelligent Systems Dpt
Keywords: Multi-Agent Systems, Distributed Ledger Technology, Smart Energy Management


The global context of this research concerns the transformations of the global energy market due to the evolution of digital technologies. This global market is becoming a decentralized eco-system of several local and agile energy markets where prosumers trade the energy they produce as well as the one they consume.
In this context, Multi-Agent System (MAS) technologies are proposing promising directions [12, 14]: energy eco-system simulation [15], energy allocation optimisation [1, 4], energy trading management on behalf of human users (individuals or energy stakeholders), virtual plant formation[13], etc.
Distributed Ledger System (DLS) is another promising direction of research in such a context. Issued from the extensions and generalizations of the blockchain technology (BT) based on Bitcoin [9], the current developments show that their applications are going beyond cryptocurrencies to address the management of smart contracts (e.g. [11, 8] or Scanergy Project1).
While MAS technologies are targeted to the definition of autonomous agents and to their decentralized coordination thanks to coordination protocols, normative agent organizations, etc, the technologies supporting DLS [18] are complementary. They are used to verify and store any transactions [16] without relying on any central authority in control of the transactions. They share and make available to all nodes participating to the system, the information about every transaction ever completed. In such a context, smart contracts express agreements between two or more participants with contract terms corresponding to user-defined program executed in the DLS decentralized environment [10, 6] (e.g. ETHEREUM 2 supporting a Smart Contract Language 3). Smart contracts can be used to reach agreements, to solve common problems. They can be enforced as part of transactions and are executed across the blockchain network by all connected nodes. As shown in [17] for business process monitoring, smart contracts open new opportunities such as the support of Decentralized Autonomous Organizations (DAO)[7].
The objective of this research is to investigate how DLS Technology can be used to address the monitoring and enforcement of normative and coordination processes within agent organizations in open multi-agent system. The research questions to study are related to how these processes could be mapped onto a distributed ledger infrastructure. More precisely, we are interested in analyzing the interests of (i) integrating the automatic and immutable chain of transaction in the process monitoring facilities supporting the agent organizations, (ii) using smart contracts as control support of the collaborative and normative process in the context of open MAS, (iii) introducing mechanisms to ensure autonomy of the agents while also ensuring enforcement of their behaviour. These three questions have to be conducted considering the fact that at some point, several processes, organizations could be governing the agents. It is thus of first importance to address the management and coordination of multiple interdependent DLS at the same time. Interoperability (e.g. [2]) and scalability (e.g. [5]) are important features to consider.
The feasibility of the proposed models will be evaluated by prototyping a simplified smart energy management use case on top of the developed infrastructure.

Expected results


Expected theoretical results consist in state of the art analysis and model proposal for:

  • Integration of the automatic and immutable transaction history in the organization monitoring facilities of a normative multi-agent system
  • Using smart contracts as support of the collaborative and normative process control in the context of open agent organisations,
  • Introduction of mechanisms in the DLS infrastructure to ensure autonomy of the agents while also ensuring enforcement and trust.

These three analysis have to be conducted considering the management and coordination of multiple interdependent and interoperable chains/organisations at the same time.


  • Proof of concept of the proposed model developed in the context of the JaCaMo platform [3]
  • Application and feasibility evaluation on smart energy management use case


[1] C. Akasiadis and G. Chalkiadakis. Decentralized large-scale electricity consumption shifting by prosumer cooperatives. In G. A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, and F. van Harmelen, editors, ECAI 2016 – 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands – Including Prestigious Applications of Artificial Intelligence (PAIS 2016), volume 285 of Frontiers in Artificial Intelligence and Applications, pages 175-183. IOS Press, 2016.
[2] A. Back, M. Corallo, L. Dashjr, M. Friedenbach, G. Maxwell, A. Miller, A. Poelstra, J. Timon, and P. Wuille. Enabling blockchain innovations with pegged sidechains. (online), 2014.
[3] O. Boissier, R. H. Bordini, J. F. Hubner, A. Ricci, and A. Santi. Multi-agent oriented programming with jacamo. Science of Computer Programming, 78(6):747-761, 2013.
[4] J. Cerquides, G. Picard, and J. Rodriguez-Aguilar. Designing a marketplace for the trading and distribution of energy in the smart grid. In 14th International Confer- ence on Autonomous Agents and Multiagent Systems (AAMAS), pages 1285-1293. International Foundation for Autonomous Agents and Multiagent Systems, 2015.
[5] K. Croman, C. Decker, I. Eyal, A. E. Gencer, A. Juels, A. Kosba, A. Miller, P. Saxena, E. Shi, and E. Gün. On scaling decentralized blockchains. In Proc. 3rd Workshop on Bitcoin and Blockchain Research, 2016.
[6] F. Idelberger, G. Governatori, R. Riveret, and G. Sartor. Evaluation of logic-based smart contracts for blockchain systems. In International Symposium on Rules and Rule Markup Languages for the Semantic Web, pages 167-183. Springer, 2016.
[7] A. Levine. Application specific, autonomous, self-bootstrapping consensus platforms. In Bitsharestalk forum, January, volume 1, 2014.
[8] H. Massy-Beresford. Virtual currency for prosumers could save eu power grids.
[9] S. Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2008.
[10] S. Omohundro. Cryptocurrencies, smart contracts, and artificial intelligence. AI Matters, 1(2):19-21, Dec. 2014.
[11] G. Prisco. An energy blockchain for european prosumers, 2016. 3
[12] S. D. Ramchurn, P. Vytelingum, A. Rogers, and N. R. Jennings. Putting the’smarts’ into the smart grid: a grand challenge for artificial intelligence. Communications of the ACM, 55(4):86-97, 2012.
[13] V. Robu, R. Kota, G. Chalkiadakis, A. Rogers, and N. R. Jennings. Cooperative virtual power plant formation using scoring rules. In Proceedings of the 11th Inter- national Conference on Autonomous Agents and Multiagent Systems-Volume 3, pages 1165-1166. International Foundation for Autonomous Agents and Multiagent Systems, 2012.
[14] A. Rogers, S. D. Ramchurn, and N. R. Jennings. Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research. In J. Hoffmann and B. Selman, editors, Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22-26, 2012, Toronto, Ontario, Canada. AAAI Press, 2012.
[15] F. Silva, B. Teixeira, T. Pinto, G. Santos, Z. Vale, and I. Praça. Generation of realistic scenarios for multi-agent simulation of electricity markets. Energy, 116:128-139, 2016.
[16] F. Tschorsch and B. Scheuermann. Bitcoin and beyond: A technical survey on decentralized digital currencies. 2015.
[17] I. Weber, X. Xu, R. Riveret, G. Governatori, A. Ponomarev, and J. Mendling. Untrusted business process monitoring and execution using blockchain. In International Conference on Business Process Management, pages 329-347. Springer, 2016.
[18] J. Yli-Huumo, D. Ko, S. Choi, S. Park, and K. Smolander. Where is current research on blockchain technology? a systematic review. PloS one, 11(10):e0163477, 2016.

Distributed Ledgers; Multi-agent Systems; Smart Energy Management
Institut Mines Télécom (IMT) Saint-Etienne
42023 Saint-Étienne  
Équipe de recherche
Connected Intelligence
Site Web
Langues obligatoires
Anglais; Français
Bac +5
Informations de contact

Olivier Boissier,
Philippe Calvez,