Reputation Management in Multi-Agent Systems Using Permissioned Blockchain Technology

The multi-agent framework is a well-known approach to realize distributed intelligent systems. Multi-agent systems (MAS) are increasingly employed in safety-and information-critical domains (e.g., eHealth, cyber-physical systems, financial services, and energy market). Therefore, these systems need to be equipped with mechanisms to ensure transparency and the trustworthiness of the behaviors of their components. Trust can be achieved by employing reputation-based mechanisms. Nevertheless, the existing methods are still unable to fully guarantee the desired accountability and transparency. Aligned with the recent trends, advocating the distribution of trust to avoid the risks of having a single point of failure of the system, this work extends existing efforts on combining blockchain technologies (BCT) and MAS. To attain a trusted environment, we provide the architecture and implementation of a system that allows the agents to interact with each other and enables tracking how their reputation changes after every interaction. Agents reputations are computed transparently using smart contracts. Immutable distributed ledger stores reputation values, as well as services and their evaluations to ensure trustworthy interactions between the agents. We also developed a graphical interface to test different scenarios of interactions between the agents. Finally, we summarize and discuss the experience gained and explain the strategic choices when binding MAS and BCT.

[1]  Karl Aberer,et al.  A Multiagent System for Dynamic Data Aggregation in Medical Research , 2016, BioMed research international.

[2]  Aldo Franco Dragoni,et al.  A Generalized Approach to Consistency Based Belief Revision , 1995, AI*IA.

[3]  Stefan Resmerita,et al.  Conflict resolution in multi-agent systems , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[4]  Audun Jøsang,et al.  A survey of trust and reputation systems for online service provision , 2007, Decis. Support Syst..

[5]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[6]  Munindar P. Singh,et al.  An evidential model of distributed reputation management , 2002, AAMAS '02.

[7]  Stephen Kozlowski,et al.  An Audit Ecosystem to Support Blockchain-based Accounting and Assurance , 2018 .

[8]  Christian Cachin,et al.  Architecture of the Hyperledger Blockchain Fabric , 2016 .

[9]  Kuldar Taveter,et al.  Multi-Agent Systems and Blockchain: Results from a Systematic Literature Review , 2018, PAAMS.

[10]  Karima Qayumi,et al.  Multi-agent Based Intelligence Generation from Very Large Datasets , 2015, 2015 IEEE International Conference on Cloud Engineering.

[11]  Esmiralda Moradian,et al.  Knowledge Based and Intelligent Information and Engineering Systems Security in Multi-Agent Systems , 2015 .

[12]  Giuseppe Primiero,et al.  Multi-agent Based Simulations of Block-Free Distributed Ledgers , 2018, 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA).

[13]  Markus Kraft,et al.  Incorporating seller/buyer reputation-based system in blockchain-enabled emission trading application , 2018 .

[14]  Marijn Janssen,et al.  Innovation with open data: Essential elements of open data ecosystems , 2014, Inf. Polity.

[15]  Wamberto Weber Vasconcelos,et al.  Normative conflict resolution in multi-agent systems , 2009, Autonomous Agents and Multi-Agent Systems.

[16]  Feng Qian,et al.  Secure impulsive synchronization control of multi-agent systems under deception attacks , 2018, Inf. Sci..

[17]  Agostino Poggi,et al.  Developing Multi-agent Systems with JADE , 2007, ATAL.

[18]  Katia P. Sycara,et al.  Adding security and trust to multiagent systems , 2000, Appl. Artif. Intell..

[19]  Thomas Locher,et al.  When Can a Distributed Ledger Replace a Trusted Third Party? , 2018, 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[20]  Aldo Franco Dragoni,et al.  Learning Agents' Reliability Through Bayesian Conditioning: A Simulation Experiment , 1996, ECAI Workshop LDAIS / ICMAS Workshop LIOME.

[21]  J. Schreiber Foundations Of Statistics , 2016 .

[22]  Sarvapali D. Ramchurn,et al.  Trust in multi-agent systems , 2004, The Knowledge Engineering Review.

[23]  Marko Vukolic,et al.  The Quest for Scalable Blockchain Fabric: Proof-of-Work vs. BFT Replication , 2015, iNetSeC.

[24]  Aldo Franco Dragoni,et al.  MAXIMAL CONSISTENCY, THEORY OF EVIDENCE, AND BAYESIAN CONDITIONING IN THE INVESTIGATIVE DOMAIN , 2003, Cybern. Syst..

[25]  Eduardo Castelló Ferrer The blockchain: a new framework for robotic swarm systems , 2016, Proceedings of the Future Technologies Conference (FTC) 2018.

[26]  Aldo Franco Dragoni,et al.  Trusted Registration, Negotiation, and Service Evaluation in Multi-Agent Systems throughout the Blockchain Technology , 2018, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[27]  Laurent Chaudron,et al.  Conflicting agents: conflict management in multi-agent systems , 2001 .

[28]  Aldo Franco Dragoni,et al.  Distributed Belief Revision , 2004, Autonomous Agents and Multi-Agent Systems.

[29]  Fu-Shiung Hsieh Modeling and control of holonic manufacturing systems based on extended contract net protocol , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[30]  Aldo Franco Dragoni,et al.  A goal-oriented requirements engineering approach for the ambient assisted living domain , 2014, PETRA.

[31]  Giorgio C. Buttazzo,et al.  Agent-Based Systems for Telerehabilitation: Strengths, Limitations and Future Challenges , 2017, A2HC@AAMAS/A-HEALTH@PAAMS.

[32]  Paolo Sernani,et al.  Exploring the ambient assisted living domain: a systematic review , 2017, J. Ambient Intell. Humaniz. Comput..