Maintaining ethical resolution in distributed constraint reasoning

Multi-agent systems (MAS) consist of autonomous agents that parcel out different tasks and make decisions in dynamic environments. Distributed constraint satisfaction problem (DisCSP) is the most effective and applicative MAS framework. In DisCSP, each agent is connected to the other agents via constraints and holds its own local constrained problem. Those agents find solutions satisfying their own constraints and the linking ones too by collaborating and exchanging messages holding their instantiations. This formalism does not take into consideration the possibility of the presence of unethical agents which can make irrelevant or even dangerous decisions, especially when human agents are involved in the resolution. In this paper, we propose an extension of the DisCSP into an ethical formalism “E-DisCSP”. It allows to control agents, detect intrusions and apply the convenient actions when an unethical agent is picked up. All these functionalities are done via control framework, in order to maintain the DisCSP resolution as normal as possible. Experimental results show the efficiency of our contribution. The detection rate of unethical agents achieves up to 100%. And the convenient actions’ application allows, to go from 45 to 0% of wrong solutions.

[1]  John McCarthy,et al.  Generality in artificial intelligence , 1987, Resonance.

[2]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[3]  Mete Celik,et al.  Anomaly detection in temperature data using DBSCAN algorithm , 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications.

[4]  Imade Benelallam,et al.  Selective Sorting and Smart Nogood-Based Complex Distributed Constraint Satisfaction Problems , 2019 .

[5]  Wei-Min Shen,et al.  A Dynamic Distributed Constraint Satisfaction Approach to Resource Allocation , 2001, CP.

[6]  Jean-Pierre Mano,et al.  Adaptive Multi-agent System for Multi-sensor Maritime Surveillance , 2010, PAAMS.

[7]  Amnon Meisels,et al.  Asynchronous Forward-checking for DisCSPs , 2007, Constraints.

[8]  Makoto Yokoo,et al.  Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems , 1995, CP.

[9]  João P. Martins,et al.  The Truth, the Whole Truth, and Nothing But the Truth , 1990, AI Mag..

[10]  Kagan Tumer,et al.  Distributed agent-based air traffic flow management , 2007, AAMAS '07.

[11]  Imade Benelallam,et al.  Constraint programming based techniques for medical resources optimization: medical internships planning , 2020, J. Ambient Intell. Humaniz. Comput..

[12]  Christian Bessiere,et al.  Improving Asynchronous Backtracking for Dealing with Complex Local Problems , 2004, ECAI.

[13]  Kenneth D. Forbus,et al.  Building Problem Solvers , 1993 .

[14]  Makoto Yokoo,et al.  The Distributed Constraint Satisfaction Problem: Formalization and Algorithms , 1998, IEEE Trans. Knowl. Data Eng..

[15]  Noury Bouraqadi,et al.  Distributed Constraint Reasoning Applied to Multi-robot Exploration , 2009, 2009 21st IEEE International Conference on Tools with Artificial Intelligence.

[16]  David Camacho,et al.  Constrained multi-objective optimization for multi-UAV planning , 2019, J. Ambient Intell. Humaniz. Comput..

[17]  Milind Tambe,et al.  Distributed Sensor Networks: A Multiagent Perspective , 2003 .

[18]  Imade Benelallam,et al.  MP-ABT: A Minimal Perturbation Approach for Complex Local Problems , 2017, ICAART.

[19]  Driss Diri,et al.  Élaboration d'un système de maintien de vérité : une approche orientée objet , 2009 .

[20]  Eliezer Yudkowsky,et al.  The Ethics of Artificial Intelligence , 2014, Artificial Intelligence Safety and Security.

[21]  Vipin Kumar,et al.  Algorithms for Constraint-Satisfaction Problems: A Survey , 1992, AI Mag..

[22]  Ian Miguel.  Dynamic flexible constraint satisfaction and its application to AI planning , 2001 .

[23]  Marie-Pierre Gleizes,et al.  A Self-adaptive Multi-Agent System for Abnormal Behavior Detection in Maritime Surveillance , 2012, KES-AMSTA.

[24]  Toramatsu Shintani,et al.  Scheduling Meetings Using Distributed Valued Constraint Satisfaction Algorithm , 2000, ECAI.

[25]  Imade Benelallam,et al.  Contribution to Modeling Smart Grid Problem with the Constraint Satisfaction Problem Formalism , 2018, MedPRAI '18.

[26]  Zakarya Erraji,et al.  Dynamic JChoc: A Distributed Constraints Reasoning Platform for Dynamically Changing Environments , 2015, ICAART.

[27]  Victor R. Lesser,et al.  Solving distributed constraint optimization problems using cooperative mediation , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[28]  Christian Bessiere,et al.  Asynchronous backtracking without adding links: a new member in the ABT family , 2005, Artif. Intell..

[29]  Olivier Boissier,et al.  Ethical Judgment of Agents' Behaviors in Multi-Agent Systems , 2016, AAMAS.

[30]  Imade Benelallam,et al.  Detection of Unethical Intelligent Agents in Ethical Distributed Constraint Satisfaction Problems , 2018, MedPRAI '18.

[31]  Christian Bessiere,et al.  Nogood-based asynchronous forward checking algorithms , 2013, Constraints.

[32]  Javier Bajo,et al.  A multi-agent architecture for mobile sensing systems , 2019, Journal of Ambient Intelligence and Humanized Computing.

[33]  Paolo Traverso,et al.  Automated planning - theory and practice , 2004 .

[34]  David Clark,et al.  Using a Genetic Algorithm to Optimise Maritime Surveillance Performed by Space-based Sensors , 2011 .

[35]  Salvatore Venticinque,et al.  Agents based multi-criteria decision-aid , 2013, Journal of Ambient Intelligence and Humanized Computing.

[36]  Jon Doyle.  A truth maintenance system , 1981 .

[37]  N. Sankar Ram,et al.  Analyze traffic forecast for decentralized multi agent system using I-ACO routing algorithm , 2018 .