Modeling the Management of Water Resources Systems Using Multi-Objective DCOPs

Multi-agent systems are being increasingly used in environmental modeling applications to characterize human behavior and interactions with natural processes. A model based on Distributed Constraint Optimization Problems (DCOPs) has been recently proposed for studying the management of water resources systems from the point of view of a regulating institution in charge of coordinating multiple distributed decision-makers (agents). However, this DCOP-based model does not explicitly account for the variety of stakeholders' and regulators' interests that are generally involved, representing incommensurable and often competing objectives. In this paper, we provide a Multi-Objective DCOP (MODCOP) model that supports distributed water resources management through the exploration of tradeoffs across different agents' objectives. Among the available algorithms for solving the resulting MO-DCOP, we choose a variant of the B-MOMS algorithm because it allows identifying (an approximation of) the whole Pareto frontier for the problem. Experimental results conducted on a number of randomly generated water systems show that the approximation introduced by the solving algorithm is limited for most of the systems.

[1]  Makoto Yokoo,et al.  Adopt: asynchronous distributed constraint optimization with quality guarantees , 2005, Artif. Intell..

[2]  John M. Flach,et al.  MGA: a decision support system for complex, incompletely defined problems , 1990, IEEE Trans. Syst. Man Cybern..

[3]  Ximing Cai,et al.  A decentralized optimization algorithm for multiagent system–based watershed management , 2009 .

[4]  Makoto Yokoo,et al.  Distributed Search Method with Bounded Cost Vectors on Multiple Objective DCOPs , 2012, PRIMA.

[5]  Lufthansa Kanta,et al.  Complex Adaptive Systems Framework to Assess Supply-Side and Demand-Side Management for Urban Water Resources , 2014 .

[6]  Yoav Shoham,et al.  Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations , 2009 .

[7]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[8]  N. P. Nguyena,et al.  Water quality trading with asymmetric information , uncertainty and transaction costs : A stochastic agent-based simulation , 2015 .

[9]  Andrea Castelletti,et al.  Multiagent Systems and Distributed Constraint Reasoning for Regulatory Mechanism Design in Water Management , 2015 .

[10]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Nicholas R. Jennings,et al.  Bounded decentralised coordination over multiple objectives , 2011, AAMAS.

[12]  José Manuel Galán,et al.  An agent‐based model for domestic water management in Valladolid metropolitan area , 2009 .

[13]  A. Castelletti,et al.  Assessing the value of cooperation and information exchange in large water resources systems by agent‐based optimization , 2013 .

[14]  M. Ehsan Shafiee,et al.  An Agent-based Modeling Framework for Sociotechnical Simulation of Water Distribution Contamination Events , 2013, ArXiv.

[15]  D. Whittington,et al.  Water Resources Management in the Nile Basin: The Economic Value of Cooperation , 2005 .

[16]  Roland W. Scholz,et al.  Feedback loops and types of adaptation in the modelling of land-use decisions in an agent-based simulation , 2012, Environ. Model. Softw..

[17]  I. Huskova,et al.  An agent model to simulate water markets , 2012 .

[18]  François Bousquet,et al.  Multi-agent simulations and ecosystem management: a review , 2004 .

[19]  L. An,et al.  Modeling human decisions in coupled human and natural systems : Review of agent-based models , 2012 .

[20]  Thomas Berger,et al.  An agent-based simulation model of human-environment interactions in agricultural systems , 2011, Environ. Model. Softw..

[21]  Avi Ostfeld,et al.  Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions , 2014, Environ. Model. Softw..

[22]  J. Eheart,et al.  An agent‐based model of farmer decision‐making and water quality impacts at the watershed scale under markets for carbon allowances and a second‐generation biofuel crop , 2011 .

[23]  Nicholas R. Jennings,et al.  Bounded approximate decentralised coordination via the max-sum algorithm , 2009, Artif. Intell..

[24]  Agostino Poggi,et al.  Multiagent Systems , 2006, Intelligenza Artificiale.

[25]  Ximing Cai,et al.  Decentralized Optimization Method for Water Allocation Management in the Yellow River Basin , 2012 .

[26]  Ioannis N. Athanasiadis,et al.  A review of agent-based systems applied in environ- mental informatics , 2005 .

[27]  Pierre A. Humblet,et al.  A Distributed Algorithm for Minimum-Weight Spanning Trees , 1983, TOPL.