Preference Elicitation in DCOPs for Scheduling Devices in Smart Buildings

Distributed Constraint Optimization Problems (DCOPs) offer a powerful approach for the description and resolution of cooperative multi-agent problems. In such model a group of agents coordinate their actions to optimize a global objective function, taking into account their preferences and constraints. A core limitation of this model is the assumption that all agents’ preferences are specified a priori. Unfortunately, in a number of application domains, such knowledge is not assumed, and these values may become available only after being elicited from users in the domain. Motivated by the current developments in smart buildings we explore the effect of preference elicitation in scheduling smart appliances within a network of interconnected buildings, with the goal of reducing the users’ energy consumption costs, while taking into account the comfort of the occupants. This paper makes the following contributions: (1) It introduces the Smart Building Devices Scheduling (SBDS) problem and maps it as a DCOP; (2) It proposes a general model for preference elicitation in DCOPs; (3) and It empirically evaluates the effect of several heuristics to select a set of preferences to elicit in SBDS problems.

[1]  Steven Okamoto,et al.  Explorative anytime local search for distributed constraint optimization , 2014, Artif. Intell..

[2]  Enrico Pontelli,et al.  Distributed Constraint Optimization Problems and Applications: A Survey , 2016, J. Artif. Intell. Res..

[3]  Leandro Magatão,et al.  Enhancing Supply Chain Decisions Using Constraint Programming: A Case Study , 2007, MICAI.

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

[5]  Marco Aiello,et al.  Optimizing Energy Costs for Offices Connected to the Smart Grid , 2012, IEEE Transactions on Smart Grid.

[6]  Feng Wu,et al.  Regret-Based Multi-Agent Coordination with Uncertain Task Rewards , 2014, AAAI.

[7]  Nilanjan Banerjee,et al.  Predictability of energy use in homes , 2014, International Green Computing Conference.

[8]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[9]  Lazaros G. Papageorgiou,et al.  Efficient energy consumption and operation management in a smart building with microgrid , 2013 .

[10]  Hoong Chuin Lau,et al.  Decentralized Multi-Agent Reinforcement Learning in Average-Reward Dynamic DCOPs , 2014, AAAI.

[11]  Enrico Pontelli,et al.  ASP-DPOP: solving distributed constraint optimization problems with logic programming , 2014, AAMAS.

[12]  Makoto Yokoo,et al.  Distributed on-Line Multi-Agent Optimization under Uncertainty: Balancing Exploration and Exploitation , 2011, Adv. Complex Syst..

[13]  Meritxell Vinyals,et al.  Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law , 2010, Autonomous Agents and Multi-Agent Systems.

[14]  Boi Faltings,et al.  A Scalable Method for Multiagent Constraint Optimization , 2005, IJCAI.

[15]  Sarvapali D. Ramchurn,et al.  Optimal decentralised dispatch of embedded generation in the smart grid , 2012, AAMAS.

[16]  Enrico Pontelli,et al.  Improving DPOP with Branch Consistency for Solving Distributed Constraint Optimization Problems , 2014, CP.

[17]  Sylvie Thiébaux,et al.  Distributed Multi-Period Optimal Power Flow for Demand Response in Microgrids , 2015, e-Energy.

[18]  Enrico Pontelli,et al.  Multi-Variable Agents Decomposition for DCOPs , 2016, AAAI Conference on Artificial Intelligence.

[19]  Steven Okamoto,et al.  Distributed constraint optimization for teams of mobile sensing agents , 2014, Autonomous Agents and Multi-Agent Systems.

[20]  Enrico Pontelli,et al.  ER-DCOPs: A Framework for Distributed Constraint Optimization with Uncertainty in Constraint Utilities , 2016, AAMAS.

[21]  Slim Abdennadher,et al.  Nurse Scheduling using Constraint Logic Programming , 1999, AAAI/IAAI.

[22]  Nicholas R. Jennings,et al.  Decentralised coordination of low-power embedded devices using the max-sum algorithm , 2008, AAMAS.

[23]  Peter J. Stuckey,et al.  MiniZinc: Towards a Standard CP Modelling Language , 2007, CP.

[24]  H. Levy Stochastic Dominance: Investment Decision Making under Uncertainty , 2010 .

[25]  Milind Tambe,et al.  Distributed Algorithms for DCOP: A Graphical-Game-Based Approach , 2004, PDCS.

[26]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[27]  Sarvapali D. Ramchurn,et al.  Forecasting Multi-Appliance Usage for Smart Home Energy Management , 2013, IJCAI.

[28]  Makoto Yokoo,et al.  Distributed Problem Solving , 2012, AI Mag..

[29]  Nicholas R. Jennings,et al.  DCOPs and bandits: exploration and exploitation in decentralised coordination , 2012, AAMAS.

[30]  Makoto Yokoo,et al.  Coalition Structure Generation based on Distributed Constraint Optimization , 2010, AAAI.

[31]  Sven Koenig,et al.  BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm , 2008, AAMAS.

[32]  Wei-Min Shen,et al.  Distributed constraint optimization for multiagent systems , 2003 .

[33]  Pascal Van Hentenryck,et al.  Residential Demand Response under Uncertainty , 2013, CP.

[34]  Shing-Chow Chan,et al.  Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing , 2012, IEEE Transactions on Smart Grid.