Large-scale, Dynamic and Distributed Coalition Formation with Spatial and Temporal Constraints

The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem in which few agents have to perform many tasks, each with its deadline and workload. To maximize the number of completed tasks, the agents need to cooperate by forming, disbanding and reforming coalitions. The original mathematical programming formulation of the CFSTP is difficult to implement, since it is lengthy and based on the problematic Big-M method. In this paper, we propose a compact and easy-to-implement formulation. Moreover, we design D-CTS, a distributed version of the state-of-the-art CFSTP algorithm. Using public London Fire Brigade records, we create a dataset with 347588 tasks and a test framework that simulates the mobilization of firefighters in dynamic environments. In problems with up to 150 agents and 3000 tasks, compared to DSA-SDP, a state-of-the-art distributed algorithm, D-CTS completes 3.79%± [42.22%, 1.96%] more tasks, and is one order of magnitude more efficient in terms of communication overhead and time complexity. D-CTS sets the first large-scale, dynamic and distributed CFSTP benchmark.

[1]  Aldy Gunawan,et al.  Orienteering Problems , 2019, EURO Advanced Tutorials on Operational Research.

[2]  Brendan J. Frey,et al.  Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.

[3]  D. Atkin OR scheduling algorithms. , 2000, Anesthesiology.

[4]  Sarvapali D. Ramchurn,et al.  Anytime and Efficient Multi-agent Coordination for Disaster Response , 2021, SN Computer Science.

[5]  M. Wooldridge,et al.  On the Formal Semantics of Speech-Act Based Communication in an Agent-Oriented Programming Language , 2007, J. Artif. Intell. Res..

[6]  Nicholas R. Jennings,et al.  On Population-Based Algorithms for Distributed Constraint Optimization Problems , 2020, ArXiv.

[7]  Marco Spuri,et al.  Deadline Scheduling for Real-Time Systems: Edf and Related Algorithms , 2013 .

[8]  Sarvapali D. Ramchurn,et al.  Coalition formation with spatial and temporal constraints , 2010, AAMAS.

[9]  Victor R. Lesser,et al.  Effective Variants of the Max-Sum Algorithm for Radar Coordination and Scheduling , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

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

[11]  Hakim Mitiche,et al.  A taxonomy for task allocation problems with temporal and ordering constraints , 2017, Robotics Auton. Syst..

[12]  Victor R. Lesser,et al.  Challenges for multi-agent coordination theory based on empirical observations , 2014, AAMAS.

[13]  Makoto Yokoo,et al.  Distributed constraint satisfaction for formalizing distributed problem solving , 1992, [1992] Proceedings of the 12th International Conference on Distributed Computing Systems.

[14]  Amnon Meisels,et al.  Distributed Search by Constrained Agents: Algorithms, Performance, Communication , 2007, Advanced Information and Knowledge Processing.

[15]  Sarvapali D. Ramchurn,et al.  Decentralized Coordination in RoboCup Rescue , 2010, Comput. J..

[16]  Alessandro Farinelli,et al.  Efficient Inter-Team Task Allocation in RoboCup Rescue , 2015, AAMAS.

[17]  Maria Edith Csermelyi Balogh On scheduling algorithms , 1965 .

[18]  Gabriel Oliver,et al.  Multi-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions , 2017, PloS one.

[19]  Sofia Amador Nelke,et al.  Market Clearing–based Dynamic Multi-agent Task Allocation , 2020, ACM Trans. Intell. Syst. Technol..

[20]  Ana L. C. Bazzan,et al.  Evaluating the performance of DCOP algorithms in a real world, dynamic problem , 2008, AAMAS.

[21]  H.-A. Loeliger,et al.  An introduction to factor graphs , 2004, IEEE Signal Process. Mag..

[22]  Sarvapali D. Ramchurn,et al.  An Anytime Algorithm for Optimal Coalition Structure Generation , 2014, J. Artif. Intell. Res..

[23]  Steven Okamoto,et al.  Distributed Breakout: Beyond Satisfaction , 2016, IJCAI.

[24]  Fabrício Enembreck,et al.  Distributed Constraint Optimization Problems: Review and perspectives , 2014, Expert Syst. Appl..

[25]  Hiroaki Kitano,et al.  RoboCup Rescue A Grand Challenge for Multiagent and Intelligent Systems , 2001 .

[26]  David Alexander,et al.  Principles of Emergency Planning and Management , 2002 .

[27]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[28]  Alborz Geramifard,et al.  Cooperative Mission Planning for Multi-UAV Teams , 2015 .

[29]  Hoong Chuin Lau,et al.  Distributed Gibbs: A Linear-Space Sampling-Based DCOP Algorithm , 2019, J. Artif. Intell. Res..

[30]  Robin R. Murphy,et al.  Disaster Robotics , 2014, Springer Handbook of Robotics, 2nd Ed..

[31]  Milind Tambe,et al.  Taking DCOP to the real world: efficient complete solutions for distributed multi-event scheduling , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[32]  Roie Zivan,et al.  Max-sum Revisited: The Real Power of Damping , 2017, AAMAS.

[33]  S. Nash,et al.  Linear and Nonlinear Optimization , 2008 .

[34]  Huimin Zheng,et al.  Dynamic, distributed constraint solving and thermodynamic theory , 2017, Autonomous Agents and Multi-Agent Systems.

[35]  Steven Okamoto,et al.  Balancing exploration and exploitation in incomplete Min/Max-sum inference for distributed constraint optimization , 2017, Autonomous Agents and Multi-Agent Systems.

[36]  Amnon Meisels,et al.  Distributed Search by Constrained Agents , 2007, IDC.

[37]  M. Yokoo,et al.  Distributed Breakout Algorithm for Solving Distributed Constraint Satisfaction Problems , 1996 .

[38]  Marco Spuri,et al.  Deadline Scheduling for Real-Time Systems , 2011 .

[39]  C. Hewitt The challenge of open systems , 1990 .

[40]  Weixiong Zhang,et al.  Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks , 2005, Artif. Intell..

[41]  Klaus-Peter Zauner,et al.  Sparse Robot Swarms: Moving Swarms to Real-World Applications , 2020, Frontiers in Robotics and AI.

[42]  Milind Tambe,et al.  Quality Guarantees on k-Optimal Solutions for Distributed Constraint Optimization Problems , 2007, IJCAI.

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

[44]  Steven Reece,et al.  Human–agent collaboration for disaster response , 2015, Autonomous Agents and Multi-Agent Systems.

[45]  Sarvapali D. Ramchurn,et al.  Multi-Agent Routing and Scheduling Through Coalition Formation , 2021, ArXiv.

[46]  G. Nemhauser,et al.  Integer Programming , 2020 .

[47]  Nicholas R. Jennings,et al.  Coalition structure generation: A survey , 2015, Artif. Intell..

[48]  Sarvapali D. Ramchurn,et al.  Planning Search and Rescue Missions for UAV Teams , 2016, ECAI.

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

[50]  Milind Tambe,et al.  Quality guarantees for region optimal DCOP algorithms , 2011, AAMAS.

[51]  Milind Tambe,et al.  Asynchronous algorithms for approximate distributed constraint optimization with quality bounds , 2010, AAMAS.

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

[53]  Nicholas R. Jennings,et al.  A Hybrid Algorithm for Coalition Structure Generation , 2012, AAAI.

[54]  Aditya K. Ghose,et al.  SBDO: A New Robust Approach to Dynamic Distributed Constraint Optimisation , 2009, PRIMA.

[55]  Richard M. Soland,et al.  A branch and bound algorithm for the generalized assignment problem , 1975, Math. Program..