Accurate on-line support vector regression
Neural Computation
A tutorial on support vector regression
Statistics and Computing
A Swarm Based Approach for Task Allocation in Dynamic Agents Organizations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Physical interference impact in multi-robot task allocation auction methods
DIS '06 Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications
Distributed decision-making and task coordination in dynamic, uncertain and real-time multiagent environments
Multi-agent task allocation: learning when to say no
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multi-robot task allocation through vacancy chain scheduling
Robotics and Autonomous Systems
Task allocation via self-organizing swarm coalitions in distributed mobile sensor network
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Sequential auctions for heterogeneous task allocation in multiagent routing domains
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Coalition formation for task allocation: theory and algorithms
Autonomous Agents and Multi-Agent Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Multi-robot coalition formation
Multi-robot coalition formation
Information Sciences: an International Journal
Decentralized task allocation for surveillance systems with critical tasks
Robotics and Autonomous Systems
Non-additive multi-objective robot coalition formation
Expert Systems with Applications: An International Journal
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Task allocation is one of the main issues to be addressed in multi-robot systems, especially when the robots form coalitions and the tasks have to be fulfilled before a deadline. In general, it is difficult to foresee the time required by a coalition to finish a task because it depends, among other factors, on the physical interference. Interference is a phenomenon produced when two or more robots want to access the same point simultaneously. This paper presents a new model to predict the time to execute a task. Thanks to this model, the robots needed to carry out a task before a deadline can be determined. Within this framework, the robots learn the interference and therefore, the coalition's utility, from their past experience using an on-line Support Vector Regression method (SVR). Furthermore, the SVR model is used together with a new auction method called 'Double Round auction' (DR). It will be demonstrated that by combining the interference model and the DR process, the total utility of the system significantly increases compared to classical auction approaches. This is the first auction method that includes the physical interference effects and that can determine the coalition size during the execution time to address tasks with deadlines. Transport like tasks run on a simulator and on real robots have been used to validate the proposed solutions.