Add to ORKGThis article describes a parallel and distributed machine learning approach to a basic variant of the job assignment problem. The approach is in the line of the multiagent learning paradigm as investigated in distributed artificial intelligence. The job assignment problem requires to solve the task of assigning a given set of jobs to a given set of executing nodes in such a way that the overall execution time is reduced, where the individual jobs may depend on each other and the individual nodes may di#er from each other in their execution abilities
International audienceThe linear assignment problem is a fundamental com-binatorial problem and a cl...
We report on the improvements that can be achieved by applying machine learning techniques, in parti...
In managing multiprocessing of parallel distributed systems the central issue is the scheduling of j...
Multi-machine scheduling, that is, the assigment of jobs to machines such that certain performance d...
In a multi-agent system, multi-job assignment is an optimization problem that seeks to minimize tota...
In a distributed system of networked heterogeneous processors, an efficient assignment of communicat...
The demand for artificial intelligence has grown significantly over the past decade, and this growth...
Decentralized decision-making is an active research topic in artificial intelligence. In a distribut...
The demand for artificial intelligence has grown significantly over the past decade, and this growth...
Multi-agent task allocation problems pervade a wide range of real-world applications, such as search...
An efficient assignment of tasks to the processors is imperative for achieving a fast job turnaround...
Coded distributed computing framework enables large-scale machine learning (ML) models to be trained...
Conventional microtask crowdsourcing platforms rely on a random task distribution strategy and repea...
The paper proposes using genetic algorithms-based learning classifier system (CS) to solve multiproc...
It is difficult to coordinate the various processes in the process industry. We built a multiagent d...
International audienceThe linear assignment problem is a fundamental com-binatorial problem and a cl...
We report on the improvements that can be achieved by applying machine learning techniques, in parti...
In managing multiprocessing of parallel distributed systems the central issue is the scheduling of j...
Multi-machine scheduling, that is, the assigment of jobs to machines such that certain performance d...
In a multi-agent system, multi-job assignment is an optimization problem that seeks to minimize tota...
In a distributed system of networked heterogeneous processors, an efficient assignment of communicat...
The demand for artificial intelligence has grown significantly over the past decade, and this growth...
Decentralized decision-making is an active research topic in artificial intelligence. In a distribut...
The demand for artificial intelligence has grown significantly over the past decade, and this growth...
Multi-agent task allocation problems pervade a wide range of real-world applications, such as search...
An efficient assignment of tasks to the processors is imperative for achieving a fast job turnaround...
Coded distributed computing framework enables large-scale machine learning (ML) models to be trained...
Conventional microtask crowdsourcing platforms rely on a random task distribution strategy and repea...
The paper proposes using genetic algorithms-based learning classifier system (CS) to solve multiproc...
It is difficult to coordinate the various processes in the process industry. We built a multiagent d...
International audienceThe linear assignment problem is a fundamental com-binatorial problem and a cl...
We report on the improvements that can be achieved by applying machine learning techniques, in parti...
In managing multiprocessing of parallel distributed systems the central issue is the scheduling of j...