Dynamic Perfect Hashing: Upper and Lower Bounds
SIAM Journal on Computing
Artificial Neural Networks: Approximation and Learning Theory
Artificial Neural Networks: Approximation and Learning Theory
Introduction to Multi-Agent Modified Q-Learning Routing for Computer Networks
AICT-SAPIR-ELETE '05 Proceedings of the Advanced Industrial Conference on Telecommunications/Service Assurance with Partial and Intermittent Resources Conference/E-Learning on Telecommunications Workshop
Single-machine group scheduling with a time-dependent learning effect
Computers and Operations Research
The learning effect: Getting to the core of the problem
Information Processing Letters
A new approach to the learning effect: Beyond the learning curve restrictions
Computers and Operations Research
A single-machine bi-criterion learning scheduling problem with release times
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Assembly line balancing problem with deterioration tasks and learning effect
Expert Systems with Applications: An International Journal
Experience-based approach to scheduling problems with the learning effect
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Adaptive job routing and scheduling
Engineering Applications of Artificial Intelligence
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Information Sciences: an International Journal
The solution algorithms for the multiprocessor scheduling with workspan criterion
Expert Systems with Applications: An International Journal
Computers and Operations Research
Information Sciences: an International Journal
Adaptive learning algorithm of self-organizing teams
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, we point out that the learning effect, in the form known from industrial systems or services sectors, takes place in multi-agent optimization. In particular, we show that the minimization of a total transmission cost of packets in a computer network that uses a reinforcement learning routing algorithm can be expressed as the single machine makespan minimization scheduling problem with the learning effect. On this basis, we prove this problem is at least NP-hard (even off-line version). However, we derive properties, which allow us to construct on-line scheduling algorithms that can be applied in the computer network to increase its efficiency by the utilization of its learning ability.