The application of genetic algorithms in a career planning environment: CAPTAINS
International Journal of Human-Computer Interaction
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
A fast learning algorithm for deep belief nets
Neural Computation
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
Grid management support by means of collaborative learning agents
GMAC '09 Proceedings of the 6th international conference industry session on Grids meets autonomic computing
Approximation of the two-part MDL code
IEEE Transactions on Information Theory
Using MDL for grammar induction
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Ant colony optimization for routing and load-balancing: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
We introduce a formal paradigm to study global adaptive behavior of organizations of collaborative agents with local learning capabilities. Our model is based on an extension of the classical language learning setting in which a teacher provides examples to a student that must guess a correct grammar. In our model the teacher is transformed in to a workload dispatcher and the student is replaced by an organization of worker-agents. The jobs that the dispatcher creates consist of sequences of tasks that can be modeled as sentences of a language. The agents in the organization have language learning capabilities that can be used to learn local work-distribution strategies. In this context one can study the conditions under which the organization can adapt itself to structural pressure from an environment. We show that local learning capabilities contribute to global performance improvements. We have implemented our theoretical framework in a workbench that can be used to run simulations. We discuss some results of these simulations. We believe that this approach provides a viable framework to study processes of self-organization and optimization of collaborative agent networks.