Most algorithms for solving Markov decision processes rely on a discount factor, which ensures their convergence. It is generally assumed that using an artificially low discount f...
Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an interve...
The distributed constraint satisfaction problem (CSP) is a general formalization used to represent problems in distributed multi-agent systems. To deal with realistic problems, mu...
The usage and service options of a pubic network generally differ from a private (enterprise or home) network and consequently, the two networks are often configured differently. ...
The main purpose of this paper is to propose a Multi-Agent Autonomic and Bio-Inspired based framework with selfmanaging capabilities to solve complex scheduling problems using coo...