Today's wireless sensor networks have limited flexibility because their software is static. Mobile agents alleviate this problem by introducing mobile code and state. Mobile ...
Daniel Massaguer, Chien-Liang Fok, Nalini Venkatas...
This paper is concerned with how multi-agent reinforcement learning algorithms can practically be applied to real-life problems. Recently, a new coordinated multi-agent exploratio...
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
A mobile agent (robot), modeled as a finite automaton, has to visit all nodes of a regular graph. How does the memory size of the agent (the number of states of the automaton) inf...
Debugging multi-agent systems, which are concurrent, distributed, and consist of complex components, is difficult, yet crucial. In earlier work we have proposed mechanisms whereby...