This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes ...
This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcemen...
Due to the various and dynamic nature of stimuli, decisions of intelligent agents must rely on the coordination of complex cognitive systems. This paper precisely focusses on a gen...
Interactions between agents are traditionally specified as interaction protocols using notations such as Petri nets, AUML, or finite state machines. These protocols are a poor ...
We discuss challenges and opportunities for developing generalized task markets where human and machine intelligence are enlisted to solve problems, based on a consideration of th...