Both explanation-based and inductive learning techniques have proven successful in a variety of distributed domains. However, learning in multi-agent systems does not necessarily ...
This paper uses partially observable Markov decision processes (POMDP’s) as a basic framework for MultiAgent planning. We distinguish three perspectives: first one is that of a...
Bharaneedharan Rathnasabapathy, Piotr J. Gmytrasie...
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...
The Rule Responder project (responder.ruleml.org) extends the Semantic Web towards a Pragmatic Web infrastructure for collaborative human-computer networks. These allow semi-autom...
Adrian Paschke, Harold Boley, Alexander Kozlenkov,...
We present a novel approach to interaction-oriented programming based on declaratively representing communication protocols. Our approach exhibits the following distinguishing fea...