Robots are complex entities that can be modeled as multi-agent systems. The multi-agent paradigm provides an integrated intelligence framework such as a path planning agent that us...
Despite increasing deployment of agent technologies in several business and industry domains, user confidence in fully automated agent driven applications is noticeably lacking. T...
In this paper, we present a semantical approach to multi-agent belief revision and belief update. For this, we introduce relational structures called conditional doxastic models (...
We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...
Application of autonomous intelligent systems into airspace domain is very important nowadays. The paper presents decentralized collision avoidance algorithm utilizing a solution ...