RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Language run-time systems are routinely used to host potentially buggy or malicious codelets — software modules, agents, applets, etc. — in a secure environment. A number of t...
Collaborative Browsing is a new and promising research area whose purpose is to provide new collaboration schemes among users browsing the Web. To become an efficient collaboratio...
This paper addresses the problem of multiagent task allocation in extreme teams. An extreme team is composed by a large number of agents with overlapping functionality operating i...
The emerging field of mobile computing (MC) studies systems in which computational components may change locations. In terms of hardware, mobile work is usually across heterogene...