Intelligent agents acting in real world environments need to synthesize their course of action based on multiple sources of knowledge. They also need to generate plans that smoothl...
Abstract. One of the key problems in model-based reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large relational domains, in wh...
This paper is focused on how a general-purpose hierarchical planning representation, based on the HTN paradigm, can be used to support the representation of oncology treatment pro...
In this paper we present the robot programming and planning language Readylog, a Golog dialect which was developed to support the decision making of robots acting in dynamic real-...
Volunteer Computing (VC) is a paradigm that takes advantage of idle cycles from computing resources donated by volunteers and connected through the Internet to compute large-scale...