Motivated by applications such as the spread of epidemics and the propagation of influence in social networks, we propose a formal model for analyzing the dynamics of such networ...
Christopher L. Barrett, Harry B. Hunt III, Madhav ...
The options framework provides a method for reinforcement learning agents to build new high-level skills. However, since options are usually learned in the same state space as the...
Hypertree decomposition has been shown to be the most general CSP decomposition method. However, so far the exact methods are not able to find optimal hypertree decompositions of...
Abstract. We propose an approach for extending both the terminological and the assertional part of a Description Logic knowledge base by using information provided by the knowledge...
Franz Baader, Bernhard Ganter, Baris Sertkaya, Ulr...
Buyers and sellers in online auctions are faced with the task of deciding who to entrust their business to based on a very limited amount of information. Current trust ratings on ...
John O'Donovan, Barry Smyth, Vesile Evrim, Dennis ...
We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other ...
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
Deictic representation is a representational paradigm, based on selective attention and pointers, that allows an agent to learn and reason about rich complex environments. In this...
Balaraman Ravindran, Andrew G. Barto, Vimal Mathew
Classic direct mechanisms require full type (or utility) revelation from participating agents, something that can be very difficult in practical multi-attribute settings. In this...