Abstract. Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, w...
RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule...
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
We present a general method for agents using ontologies as part of their knowledge representation to teach each other concepts to improve their communication and thus cooperation ...
This paper analyses some general issues about human language technology evaluation, focusing on economic aspects. It first provides a scientific rationale for the need to organize...