This paper presents a control structure for general purpose image understanding that addresses both the high level of uncertainty in local hypotheses and the computational complex...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
This paper proposes a two-phase example-based machine translation methodology which develops translation templates from examples and then translates using template matching. This ...
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 ...
By expanding the teaching styles used in computer science classrooms, we can expand the audience of students that enjoy and excel in technology. Rather than focusing on major curr...