This paper presents a method by which a reinforcement learning agent can automatically discover certain types of subgoals online. By creating useful new subgoals while learning, the agent is able to… (More)
We analyze the use of built-in policies, or macro-actions, as a form of domain knowledge that can improve the speed and scaling of reinforcement learning algorithms. Such macro-actions are often used… (More)
Sensory input from the airways to suprapontine brain regions contributes to respiratory sensations and the regulation of respiratory function. However, relatively little is known about the central… (More)
We introduced an arcade-style gaming environment for use in a mixed undergraduate and graduate introductory artificial intelligence (AI) course. Our primary goal in this course was to provide… (More)
2008 Eighth IEEE International Conference on Data…
2008
We introduce spatiotemporal relational probability trees (SRPTs), probability estimation trees for relational data that can vary in both space and time. The SRPT algorithm addresses the exponential… (More)
The execution order of a block of computer instructions can make a difference in its running time by a factor of two or more. In order to achieve the best possible speed, compilers use heuristic… (More)
The Domain Adaptive Information System (DAIS) is a mobile intebigent agent system for information discovery and dissemination in a military intelligence network DAIS is used reliably over low… (More)
We analyze publication patterns in theoretical high-energy physics using a relational learning approach. We focus on four related areas: understanding and identifying patterns of citations, examining… (More)
Several researchers have proposed reinforcement learning methods that obtain ad vantages in learning by using temporally extended actions or macro actions but none has carefully analyzed what these… (More)