This paper presents an automatic road extraction technique that combines information from aerial photos and laser scanning data (LSD). This innovative Road Extraction Assisted by Laser (REAL) can detect the road edges shadowed by surrounding high objects. A new concept of Associated Road Line (ARL) graph from LSD is introduced to enhance Real Road Line (RRL) graph from aerial photos. The extrac...

This paper presents an algorithm for the discovery of building blocks in genetic programming (GP) called adaptive representation through learning (ARL). The central idea of ARL is the adaptation of the problem representation, by extending the set of terminals and functions with a set of evolvable subroutines. The set of subroutines extracts common knowledge emerging during the evolutionary proc...

Almost all the work in Average-reward Reinforcement Learning (ARL) so far has fo-cused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a model-based ARL method called H-learning to address the scale-up problem. We extend H-learning to learn action models and reward functions in the form of Bayesian networks, and approxima...

Cassie Wagner is Web Development Librarian in the Morris Library at Southern Illinois University Carbondale; e-mail: [email protected]. ©Cassie Wagner This study examines the extent to which ARL academic libraries collect graphic novels. Using a core list of 176 titles developed from winners of major comics industry awards and a library-focused “best of” list, the holdings of 111 ARL academic...

Procedural representations of control policies have two advantages when facing the scale-up problem in learning tasks. First they are implicit, with potential for inductive generalization over a very large set of situations. Second they facilitate modularization. In this paper we compare several randomized algorithms for learning modular procedural representations. The main algorithm, called Ad...

Recently, there has been growing interest in average-reward reinforcement learning (ARL), an undiscounted optimality framework that is applicable to many diierent control tasks. ARL seeks to compute gain-optimal control policies that maximize the expected payoo per step. However, gain-optimality has some intrinsic limitations as an optimality criterion, since for example, it cannot distinguish ...

EWMA control charts designed for monitoring the variance or the mean and the variance of a normally distributed variable are either based on the log transformation of the sample variance S or provide only rough ARL results. Gan (1995), as the most prominent example for the simultaneous case, calculated ARL values precisely for X̄-lnS EWMA schemes. The results in Knoth and Schmid (2002) for X̄-S o...

Average-reward reinforcement learning (ARL) is an undiscounted optimality framework that is generally applicable to a broad range of control tasks. ARL computes gain-optimal control policies that maximize the expected payoff per step. However, gainoptimality has some intrinsic limitations as an optimality criterion, since for example, it cannot distinguish between different policies that all re...

Persons infected with the human immunodeficiency virus (HIV) are at increased risk for the development of malignant lymphoma.' When analyzed with molecular techniques, all of these acquired immunodeficiency syndrome (AIDS)-related lymphomas (ARL) have been shown to contain clonal rearrangements of portions of B-cell genes such as Ig heavy chain (JH) and K-lg light chain Clonal rearrangements of...