8531327
Towards automatic shaping in robot navigation
Shaping is a potentially powerful tool in reinforcement learning applications. Shaping often fails to function effectively because of a lack of understanding about its effects when applied in reinforcement learning settings and the use of inadequate algorithms in its implementation. Due to these difficulties current shaping techniques require some form of manual intervention. We examine some of the principles involved in shaping and present a new algorithm for automatic transferral of knowledge, which uses the Q-values established in a previous task to guide exploration in the learning of a new task. This algorithm is applied to two different but related robot navigation tasks.
5584278
Reinforcement Learning Task Clustering
2252135
Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of disambiguating information. We evaluate WSD using selectional preferences acquired for English adjectivenoun, subject, and direct object grammatical relationships with respect to a standard test corpus. The selectional preferences are specific to verb or adjective classes, rather than individual word forms, so they can be used to disambiguate the co-occurring adjectives and verbs, rather than just the nominal argument heads. We also investigate use of the one-senseper-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage. Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage. In addition to quantifying performance, we analyze the results to investigate the situations in which the selectional preferences achieve the best precision and in which the one-sense-per-discourse heuristic increases performance.
1044865
Finding Predominant Word Senses in Untagged Text
In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of hand-tagged data. Whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. We present work on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically. The acquired predominant senses give a precision of 64% on the nouns of the SENSEVAL-2 English all-words task. This is a very promising result given that our method does not require any hand-tagged text, such as SemCor. Furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domain-specific corpora.
16127213
Supporting interactive collaboration on the Web with CORK
Abstract The World Wide Web has served as a medium for collaboration since its inception. Web-based collaboration has, however, been dominated by systems supporting asynchronous activities such as sharing documents and participating in discussion forums. Supporting interactive, synchronous collaboration on the Web has proven much more challenging. In this paper we describe three of the challenges encountered in the context of supporting network-based collaboration among middle and high school science students: integrating synchronous and asynchronous modes of interaction, minimizing consumption of bandwidth, and adapting non-collaborative software components for collaborative use. We then present the Content Object Replication Kit, a toolkit for building interactive Java-based collaborative systems for use on the Web.
2269827
Training wheels in a user interface
New users of high-function application systems can become frustrated and confused by the errors they make in the early stages of learning. A training interface for a commercial word processor was designed to make typical and troublesome error states “unreachable,” thus eliminating the sources of some new-user learning problems. Creating a training environment from the basic function of the system itself afforded substantially faster learning coupled with better learning achievement and better performance on a comprehension post-test. A control group spent almost a quarter of their time recovering from the error states that the training interface blocked off. We speculate on how this training strategy might be refined, and more generally, on how function should be organized in a user interface.
1970300
Acting out the future: a process for envisionment
It is difficult to design innovative information and communication technologies to meet needs that will emerge from future interactions between users, technologies and their situations of use. We have been using scenarios that are acted out as a way of envisioning the future. This paper describes some of the core dilemmas of innovative design. It draws on our existing work as well as ideas from a range of disciplines to present a ‘light’ but useful envisionment process that may be applied to commercial design problems.