7,040 research outputs found
Reason Maintenance - State of the Art
This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques
A beginner's guide to belief revision and truth maintenance systems
This brief note is intended to familiarize the non-TMS audience with some of the basic ideas surrounding classic TMS's (truth maintenance systems), namely the justification-based TMS and the assumption-based TMS. Topics of further interest include the relation between non-monotonic logics and TMS's, efficiency and search issues, complexity concerns, as well as the variety of TMS systems that have surfaced in the past decade or so. These include probabilistic-based TMS systems, fuzzy TMS systems, tri-valued belief systems, and so on
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
In many recommendation applications such as news recommendation, the items
that can be rec- ommended come and go at a very fast pace. This is a challenge
for recommender systems (RS) to face this setting. Online learning algorithms
seem to be the most straight forward solution. The contextual bandit framework
was introduced for that very purpose. In general the evaluation of a RS is a
critical issue. Live evaluation is of- ten avoided due to the potential loss of
revenue, hence the need for offline evaluation methods. Two options are
available. Model based meth- ods are biased by nature and are thus difficult to
trust when used alone. Data driven methods are therefore what we consider here.
Evaluat- ing online learning algorithms with past data is not simple but some
methods exist in the litera- ture. Nonetheless their accuracy is not satisfac-
tory mainly due to their mechanism of data re- jection that only allow the
exploitation of a small fraction of the data. We precisely address this issue
in this paper. After highlighting the limita- tions of the previous methods, we
present a new method, based on bootstrapping techniques. This new method comes
with two important improve- ments: it is much more accurate and it provides a
measure of quality of its estimation. The latter is a highly desirable property
in order to minimize the risks entailed by putting online a RS for the first
time. We provide both theoretical and ex- perimental proofs of its superiority
compared to state-of-the-art methods, as well as an analysis of the convergence
of the measure of quality
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