5,791 research outputs found
Automatic Music Composition using Answer Set Programming
Music composition used to be a pen and paper activity. These these days music
is often composed with the aid of computer software, even to the point where
the computer compose parts of the score autonomously. The composition of most
styles of music is governed by rules. We show that by approaching the
automation, analysis and verification of composition as a knowledge
representation task and formalising these rules in a suitable logical language,
powerful and expressive intelligent composition tools can be easily built. This
application paper describes the use of answer set programming to construct an
automated system, named ANTON, that can compose melodic, harmonic and rhythmic
music, diagnose errors in human compositions and serve as a computer-aided
composition tool. The combination of harmonic, rhythmic and melodic composition
in a single framework makes ANTON unique in the growing area of algorithmic
composition. With near real-time composition, ANTON reaches the point where it
can not only be used as a component in an interactive composition tool but also
has the potential for live performances and concerts or automatically generated
background music in a variety of applications. With the use of a fully
declarative language and an "off-the-shelf" reasoning engine, ANTON provides
the human composer a tool which is significantly simpler, more compact and more
versatile than other existing systems. This paper has been accepted for
publication in Theory and Practice of Logic Programming (TPLP).Comment: 31 pages, 10 figures. Extended version of our ICLP2008 paper.
Formatted following TPLP guideline
OptEEmAL: Decision-Support Tool for the Design of Energy Retrofitting Projects at District Level
Designing energy retrofitting actions poses an elevated number of problems, as the definition of the baseline, selection of indicators to measure performance, modelling, setting objectives, etc. This is time-consuming and it can result in a number of inaccuracies, leading to inadequate decisions. While these problems are present at building level, they are multiplied at district level, where there are complex interactions to analyse, simulate and improve. OptEEmAL proposes a solution as a decision-support tool for the design of energy retrofitting projects at district level. Based on specific input data (IFC(s), CityGML, etc.), the platform will automatically simulate the baseline scenario and launch an optimisation process where a series of Energy Conservation Measures (ECMs) will be applied to this scenario. Its performance will be evaluated through a holistic set of indicators to obtain the best combination of ECMs that complies with user's objectives. A great reduction in time and higher accuracy in the models are experienced, since they are automatically created and checked. A subjective problem is transformed into a mathematical problem; it simplifies it and ensures a more robust decision-making. This paper will present a case where the platform has been tested.This research work has been partially funded by the European Commission though the European Unionâs Horizon 2020 Research and Innovation Programme under grant agreement No 680676. All related information to the project is available at https://www.opteemal-project.eu
Interpretable multiclass classification by MDL-based rule lists
Interpretable classifiers have recently witnessed an increase in attention
from the data mining community because they are inherently easier to understand
and explain than their more complex counterparts. Examples of interpretable
classification models include decision trees, rule sets, and rule lists.
Learning such models often involves optimizing hyperparameters, which typically
requires substantial amounts of data and may result in relatively large models.
In this paper, we consider the problem of learning compact yet accurate
probabilistic rule lists for multiclass classification. Specifically, we
propose a novel formalization based on probabilistic rule lists and the minimum
description length (MDL) principle. This results in virtually parameter-free
model selection that naturally allows to trade-off model complexity with
goodness of fit, by which overfitting and the need for hyperparameter tuning
are effectively avoided. Finally, we introduce the Classy algorithm, which
greedily finds rule lists according to the proposed criterion. We empirically
demonstrate that Classy selects small probabilistic rule lists that outperform
state-of-the-art classifiers when it comes to the combination of predictive
performance and interpretability. We show that Classy is insensitive to its
only parameter, i.e., the candidate set, and that compression on the training
set correlates with classification performance, validating our MDL-based
selection criterion
Exploring probabilistic grammars of symbolic music using PRISM
In this paper we describe how we used the logic-based probabilistic
programming language PRISM to conduct a systematic comparison
of several probabilistic models of symbolic music, including 0th and
1st order Markov models over pitches and intervals, and a probabilistic
grammar with two parameterisations. Using PRISM allows us to take
advantage of variational Bayesian methods for assessing the goodness of
fit of the models. When applied to a corpus of Bach chorales and the Essen
folk song collection, we found that, depending on various parameters, the
probabilistic grammars sometimes but not always out-perform the simple
Markov models. Examining how the models perform on smaller subsets
of pieces, we find that the simpler Markov models do out-perform the
best grammar-based model at the small end of the scale
Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies
An automatic word classification system has been designed which processes
word unigram and bigram frequency statistics extracted from a corpus of natural
language utterances. The system implements a binary top-down form of word
clustering which employs an average class mutual information metric. Resulting
classifications are hierarchical, allowing variable class granularity. Words
are represented as structural tags --- unique -bit numbers the most
significant bit-patterns of which incorporate class information. Access to a
structural tag immediately provides access to all classification levels for the
corresponding word. The classification system has successfully revealed some of
the structure of English, from the phonemic to the semantic level. The system
has been compared --- directly and indirectly --- with other recent word
classification systems. Class based interpolated language models have been
constructed to exploit the extra information supplied by the classifications
and some experiments have shown that the new models improve model performance.Comment: 17 Page Paper. Self-extracting PostScript Fil
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