NDLI: Markov constraints: steerable generation of Markov sequences

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Markov constraints: steerable generation of Markov sequences

Content Provider	Springer Nature Link
Author	Pachet, François Roy, Pierre
Copyright Year	2010
Abstract	Markov chains are a well known tool to model temporal properties of many phenomena, from text structure to fluctuations in economics. Because they are easy to generate, Markovian sequences, i.e. temporal sequences having the Markov property, are also used for content generation applications such as text or music generation that imitate a given style. However, Markov sequences are traditionally generated using greedy, left-to-right algorithms. While this approach is computationally cheap, it is fundamentally unsuited for interactive control. This paper addresses the issue of generating steerable Markovian sequences. We target interactive applications such as games, in which users want to control, through simple input devices, the way the system generates a Markovian sequence, such as a text, a musical sequence or a drawing. To this aim, we propose to revisit Markov sequence generation as a branch and bound constraint satisfaction problem (CSP). We propose a CSP formulation of the basic Markovian hypothesis as elementary Markov Constraints (EMC). We propose algorithms that achieve domain-consistency for the propagators of EMCs, in an event-based implementation of CSP. We show how EMCs can be combined to estimate the global Markovian probability of a whole sequence, and accommodate for different species of Markov generation such as fixed order, variable-order, or smoothing. Such a formulation, although more costly than traditional greedy generation algorithms, yields the immense advantage of being naturally steerable, since control specifications can be represented by arbitrary additional constraints, without any modification of the generation algorithm. We illustrate our approach on simple yet combinatorial chord sequence and melody generation problems and give some performance results.
Starting Page	148
Ending Page	172
Page Count	25
File Format	PDF
ISSN	13837133
Journal	Constraints
Volume Number	16
Issue Number	2
e-ISSN	15729354
Language	English
Publisher	Springer US
Publisher Date	2010-09-18
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	CSP Markov chains Sequence constraints Global constraints Interactive applications Operations Research/Decision Theory Computing Methodologies Optimization Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Discrete Mathematics and Combinatorics Artificial Intelligence Computational Theory and Mathematics Software

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