18 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
Transition Systems for Model Generators - A Unifying Approach
A fundamental task for propositional logic is to compute models of
propositional formulas. Programs developed for this task are called
satisfiability solvers. We show that transition systems introduced by
Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers
can be adapted for solvers developed for two other propositional formalisms:
logic programming under the answer-set semantics, and the logic PC(ID). We show
that in each case the task of computing models can be seen as "satisfiability
modulo answer-set programming," where the goal is to find a model of a theory
that also is an answer set of a certain program. The unifying perspective we
develop shows, in particular, that solvers CLASP and MINISATID are closely
related despite being developed for different formalisms, one for answer-set
programming and the latter for the logic PC(ID).Comment: 30 pages; Accepted for presentation at ICLP 2011 and for publication
in Theory and Practice of Logic Programming; contains the appendix with
proof
Experiments with SAT-based Answer Set Programming
Answer Set Programming (ASP) emerged in the late 1990s as a new logic programming paradigm which has been successfully applied in various application domains. Propositional satisfiability (SAT) is one of the most studied problems in Computer Science. ASP and SAT are closely related: Recent works have studied their relation, and efficient SAT-based ASP solvers (like assat and Cmodels) exist. In this paper we report about (i) the extension of the basic procedures in Cmodels in order to incorporate the most popular SAT reasoning strategies, and (ii) an extensive comparative analysis involving also other state-of-the-art answer set solvers. The experimental analysis points out, besides the fact that Cmodels is highly competitive, that the reasoning strategies that work best on “small but hard” problems are ineffective on “big but easy” problems and vice-versa
Abstract Answer Set Solvers
Nieuwenhuis, Oliveras, and Tinelli showed how to describe enhancements of the Davis-Putnam-Logemann-Loveland algorithm using transition systems, instead of pseudocode. We design a similar framework for three algorithms that generate answer sets for logic programs: SMODELS, ASP-SAT with Backtracking, and a newly designed and implemented algorithm SUP. This approach to describing answer set solvers makes it easier to prove their correctness, to compare them, and to design new systems
Abstract Answer Set Solvers with Backjumping and Learning
Nieuwenhuis et al. (2006. Solving SAT and SAT modulo theories: From an abstract Davis-Putnam-Logemann-Loveland procedure to DPLL(T). Journal of the ACM 53(6), 937977 showed how to describe enhancements of the Davis–Putnam–Logemann–Loveland algorithm using transition systems, instead of pseudocode. We design a similar framework for several algorithms that generate answer sets for logic programs: SMODELS, SMODELScc, asp-sat with Learning (CMODELS), and a newly designed and implemented algorithm sup. This approach to describe answer set solvers makes it easier to prove their correctness, to compare them, and to design new systems