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

Justifying Answer Sets using Argumentation

An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal is or is not contained in a given answer set by defining two justification methods, both of which make use of the correspondence between answer sets of a logic program and stable extensions of the Assumption-Based Argumentation (ABA) framework constructed from the same logic program. Attack Trees justify a literal in argumentation-theoretic terms, i.e. using arguments and attacks between them, whereas ABA-Based Answer Set Justifications express the same justification structure in logic programming terms, that is using literals and their relationships. Interestingly, an ABA-Based Answer Set Justification corresponds to an admissible fragment of the answer set in question, and an Attack Tree corresponds to an admissible fragment of the stable extension corresponding to this answer set.Comment: This article has been accepted for publication in Theory and Practice of Logic Programmin

Hierarchical Style Modeling: A generative framework for Style-Centric Generation of 3D Models

This work focuses on incorporating style - specifically visual style - into procedural content generation processes. Specifically, I model style as a series of constraints that must be satisfied while an object is generated

Finding similar or diverse solutions in answer set programming: theory and applications

For many computational problems, the main concern is to find a best solution (e.g., a most preferred product configuration, a shortest plan, a most parsimonious phylogeny) with respect to some well-described criteria. On the other hand, in many real-world applications, computing a subset of good solutions that are similar/diverse may be desirable for better decision-making. For one reason, the given computational problem may have too many good solutions, and the user may want to examine only a few of them to pick one; in such cases, finding a few similar/diverse good solutions may be useful. Also, in many real-world applications the users usually take into account further criteria that are not included in the formulation of the optimization problem; in such cases, finding a few good solutions that are close to or distant from a particular set of solutions may be useful. With this motivation, we have studied various computational problems related to finding similar/diverse (resp. close/distant) solutions with respect to a given distance function, in the context of Answer Set Programming (ASP). We have introduced novel offline/online computational methods in ASP to solve such computational problems. We have modified an ASP solver according to one of our online methods, providing a useful tool (CLASP-NK) for various ASP applications. We have showed the applicability and effectiveness of our methods/tools in three domains: phylogeny reconstruction, AI planning, and biomedical query answering. Motivated by the promising results, we have developed computational tools to be used by the experts in these areas

Efficient reasoning procedures for complex first-order theories

The complexity of a set of first-order formulas results from the size of the set and the complexity of the problem described by its formulas. Decision Procedures for Ontologies This thesis presents new superposition based decision procedures for large sets of formulas. The sets of formulas may contain expressive constructs like transitivity and equality. The procedures decide the consistency of knowledge bases, called ontologies, that consist of several million formulas and answer complex queries with respect to these ontologies. They are the first superposition based reasoning procedures for ontologies that are at the same time efficient, sound, and complete. The procedures are evaluated using the well-known ontologies YAGO, SUMO and CYC. The results of the experiments, which are presented in this thesis, show that these procedures decide the consistency of all three above-mentioned ontologies and usually answer queries within a few seconds. Reductions for General Automated Theorem Proving Sophisticated reductions are important in order to obtain efficient reasoning procedures for complex, particularly undecidable problems because they restrict the search space of theorem proving procedures. In this thesis, I have developed a new powerful reduction rule. This rule enables superposition based reasoning procedures to find proofs in sets of complex formulas. In addition, it increases the number of problems for which superposition is a decision procedure.Die Komplexität einer Formelmenge für einen automatischen Theorembeweiser in Prädikatenlogik 1. Stufe ergibt sich aus der Anzahl der zu betrachtenden Formeln und aus der Komplexität des durch die Formeln beschriebenen Problems. Entscheidungsprozeduren für Ontologien Diese Arbeit entwickelt effiziente auf Superposition basierende Beweisprozeduren für sehr große entscheidbare Formelmengen, die ausdrucksstarke Konstrukte, wie Transitivität und Gleichheit, enthalten. Die Prozeduren ermöglichen es Wissenssammlungen, sogenannte Ontologien, die aus mehreren Millionen Formeln bestehen, auf Konsistenz hin zu überprüfen und Antworten auf komplizierte Anfragen zu berechnen. Diese Prozeduren sind die ersten auf Superposition basierten Beweisprozeduren für große, ausdrucksstarke Ontologien, die sowohl korrekt und vollständig, als auch effizient sind. Die entwickelten Prozeduren werden anhand der weit bekannten Ontologien YAGO, SUMO und CYC evaluiert. Die Experimente zeigen, dass diese Prozeduren die Konsistenz aller untersuchten Ontologien entscheiden und Anfragen in wenigen Sekunden beantworten. Reduktionen für allgemeines Theorembeweisen Um effiziente Prozeduren für das Beweisen in sehr schwierigen und insbesondere in unentscheidbaren Formelmengen zu erhalten, sind starke Reduktionsregeln, die den Beweisraum einschränken, von essentieller Bedeutung. Diese Arbeit entwickelt eine neue mächtige Reduktionsregel, die es Superposition ermöglicht Beweise in sehr schwierigen Formelmengen zu finden und erweitert die Menge von Problemen, für die Superposition eine Entscheidungsprozedur ist