A Planning-based Approach for Music Composition

. Automatic music composition is a fascinating field within computational creativity. While different Artificial Intelligence techniques have been used for tackling this task, Planning – an approach for solving complex combinatorial problems which can count on a large number of high-performance systems and an expressive language for describing problems – has never been exploited. In this paper, we propose two different techniques that rely on automated planning for generating musical structures. The structures are then filled from the bottom with “raw” musical materials, and turned into melodies. Music experts evaluated the creative output of the system, acknowledging an overall human-enjoyable trait of the melodies produced, which showed a solid hierarchical structure and a strong musical directionality. The techniques proposed not only have high relevance for the musical domain, but also suggest unexplored ways of using planning for dealing with non-deterministic creative domains

The Effect of Repetition and Expertise on Liking and Complexity in Contemporary Music

Aesthetic perception of music has been extensively researched in the last decades. Numerous studies suggest that listeners find a piece of music more or less pleasant according to its complexity. Experimental results show that complexity and liking have different relationship according to the musical genre examined, and that these two variables are also affected by other factors such as familiarity to the music and expertise of the listener. Although previous experiments have examined several genres such as jazz, pop, rock and bluegrass, surprisingly, no study has focused on contemporary music. In this paper, we fill this gap by studying the relationships between complexity, liking, musical training and familiarity in the case of contemporary music. By analysing this genre – which is usually underrepresented in music cognition – it is possible to shed some light on the correlation between liking and complexity in the case of highly complex music. To obtain data, a multifactor experiment was designed in which both music experts and novices had to provide scores of subjective complexity and liking for four 30-second long excerpts of contemporary music with different degrees of complexity. Empirical results suggest that liking and complexity are negatively correlated in the case of contemporary music and that listeners’ expertise does not influence the perceived complexity of musical pieces, but it can significantly affect liking. This possibly indicates that experts have the musical knowledge needed to appreciate extremely complex music, while novices do not

Towards a Protocol for Benchmark Selection in IPC

The planning competition has traditionally played an important role in motivating research and advances in Planning & Scheduling techniques. Despite its pivotal role in the planning community, some aspects of the competition have not been engineered yet. This is the case for the protocol for selecting benchmark instances. Benchmarks are of critical importance, since they can significantly affect competition results. In this paper we describe desirable properties of a selection protocol, discuss methods exploited in past SAT and planning competitions, and identify challenges that organisers of future competitions have to address in order to improve reliability and usefulness of the insights gained by looking at competitions’ results

Challenges of Portfolio-based Planning

In the recent years the field of automated planing has significantly advanced and several powerful domain-independent planners have been developed. However, none of these systems clearly outperforms all the others in every known benchmark domain. This observation motivated the idea of configuring and exploiting a portfolio of planners to achieve better performances than any individual planner: some recent planning systems based on this idea obtained significantly good results in experimental analysis and International Planning Competitions. Such results lead us to think that future challenges for the automated planning community will converge on designing different approaches for combining existing planning algorithms. This paper focuses on the challenges and open issues of existing approaches and highlights the possible future evolution of these techniques. In addition the paper introduces algorithm portfolios, reviews existing techniques, and describes the decisions that have to be taken during the configuration

On the Online Generation of Effective Macro-operators

Macro-operator (“macro”, for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training

Issues in Planning Domain Model Engineering

The paper raises some issues relating to the engineering of domain models for automated planning. It studies the idea of a domain model as a formal specification of a domain, and considers properties of that specification. It proposes some definitions, which the planning and, more generally, the artificial intelligence community needs to consider, in order to properly deal with engineering issues in domain model creation

An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings--planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans