this paper we present a novel domain for user modeling and intelligent agents -- agents as specific users' improvisational music companions (IMCs). We also introduce Band-OUT-of-a-Box (BoB), an agent designed to trade live, customized solos with a specific improvising musician/user

The use of Evolutionary Computation approaches to create images has reached a great popularity, leading to the appearance of a new art form -- Evolutionary Art -- and to the proliferation of Evolutionary Art Tools. In this paper we present and make an assessment of one of these tools: NEvAr. We also systematise and describe the work methodology currently used to generate images. When working with NEvAr we focus on the reuse of useful individual, which we store in an image database. The size of this database, and the importance of its role, led us to the development of automatic seeding procedures, which we also describe.

We outline a framework within which machine compositions may be evaluated objectively. In particular, the framework allows statements about those compositions to be refuted on the basis of empirical experimentation. We consider this to be fundamental if we wish to evaluate the degree to which our programs achieve their compositional aims. Furthermore, a review of the literature reveals that this is a largely ignored aspect of research into algorithmic composition. Our framework involves four components: specifying the compositional aims; inducing a critic from a set of example musical phrases; composing music that satisfies the critic; and evaluating specific claims about the compositions in experiments using human subjects. We describe a system which exemplifies these four stages and which demonstrates the practicality of the framework. Finally, the application of the framework to the evaluation of musical creativity is discussed and directions for future research are suggested.

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In this paper we propose a framework for the development of artificial art critics, consisting of an architecture and a validation methodology. The architecture includes two modules: a feature extractor, which performs a pre-processing of the artwork, extracting several measurements and characteristics; and an evaluator, which, based on the output of the feature extractor, classifies the artwork according to a certain criteria. The validation methodology comprises several stages, ranging from author and style discrimination to the integration of the artificial art critic in a dynamic multi-agent environment, which includes human agents. Finally, we present experimental results obtained by the implementation of the proposed framework. 1

Creativity is one of the most remarkable characteristics of the human mind. It is thus natural that Artificial Intelligence's research groups have been working towards the study and proposal of adequate computational models to creativity. Artificial creative systems are potentially effective in a wide range of artistic, architectural and engineering domains where detailed problem specification is virtually impossible and, therefore, conventional problem solving is unlikely to produce useful solutions. Moreover their study may contribute to the overall understanding of the mechanisms behind human creativity.

We present a corpus-based hybrid approach to music analysis and composition, which incorporates statistical, connectionist, and evolutionary components. Our framework employs artificial music critics, which may be trained on large music corpora, and then pass aesthetic judgment on music artifacts. Music artifacts are generated by an evolutionary music composer, which utilizes music critics as fitness functions. To evaluate this approach we conducted three experiments. First, using music features based on Zipf’s law, we trained artificial neural networks to predict the popularity of 992 musical pieces with 87.85% accuracy. Then, assuming that popularity correlates with aesthetics, we incorporated such neural networks into a genetic-programming system, called NEvMuse. NEvMuse autonomously “composed ” novel variations of J.S. Bach’s Invention #13 in A minor (BWV 784), variations which many listeners found to be aesthetically pleasing. Finally, we compared aesthetic judgments from an artificial music critic with emotional responses from 23 human subjects. Significant correlations were found. We provide evaluation results and samples of generated music. These results have implications for music information retrieval and computeraided music composition.

Constraint programming is well suited for the computational modeling of music theories and composition: its declarative and modular approach shares similarities with the way music theory is traditionally expressed, namely by a set of rules which describe the intended result. Various music theory disciplines have been modeled, including counterpoint, harmony, rhythm, form, and instrumentation. Because modeling music theories “from scratch ” is a complex task, generic music constraint programming systems have been proposed that predefine the required building blocks for modeling a range of music theories. After introducing the field and its problems in general, this survey compares these generic systems according to a number of criteria such as the range of music theories these systems support.

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Abstract: We describe our approach for the automatic composition of monophone melodies on a user given chord sequence. For this purpose we use evolutionary algorithms for the music generation with automatically learned classifiers next to interactive evaluation as fitness functions. Key–Words: computational intelligence, music, composition, classification, data mining 1