The longer term value of creativity judgements in computational creativity

During research to develop the Standardised Procedure for Evaluating Creative Systems (SPECS) methodology for evaluat- ing the creativity of ‘creative’ systems, in 2011 an evaluation case study was carried out. The case study investigated how we can make a ‘snapshot’ decision, in a short space of time, on the creativity of systems in various domains. The systems to be evaluated were presented at the International Computational Creativity Conference in 2011. Evaluation was performed by people whose domain expertise ranges from expert to novice, depending on the system. The SPECS methodology was used for evaluation, and was compared to two other creativity evaluation methods (Ritchie’s criteria and Colton’s Creative Tripod) and to results from surveying people’s opinion on the creativity of the systems under investigation. Here, we revisit those results, considering them in the context of what these systems have contributed to computational creativity development. Five years on, we now have data on how influential these systems were within computational creativity, and to what extent the work in these systems has influenced further developments in computational creativity research. This paper investigates whether the evaluations of creativity of these systems have been helpful in predicting which systems will be more influential in computational creativity (as measured by paper citations and further development within later computational systems). While a direct correlation between evaluative results and longer term impact is not discovered (and perhaps too simplistic an aim, given the factors at play in determining research impact), some interesting alignments are noted between the 2011 results and the impact of papers five years on

Four PPPPerspectives on Computational Creativity

From what perspective should creativity of a system be considered? Are we interested in the creativity of the system’s out- put? The creativity of the system itself? Or of its creative processes? Creativity as measured by internal features or by external feedback? Traditionally within computational creativity the focus had been on the creativity of the system’s Products or of its Processes, though this focus has widened recently regarding the role of the audience or the field surrounding the creative system. In the wider creativity research community a broader take is prevalent: the creative Person is considered as well as the environment or Press within which the creative entity operates in. Here we have the Four Ps of creativity: Person, Product, Process and Press. This paper presents the Four Ps, explaining each of the Four Ps in the context of creativity research and how it relates to computational creativity. To illustrate how useful the Four Ps can be in taking a fuller perspective on creativity, the concepts of novelty and value explored from each of the Four P perspectives, uncovering aspects that may otherwise be overlooked. This paper argues that the broader view of creativity afforded by the Four Ps is vital in guiding us towards more encompassing and comprehensive computational investigations of creativity

Implementing feedback in creative systems : a workshop approach

One particular challenge in AI is the computational modelling and simulation of creativity. Feedback and learning from experience are key aspects of the creative process. Here we investigate how we could implement feedback in creative systems using a social model. From the field of creative writing we borrow the concept of a Writers Workshop as a model for learning through feedback. The Writers Workshop encourages examination, discussion and debates of a piece of creative work using a prescribed format of activities. We propose a computational model of the Writers Workshop as a roadmap for incorporation of feedback in artificial creativity systems. We argue that the Writers Workshop setting describes the anatomy of the creative process. We support our claim with a case study that describes how to implement the Writers Workshop model in a computational creativity system. We present this work using patterns other people can follow to implement similar designs in their own systems. We conclude by discussing the broader relevance of this model to other aspects of AI

Weaving creativity into the Semantic Web: a language-processing approach

This paper describes a novel language processing ap- proach to the analysis of creativity and the development of a machine-readable ontology of creativity. The ontol- ogy provides a conceptualisation of creativity in terms of a set of fourteen key components or building blocks and has application to research into the nature of cre- ativity in general and to the evaluation of creative prac- tice, in particular. We further argue that the provision of a machine readable conceptualisation of creativity pro- vides a small, but important step towards addressing the problem of automated evaluation, ’the Achilles’ heel of AI research on creativity’ (Boden 1999)

Gnome on the range: finding the hypertextual narratives in ancient wisdom texts

In this paper we present the Sharing Ancient WisdomS (SAWS) project. Working with wisdom texts, or gnomologia, the project aims to produce an enhanced digital scholarly edition of the collected manuscripts which both makes the Greek, Arabic and Spanish texts available and demonstrates the hypertexual nature of these texts. By positioning the texts as collections of sayings, of which a given manuscript only shows one narrative path, we demonstrate how a hypertextual approach allows us to explore alternate narrative paths within and across the texts and support researchers as they study the context, significance and transmission of the wisdoms within these works

Score following: An artificially intelligent musical accompanist

Score Following is the process by which a musician can be tracked through their performance of a piece, for the purpose of accompanying the musician with the appropriate notes. This tracking is done by following the progress of the musician through the score (written music) of the piece, using observations of the notes they are playing. Artificially intelligent musical accompaniment is where a human musician is accompanied by a computer musician. The computer musician is able to produce musical accompaniment that relates musically to the human performance. Hidden Markov Models (HMMs) are a stochastic modelling tool that can be used to represent real-world systems in a variety of domains. This project discusses how HMMs can be used in the domain of Score Following and describes the construction and evaluation of a score following system that uses HMMs to implement score following. It explores the hypothesis that using an HMM to represent a musical score is an efficient and practical way to implement score following, and that in particular this method is suitable for providing real-time accompaniment to a human performer. The score followers developed during this project are tested and compared against other score following systems and against human musicians. The resulting performances support the project hypothesis to a large extent