We consider aspects pertinent to evaluating creativity to be input, output and the process by which the output is achieved. These issues

Creative products are generally recognised as satisfying two requirements: firstly they are useful, and secondly they are novel. Much effort in AI and design computing has been put into developing systems that can recognise the usefulness of the products that they generate. In contrast, the work presented in this thesis has concentrated on developing computational systems that are able to recognise the novelty of their work. The research has shown that when computational systems are given the ability to recognise both the novelty and the usefulness of their products they gain a level of autonomy that opens up new possibilities for the study of creative behaviour in single agents and the emergence of social creativity in multi-agent systems. The work

This paper addresses an aspect of social environments comprising a series of processes that begin with an artificial agent (the author-agent) producing surprising products (objects, events, etc.), continue with other artificial agents (the jury-agents ) appraising those products with respect to surprise (manifested, for instance, by the focus of their attention on those products), and end with a possible update of the emotional state of the author-agent by the elicitation of emotions such as happiness and pride, or sadness (depending on whether or not those products get the attention of those jury-agents ). We describe a model of surprise that is mainly rooted in the cognitivepsychoevolutionary model of surprise proposed by the research group of the University of Bielefeld (Meyer, Reinsenzein, Schutzwohl, etc.) and also in the ideas of Ortony and Partridge. We present an experimental test about the activity related to the creation and evaluation of surprising objects.

We consider computational creativity as a search process and give a No Free Lunch result for computational creativity in this context. That is, we show that there is no a priori “best ” creative strategy. We discuss some implications of this result and suggest some additional questions to be explored.

We argue that taking surprise into account in the artificial agents’s reasoning may have advantageous implications in various situations. Relying on theoretical and empirical evidence, our arguments are supported by the application of surprise-based agents to three different domains, namely exploration of unknown environments, divergent production and evaluation of creative products, and selective attention to travel information. Categories and Subject Descriptors

Music and speech are two realms predominately species-specific to humans, and many human creative endeavors involve these two modalities. The pairing of music and spoken text can heighten the emotional and cognitive impact of both- the complete song being much more compelling than either the lyrics or the accompaniment alone. This work describes a system that is able to automatically generate and evaluate musical accompaniments for a given set of lyrics. It derives the rhythm for the melodic accompaniment from the cadence of the text. Pitches are generated through the use of n-gram models constructed from melodies of songs with a similar style. This system is able to generate pleasing melodies that fit well with the text of the lyrics, often doing so at a level similar to that of human ability.