Jamie Forth

IDyOT: A Computational Theory of Creativity as Everyday Reasoning from Learned Information

We present progress towards a computational cognitive architecture, IDyOT (Information Dynamics of Thinking) that is intended to account for certain aspects of human creativity and other forms of cognitive processing in terms of a pre-conscious predictive loop. The theory is motivated in terms of the evolutionary pressure to be efficient. It makes several predictions that may be tested by building computational implementations and studying their behaviour.

Unifying Conceptual Spaces: Concept Formation in Musical Creative Systems

We examine Gärdenfors’ theory of conceptual spaces, a geometrical form of knowledge representation (Conceptual spaces: The geometry of thought, MIT Press, Cambridge, 2000), in the context of the general Creative Systems Framework introduced by Wiggins (J Knowl Based Syst 19(7):449–458, 2006a; New Generation Comput 24(3):209–222, 2006b). Gärdenfors’ theory offers a way of bridging the traditional divide between symbolic and sub-symbolic representations, as well as the gap between representational formalism and meaning as perceived by human minds. We discuss how both these qualities may be advantageous from the point of view of artificial creative systems. We take music as our example domain, and discuss how a range of musical qualities may be instantiated as conceptual spaces, and present a detailed conceptual space formalisation of musical metre.

Entraining IDyOT: Timing in the Information Dynamics of Thinking

We present a novel hypothetical account of entrainment in music and language, in context of the Information Dynamics of Thinking model, IDyOT. The extended model affords an alternative view of entrainment, and its companion term, pulse, from earlier accounts. The model is based on hierarchical, statistical prediction, modeling expectations of both what an event will be and when it will happen. As such, it constitutes a kind of predictive coding, with a particular novel hypothetical implementation. Here, we focus on the models mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic. We propose a range of tests to validate or falsify the model, at various different levels of abstraction, and argue that computational modeling in general, and this model in particular, can offer a means of providing limited but useful evidence for evolutionary hypotheses.

SerenA: A Multi-site Pervasive Agent Environment That Supports Serendipitous Discovery in Research

We present SerenA, a multi-site, pervasive, agent environment that suppers serendipitous discovery in research. The project starts from the premise that human users cannot be aware of all the research information that is relevant to their work, because of the compartmentalisation of research into fields around particular journals, and, simply, because there is too much to know. In particular, the Semantic Web provides a resource which can assist, but there is more to be discovered than the things that a user might deliberately search for. SerenA, then, attempts to assist researchers by presenting them with information that they did not know they needed to know about their research.

Linking Neural and Symbolic Representation and Processing of Conceptual Structures

We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.

Towards a Deep Improviser: a prototype deep learning post-tonal free music generator

Two modest-sized symbolic corpora of post-tonal and post-metrical keyboard music have been constructed: one algorithmic and the other improvised. Deep learning models of each have been trained. The purpose was to obtain models with sufficient generalisation capacity that in response to separate fresh input seed material, they can generate outputs that are statistically distinctive, neither random nor recreative of the learned corpora or the seed material. This objective has been achieved, as judged by k-sample Anderson–Darling and Cramer tests. Music has been generated using the approach, and preliminary informal judgements place it roughly on a par with an example of composed music in a related form. Future work will aim to enhance the model such that it deserves to be fully evaluated in relation to expression, meaning and utility in real-time performance.

Demonstrating serena: Chance encounters in the space of ideas

We demonstrate SerenA, a multi-site, pervasive, agent environment that suppers serendipitous discovery in research. SerenA attempts to assist researchers by presenting them with information that they did not know they needed to know about their research.