Machine Creativity Edinburgh Simon Colton Universities of Edinburgh and York.

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Presentation transcript:

Machine Creativity Edinburgh Simon Colton Universities of Edinburgh and York

Overview Players Research Contacts Possibilities

Creativity Researchers Graeme Ritchie Literary creativity, assessment of creativity Simon Colton Scientific theory formation Alison Pease Cognitive modelling Alan Bundy? Roy McCasland?

Graeme Ritchie Literary/Linguistic creativity Computational humour With Kim Binsted: JAPE joke generator See Binsted PhD, AISB’00 paper Assessment of creative programs Take into account the inspiring set Fine tuning, creative set (with Pease & Colton) Shotgun approach See AISB’01 paper, ICCBR’01 workshop paper

Simon Colton The HR program Overview Scientific theory formation Implemented in the HR program Starts with ML-style background info Invents concepts (definitions and examples) Makes, proves, disproves hypotheses Used in mathematical domains Integrates with ATP, CAS, CSP, Databases Applied to mathematical discovery

The Application of HR Number theory Invention of integer sequences & theorems Constraint invention (with Ian Miguel) Speed up CSPs, 10x for QG4-quasigroups ATP (with Geoff Sutcliffe) Lemma generation, theorems to break provers Puzzle generation Study of machine creativity Cross-domain, meta-theory, multi-agent, interestingness

HR for Bioinformatics HR is now independent of maths Theory extends to other sciences E.g., making of empirically false hypotheses Multi-agent approach for large datasets Machine learning problems Concept identification: forward look-ahead Prediction: uses the whole theory Very preliminary Application to ML datasets Comparison of methods next

Alison Pease Phd proposal: A computational model of mathematical creativity via Interaction Using HR to perform cognitive modelling Multi-agent setting (see IAT paper) Lakatos-style reasoning Fixing faulty hypotheses (see ECAI paper) Conjecture-driven concept formation Implications for creativity Fit into Boden’s framework (see ICCBR’01 paper)

Contacts Edinburgh UK national centre for E-science (GRID) Bioinformatics group York Machine learning group Imperial Bioinformatics group (Muggleton)

Possibilities Problem with Large Datasets Multi-agent creativity (split data) Domain knowledge Cognitive Modelling HR applied to Bioinformatics Serious Case Study (Roy McCasland) EPSRC 1-year fellowship (fingers crossed) Using HR to Study Zariski Spaces

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