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Machine Creativity Research @ Edinburgh

Machine Creativity Research @ 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

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Machine Creativity Research @ Edinburgh

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  1. Machine Creativity Research @ Edinburgh Simon Colton Universities of Edinburgh and York

  2. Overview • Players • Research • Contacts • Possibilities

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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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)

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

  10. 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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