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  1.  2
    Matej Urbas, Mateja Jamnik & Gem Stapleton (2015). Speedith: A Reasoner for Spider Diagrams. Journal of Logic, Language and Information 24 (4):487-540.
    In this paper, we introduce Speedith which is an interactive diagrammatic theorem prover for the well-known language of spider diagrams. Speedith provides a way to input spider diagrams, transform them via the diagrammatic inference rules, and prove diagrammatic theorems. Speedith’s inference rules are sound and complete, extending previous research by including all the classical logic connectives. In addition to being a stand-alone proof system, Speedith is also designed as a program that plugs into existing general purpose theorem provers. This allows (...)
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  2.  9
    Mateja Jamnik, Alan Bundy & Ian Green (1999). On Automating Diagrammatic Proofs of Arithmetic Arguments. Journal of Logic, Language and Information 8 (3):297-321.
    Theorems in automated theorem proving are usually proved by formal logical proofs. However, there is a subset of problems which humans can prove by the use of geometric operations on diagrams, so called diagrammatic proofs. Insight is often more clearly perceived in these proofs than in the corresponding algebraic proofs; they capture an intuitive notion of truthfulness that humans find easy to see and understand. We are investigating and automating such diagrammatic reasoning about mathematical theorems. Concrete, rather than general diagrams (...)
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  3.  2
    Daniel Winterstein, Alan Bundy & Mateja Jamnik (2004). On Differences Between the Real and Physical Plane. In A. Blackwell, K. Marriott & A. Shimojima (eds.), Diagrammatic Representation and Inference. Springer 29--31.
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  4. Mateja Jamnik, Manfred Kerber, Martin Pollet & Christoph Benzmüller (2003). Automatic Learning of Proof Methods in Proof Planning. Logic Journal of the Igpl 11 (6):647-673.
    In this paper we present an approach to automated learning within mathematical reasoning systems. In particular, the approach enables proof planning systems to automatically learn new proof methods from well-chosen examples of proofs which use a similar reasoning pattern to prove related theorems. Our approach consists of an abstract representation for methods and a machine learning technique which can learn methods using this representation formalism. We present an implementation of the approach within the ΩMEGA proof planning system, which we call (...)
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