How to (Re)represent it?

Raggi, Daniel; Stockdill, Aaron; Jamnik, Mateja; Garcia, Grecia Garcia; Cheng, Peter

doi:10.1109/ictai50040.2020.00185

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Finally, we note that this research was conducted as part of a wider project that is developing automated systems for the selection of representations for individual problem solvers with varying levels of competence on different classes of problems [13], [14], [15]. One aspect of the project is to devise a measure of the cognitive cost of representations [5], which can be used to assess the relative difficulty a user will likely experience with alternative representations.…”

Section: Discussionmentioning

confidence: 99%

Representational Interpretive Structure: Theory and Notation

Cheng

Stockdill

Garcia

et al. 2022

Diagrammatic Representation and Inference

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A cognitive theory of the interpretive structure of visual representations (RIST) was proposed by Cheng (2020), which identified four classes of schemas that specify how domain concepts are encoded by graphical objects. A notation (RISN) for building RIST models as networks of these schemas was also introduced. This paper introduces common RIST/RISN network structures -idioms -that occur across varied representations. A small-scale experiment is presented in which three participants successfully modelled their own interpretation of three diverse representations using RIST/RISN and idioms.

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Section: Discussionmentioning

confidence: 99%

Representational Interpretive Structure: Theory and Notation

Cheng

Stockdill

Garcia

et al. 2022

Diagrammatic Representation and Inference

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“…We previously published the medical problem representations in [22]. In that prior work, we presented a much earlier version of the framework presented here.…”

Section: Choices Of Tokens: Motivation For Changementioning

confidence: 99%

“…Compatibility Construction spaces are compatible if their union is a construction space. We require that the three spaces over which a representational system is formed are compatible 22 . Section 4.3.1 establishes that for any set of pairwise compatible construction spaces, their union is a construction space.…”

Section: Properties Of Construction Spacesmentioning

confidence: 99%

Representational Systems Theory: A Unified Approach to Encoding, Analysing and Transforming Representations

Raggi¹,

Jamnik²,

Stockdill³

et al. 2022

Preprint

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The study of representations is of fundamental importance to any form of communication, and our ability to exploit them effectively is paramount. This article presents a novel theory -Representational Systems Theory -that is designed to abstractly encode a wide variety of representations from three core perspectives: syntax, entailment, and their properties. By introducing the concept of a construction space, we are able to encode each of these core components under a single, unifying paradigm. Using our Representational Systems Theory, it becomes possible to structurally transform representations in one system into representations in another. An intrinsic facet of our structural transformation technique is representation selection based on properties that representations possess, such as their relative cognitive effectiveness or structural complexity. A major theoretical barrier to providing general structural transformation techniques is a lack of terminating algorithms. Representational Systems Theory permits the derivation of partial transformations when no terminating algorithm can produce a full transformation. Since Representational Systems Theory provides a universal approach to encoding representational systems, a further key barrier is eliminated: the need to devise system-specific structural transformation algorithms, that are necessary when different systems adopt different formalisation approaches. Consequently, Representational Systems Theory is the first general framework that provides a unified approach to encoding representations, supports representation selection via structural transformations, and has the potential for widespread practical application.

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“…We are combining artificial intelligence, mathematics and cognitive science to investigate human cognitive abilities to find representations that suitably match problems, and the process by which humans adapt or switch between representations. We are devising a foundational theory and building computational models of the critical role that representations play in problem and automating them in a new generation of adaptive AI systems [32,30,39,31].…”

Section: How To Choose a Representationmentioning

confidence: 99%

Human-Like Computational Reasoning: Diagrams and Other Representations

Jamnik¹

2021

Mathematical Reasoning: The History and Impact of the DReaM Group

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In this chapter I give a personal account of my experience in Alan Bundy's DReaM group in the Department of Artificial Intelligence at the University of Edinburgh between the years of 1995 and 1998. Of course, the impact of this experience has been profound and long-lasting to this day. The culture and the nature of research work, the collaborations, the interests and the connections have endured, evolved and multiplied throughout this time. My own work in the DReaM group started by investigating human "informal" reasoning and formalising it in a diagrammatic theorem prover. After leaving Edinburgh, this work naturally evolved into combining diagrams with other representations in a uniform framework, as well as applying visual representations in other domains, such as reasoning with ontologies. But one of the fundamental questions remained unanswered, namely, how do we choose the right representation of a problem and for a particular user in the first place?1 The DReaM research environment Few factors influence a researcher's ethos regarding their work more than where and with whom they did their PhD project. I arrived to Alan Bundy's DReaM research group in the autumn of 1995, fresh from finishing a post-graduate Diploma in Computer Science at Cambridge. This was not exactly planned: I actually applied to do a PhD in the Cognitive Science Department at the University of Edinburgh. I was interested in humans, not machines. But given that I was a mathematician by my undergraduate degree and that I just finished a post-graduate degree in Computer Science, my application made it to Alan Bundy in the Department of Artificial Intelligence. I am so glad for this serendipity, because the privilege has been immeasurable.

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How to (Re)represent it?

Cited by 4 publications

References 24 publications

Representational Interpretive Structure: Theory and Notation

Representational Interpretive Structure: Theory and Notation

Representational Systems Theory: A Unified Approach to Encoding, Analysing and Transforming Representations

Human-Like Computational Reasoning: Diagrams and Other Representations

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