Dissecting Representations

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

doi:10.1007/978-3-030-54249-8_11

Cited by 4 publications

(3 citation statements)

References 10 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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“…At the core of rep2rep are the notions of RSs, which characterise classes of representations, RS-descriptions, which encode important features of RSs, and Q-descriptions 2 that abstractly encode the representation of a problem that is being solved. The requirements for RSs, RS-and Q-descriptions, and an informal presentation of them, were first given in [19,20]. We extend that work by precisely specifying RSs, Q-and RS-descriptions.…”

Section: Alternative Rep 3 (Represented Using the Contingency Table Rsmentioning

confidence: 99%

How to (Re)represent it?

Raggi

Stockdill

Jamnik

et al. 2020

2020 IEEE 32nd International Conference on Tools With Artificial Intelligence (ICTAI)

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Choosing an effective representation is fundamental to the ability of the representation's user to exploit it for the intended purpose. The major contribution of this paper is to provide a novel, flexible framework, rep2rep, that can be used by AI systems to recommend effective representations. What makes an effective representation is determined by whether it expresses the necessary information, supports the execution of tasks, and reflects the user's cognitive abilities. In general, there is no single 'most effective' representation for every problem and every user, which makes it difficult to choose one from the plethora of possible representations. To address this, rep2rep includes: a domain-independent language for describing representations, algorithms that compute measures of informational suitability and overall cognitive cost, and uses these measures to recommend representations. We demonstrate the application of rep2rep in the probability domain. Importantly, our framework provides the foundations for personalised interaction with AI systems in the context of representation choice.

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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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Dissecting Representations

Cited by 4 publications

References 10 publications

Representational Interpretive Structure: Theory and Notation

Representational Interpretive Structure: Theory and Notation

How to (Re)represent it?

Human-Like Computational Reasoning: Diagrams and Other Representations

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