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Revising Defeasible Theories via Instructions

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Rules and Reasoning (RuleML+RR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15183))

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Abstract

Progress in AI raises agent alignment problems. In this paper, we look at the problem of instructing an agent, i.e. informing it about a regularity in the world it did not previously know. We study an idealized case: agents reasoning with logical theories. The idealization helps to understand the space of possibilities of the problem, and illustrates potential pitfalls and solutions. We believe non-monotonic theories more plausibly approximate human practical and commonsense reasoning so our agents here also use non-monotonic inference. However, instructing a non-monotonic theory does not always result in better alignment. One main cause of this phenomenon is humans omitting the kind of information used by a non-monotonic inference system to resolve conflicts between its parts. We illustrate this with theories induced from a dataset consisting of situated objects. We argue that obtaining non-monotonic theories that respond better to instruction requires additional restrictions on the formalism and theory update procedure.

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Notes

  1. 1.

    Named after the authors: Alchourròn, Gärdenfors and Makinson.

  2. 2.

    Cut and Cautious Monotony have been put forth by Gabbay [5] as desirable properties if a non-monotonic formal system is to be regarded a logic. This proposal has since entrenched itself.

  3. 3.

    Code for rerunning the experiments: https://github.com/mpomarlan/ruleml2024,

  4. 4.

    We can stop induction once the number of rules is the same as in HeRO theories; this does not change our results regarding backslides, convergence etc.

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Author information

Authors and Affiliations

  1. Applied Linguistics Department, University of Bremen, Bremen, Germany

    Mihai Pomarlan

  2. Jönköping Artificial Intelligence Laboratory, Jönköping University, Jönköping, Sweden

    Maria M. Hedblom

  3. Digital Media Lab, University of Bremen, Bremen, Germany

    Laura Spillner & Robert Porzel

Authors
  1. Mihai Pomarlan
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  2. Maria M. Hedblom
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  3. Laura Spillner
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  4. Robert Porzel
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Correspondence to Mihai Pomarlan .

Editor information

Editors and Affiliations

  1. Vienna University of Economics and Business, Vienna, Austria

    Sabrina Kirrane

  2. TU Wien, Vienna, Austria

    Mantas Šimkus

  3. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Ahmet Soylu

  4. SINTEF AS/OsloMet – Oslo Metropolitan University, Oslo, Norway

    Dumitru Roman

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Pomarlan, M., Hedblom, M.M., Spillner, L., Porzel, R. (2024). Revising Defeasible Theories via Instructions. In: Kirrane, S., Šimkus, M., Soylu, A., Roman, D. (eds) Rules and Reasoning. RuleML+RR 2024. Lecture Notes in Computer Science, vol 15183. Springer, Cham. https://doi.org/10.1007/978-3-031-72407-7_13

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  • DOI: https://doi.org/10.1007/978-3-031-72407-7_13

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  • Online ISBN: 978-3-031-72407-7

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