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Pacific Rim International Conference on Artificial Intelligence

PRICAI 1996: Learning and Reasoning with Complex Representations pp 234–247Cite as

Modelling inertia in action languages

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  • Reasoning with Changing and Incomplete Information
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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1359))

Abstract

Logic-based approaches to reasoning about actions, change and causality, highlight efficient representation and processing of domain background knowledge as an important task. Action theories recently developed in the framework of action languages with inertia and ramifications [20,14] not only adopt the principle of minimal change reinforced with the policy of categorisation (assigning different degrees of inertia to language elements) but also try to incorporate background causal knowledge. In this paper we aim to trace the evolution of action languages and to explore interactions between ontological characteristics of action domains such as inertia and causality. Such an analysis should clarify how possible solutions to the frame and the ramification problems axe affected by applying the policy of categorisation to causal domains. We first attempt to identify conditions (more precisely, restrictions) which preserve the meaning of domain descriptions when moving among various analysed languages. Relaxing such restrictions can help in evaluating the role of the frame concept (and policy of categorisation, in general) in an action language with fluent-triggered causality.

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Author information

Authors and Affiliations

  1. CSIRO Mathematical and Information Sciences, Locked Bag 17, 2113, North Ryde, NSW, Australia

    Mikhail Prokopenko

  2. Knowledge Systems Group Department of Computing, School of MPCE, Macquarie University, 2109, NSW, Australia

    Pavlos Peppas

Authors
  1. Mikhail Prokopenko
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  2. Pavlos Peppas
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Editor information

Grigoris Antoniou Aditya K. Ghose Mirosław Truszczyński

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© 1998 Springer-Verlag Berlin Heidelberg

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Prokopenko, M., Peppas, P. (1998). Modelling inertia in action languages. In: Antoniou, G., Ghose, A.K., Truszczyński, M. (eds) Learning and Reasoning with Complex Representations. PRICAI 1996. Lecture Notes in Computer Science, vol 1359. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-64413-X_39

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  • DOI: https://doi.org/10.1007/3-540-64413-X_39

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64413-2

  • Online ISBN: 978-3-540-69780-0

  • eBook Packages: Springer Book Archive

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