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    <titleInfo>
        <title>Automatic Generation of Situation Models for Plan Recognition Problems</title>
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    <name type="personal">
        <namePart type="given">Kristina</namePart>
        <namePart type="family">Yordanova</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>INCOMA Ltd.</publisher>
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    <abstract>Recent attempts at behaviour understanding through language grounding have shown that it is possible to automatically generate models for planning problems from textual instructions. One drawback of these approaches is that they either do not make use of the semantic structure behind the model elements identified in the text, or they manually incorporate a collection of concepts with semantic relationships between them. We call this collection of knowledge situation model. The situation model introduces additional context information to the model. It could also potentially reduce the complexity of the planning problem compared to models that do not use situation models. To address this problem, we propose an approach that automatically generates the situation model from textual instructions. The approach is able to identify various hierarchical, spatial, directional, and causal relations. We use the situation model to automatically generate planning problems in a PDDL notation and we show that the situation model reduces the complexity of the PDDL model in terms of number of operators and branching factor compared to planning models that do not make use of situation models.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_105</identifier>
    <part>
        <date>2017-09</date>
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            <start>823</start>
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%0 Conference Proceedings
%T Automatic Generation of Situation Models for Plan Recognition Problems
%A Yordanova, Kristina
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F yordanova-2017-automatic
%X Recent attempts at behaviour understanding through language grounding have shown that it is possible to automatically generate models for planning problems from textual instructions. One drawback of these approaches is that they either do not make use of the semantic structure behind the model elements identified in the text, or they manually incorporate a collection of concepts with semantic relationships between them. We call this collection of knowledge situation model. The situation model introduces additional context information to the model. It could also potentially reduce the complexity of the planning problem compared to models that do not use situation models. To address this problem, we propose an approach that automatically generates the situation model from textual instructions. The approach is able to identify various hierarchical, spatial, directional, and causal relations. We use the situation model to automatically generate planning problems in a PDDL notation and we show that the situation model reduces the complexity of the PDDL model in terms of number of operators and branching factor compared to planning models that do not make use of situation models.
%R 10.26615/978-954-452-049-6_105
%U https://doi.org/10.26615/978-954-452-049-6_105
%P 823-830

Markdown (Informal)

[Automatic Generation of Situation Models for Plan Recognition Problems](https://doi.org/10.26615/978-954-452-049-6_105) (Yordanova, RANLP 2017)

• Automatic Generation of Situation Models for Plan Recognition Problems (Yordanova, RANLP 2017)

ACL

• Kristina Yordanova. 2017. Automatic Generation of Situation Models for Plan Recognition Problems. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 823–830, Varna, Bulgaria. INCOMA Ltd..