Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus

Oliver Ray

doi:10.1007/978-3-030-97454-1_16

Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus

Oliver Ray

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

Abstract

This paper presents the first automatic method for learning and revising dynamic temporal theories in the full-fledged Discrete Event Calculus (DEC), where fluents may be temporarily released from the law of inertia and subject to qualitative or quantitative domain laws. This is done by proposing a reformulation of the DEC, called the eXploratory Event Calculus (XEC), which can be more efficiently handled by state-of-the-art answer set solvers, and which supports a range of different logical semantics and policy options for resolving conflicts relating to the truth value or release status of fluents. The paper shows how XEC outperforms DEC on standard reasoning benchmarks, and how it can be used with an ILP system XHAIL to provide the first proof-of-principle demonstration of theory learning and revision in the full-featured DEC.

Original language	English
Title of host publication	Inductive Logic Programming
Subtitle of host publication	30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings
Publisher	Springer
Pages	219-233
Number of pages	15
Volume	131191
ISBN (Electronic)	978-3-030-97454-1
ISBN (Print)	978-3-030-97453-4
DOIs	https://doi.org/10.1007/978-3-030-97454-1_16
Publication status	Published - 24 Feb 2022
Event	30th International Conference on Inductive Logic Programming - Online Duration: 25 Oct 2021 → … http://lr2020.iit.demokritos.gr/ilp/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Inductive Logic Programming
Abbreviated title	ILP21
Period	25/10/21 → …
Internet address	http://lr2020.iit.demokritos.gr/ilp/

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Theory revision
Answer set programming
Event calculus

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10.1007/978-3-030-97454-1_16Licence: Unspecified

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Cite this

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title = "Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus",

abstract = "This paper presents the first automatic method for learning and revising dynamic temporal theories in the full-fledged Discrete Event Calculus (DEC), where fluents may be temporarily released from the law of inertia and subject to qualitative or quantitative domain laws. This is done by proposing a reformulation of the DEC, called the eXploratory Event Calculus (XEC), which can be more efficiently handled by state-of-the-art answer set solvers, and which supports a range of different logical semantics and policy options for resolving conflicts relating to the truth value or release status of fluents. The paper shows how XEC outperforms DEC on standard reasoning benchmarks, and how it can be used with an ILP system XHAIL to provide the first proof-of-principle demonstration of theory learning and revision in the full-featured DEC.",

keywords = "Theory revision, Answer set programming, Event calculus",

author = "Oliver Ray",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 30th International Conference on Inductive Logic Programming, ILP21 ; Conference date: 25-10-2021",

year = "2022",

month = feb,

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language = "English",

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volume = "131191",

series = "Lecture Notes in Computer Science",

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Ray, O 2022, Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus. in Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings. vol. 131191, Lecture Notes in Computer Science, Springer, pp. 219-233, 30th International Conference on Inductive Logic Programming, 25/10/21. https://doi.org/10.1007/978-3-030-97454-1_16

Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus. / Ray, Oliver.
Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings. Vol. 131191 Springer, 2022. p. 219-233 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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T1 - Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus

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N2 - This paper presents the first automatic method for learning and revising dynamic temporal theories in the full-fledged Discrete Event Calculus (DEC), where fluents may be temporarily released from the law of inertia and subject to qualitative or quantitative domain laws. This is done by proposing a reformulation of the DEC, called the eXploratory Event Calculus (XEC), which can be more efficiently handled by state-of-the-art answer set solvers, and which supports a range of different logical semantics and policy options for resolving conflicts relating to the truth value or release status of fluents. The paper shows how XEC outperforms DEC on standard reasoning benchmarks, and how it can be used with an ILP system XHAIL to provide the first proof-of-principle demonstration of theory learning and revision in the full-featured DEC.

AB - This paper presents the first automatic method for learning and revising dynamic temporal theories in the full-fledged Discrete Event Calculus (DEC), where fluents may be temporarily released from the law of inertia and subject to qualitative or quantitative domain laws. This is done by proposing a reformulation of the DEC, called the eXploratory Event Calculus (XEC), which can be more efficiently handled by state-of-the-art answer set solvers, and which supports a range of different logical semantics and policy options for resolving conflicts relating to the truth value or release status of fluents. The paper shows how XEC outperforms DEC on standard reasoning benchmarks, and how it can be used with an ILP system XHAIL to provide the first proof-of-principle demonstration of theory learning and revision in the full-featured DEC.

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KW - Answer set programming

KW - Event calculus

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ER -

Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus

Abstract

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Keywords

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