Ontology type: schema:ScholarlyArticle Open Access: True
2021-07-27
AUTHORS ABSTRACTThis paper develops a logical theory that unifies all three standard types of argumentative attack in AI, namely rebutting, undercutting and undermining attacks. We build on default justification logic that already represents undercutting and rebutting attacks, and we add undermining attacks. Intuitively, undermining does not target default inference, as undercutting, or default conclusion, as rebutting, but rather attacks an argument’s premise as a starting point for default reasoning. In default justification logic, reasoning starts from a set of premises, which is then extended by conclusions that hold by default. We argue that modeling undermining defeaters in the view of default theories requires changing the set of premises upon receiving new information. To model changes to premises, we give a dynamic aspect to default justification logic by using the techniques from the logic of belief revision. More specifically, undermining is modeled with belief revision operations that include contracting a set of premises, that is, removing some information from it. The novel combination of default reasoning and belief revision in justification logic enriches both approaches to reasoning under uncertainty. By the end of the paper, we show some important aspects of defeasible argumentation in which our logic compares favorably to structured argumentation frameworks. More... »
PAGES1-41
http://scigraph.springernature.com/pub.10.1007/s10472-021-09765-z
DOIhttp://dx.doi.org/10.1007/s10472-021-09765-z
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1139981294
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Mathematical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0102",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Applied Mathematics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0802",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Computation Theory and Mathematics",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Theoretical Philosophy Group, Department of Philosophy and Religious Studies, Utrecht University, Utrecht, The Netherlands",
"id": "http://www.grid.ac/institutes/grid.5477.1",
"name": [
"Theoretical Philosophy Group, Department of Philosophy and Religious Studies, Utrecht University, Utrecht, The Netherlands"
],
"type": "Organization"
},
"familyName": "Pand\u017ei\u0107",
"givenName": "Stipe",
"id": "sg:person.016443076325.43",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016443076325.43"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/bfb0018455",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040212503",
"https://doi.org/10.1007/bfb0018455"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-45062-7_36",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025424641",
"https://doi.org/10.1007/978-3-540-45062-7_36"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11098-018-1142-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1105564645",
"https://doi.org/10.1007/s11098-018-1142-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-0-387-98197-0_11",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036198723",
"https://doi.org/10.1007/978-0-387-98197-0_11"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10472-009-9112-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036838626",
"https://doi.org/10.1007/s10472-009-9112-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-030-39951-1_14",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1124373006",
"https://doi.org/10.1007/978-3-030-39951-1_14"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11229-011-9968-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045436839",
"https://doi.org/10.1007/s11229-011-9968-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bfb0018432",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007433882",
"https://doi.org/10.1007/bfb0018432"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bfb0018421",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024859622",
"https://doi.org/10.1007/bfb0018421"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-0-387-98197-0_10",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002732163",
"https://doi.org/10.1007/978-0-387-98197-0_10"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-981-13-7791-4_8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1119761757",
"https://doi.org/10.1007/978-981-13-7791-4_8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-32621-9_13",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038949557",
"https://doi.org/10.1007/978-3-642-32621-9_13"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-63045-7_27",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012984465",
"https://doi.org/10.1007/3-540-63045-7_27"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-90-481-9473-5_11",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009646364",
"https://doi.org/10.1007/978-90-481-9473-5_11"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-94-007-0814-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023990749",
"https://doi.org/10.1007/978-94-007-0814-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1005534223776",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005353029",
"https://doi.org/10.1023/a:1005534223776"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-40381-1_12",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044394369",
"https://doi.org/10.1007/978-3-642-40381-1_12"
],
"type": "CreativeWork"
}
],
"datePublished": "2021-07-27",
"datePublishedReg": "2021-07-27",
"description": "This paper develops a logical theory that unifies all three standard types of argumentative attack in AI, namely rebutting, undercutting and undermining attacks. We build on default justification logic that already represents undercutting and rebutting attacks, and we add undermining attacks. Intuitively, undermining does not target default inference, as undercutting, or default conclusion, as rebutting, but rather attacks an argument\u2019s premise as a starting point for default reasoning. In default justification logic, reasoning starts from a set of premises, which is then extended by conclusions that hold by default. We argue that modeling undermining defeaters in the view of default theories requires changing the set of premises upon receiving new information. To model changes to premises, we give a dynamic aspect to default justification logic by using the techniques from the logic of belief revision. More specifically, undermining is modeled with belief revision operations that include contracting a set of premises, that is, removing some information from it. The novel combination of default reasoning and belief revision in justification logic enriches both approaches to reasoning under uncertainty. By the end of the paper, we show some important aspects of defeasible argumentation in which our logic compares favorably to structured argumentation frameworks.",
"genre": "article",
"id": "sg:pub.10.1007/s10472-021-09765-z",
"inLanguage": "en",
"isAccessibleForFree": true,
"isPartOf": [
{
"id": "sg:journal.1043955",
"issn": [
"1012-2443",
"1573-7470"
],
"name": "Annals of Mathematics and Artificial Intelligence",
"publisher": "Springer Nature",
"type": "Periodical"
}
],
"keywords": [
"set of premises",
"justification logic",
"belief revision",
"default reasoning",
"structured argumentation frameworks",
"logical theory",
"argument\u2019s premises",
"defeasible argumentation",
"logic",
"default theories",
"reasoning",
"default inferences",
"premise",
"argumentation framework",
"starting point",
"undermining",
"defeaters",
"theory",
"rebutting",
"argumentation",
"important aspect",
"view",
"aspects",
"revision",
"enriches",
"AI",
"attacks",
"framework",
"paper",
"point",
"inference",
"end",
"conclusion",
"default",
"dynamic aspects",
"argumentation dynamics",
"standard type",
"default conclusions",
"belief revision operation",
"set",
"revision operations",
"new information",
"information",
"novel combination",
"uncertainty",
"operation",
"dynamics",
"changes",
"technique",
"types",
"combination",
"argumentative attack",
"undermining attacks",
"default justification logic",
"justification logic enriches",
"logic enriches",
"Structured argumentation dynamics"
],
"name": "Structured argumentation dynamics",
"pagination": "1-41",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1139981294"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s10472-021-09765-z"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s10472-021-09765-z",
"https://app.dimensions.ai/details/publication/pub.1139981294"
],
"sdDataset": "articles",
"sdDatePublished": "2022-01-01T19:02",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_910.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s10472-021-09765-z"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10472-021-09765-z'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10472-021-09765-z'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10472-021-09765-z'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10472-021-09765-z'
This table displays all metadata directly associated to this object as RDF triples.
189 TRIPLES
22 PREDICATES
99 URIs
71 LITERALS
4 BLANK NODES