Ontology type: schema:Chapter
2018-08-23
AUTHORSKiana Zeighami , Kevin Leo , Guido Tack , Maria Garcia de la Banda
ABSTRACTRecently, [16] showed that the nogoods inferred by learning solvers can be used to improve a problem model, by detecting constraints that can be strengthened and new redundant constraints. However, the detection process was manual and required in-depth knowledge of both the learning solver and the model transformations performed by the compiler. In this paper we provide the first steps towards a (largely) automatic detection process. In particular, we discuss how nogoods can be automatically simplified, connected back to the constraints in the model, and grouped into more general “patterns” for which common facts might be found. These patterns are easier to understand and provide stronger evidence of the importance of particular constraints. We also show how nogoods generated by different search strategies and problem instances can increase our confidence in the usefulness of these patterns. Finally, we identify significant challenges and avenues for future research. More... »
PAGES403-419
Principles and Practice of Constraint Programming
ISBN
978-3-319-98333-2
978-3-319-98334-9
http://scigraph.springernature.com/pub.10.1007/978-3-319-98334-9_27
DOIhttp://dx.doi.org/10.1007/978-3-319-98334-9_27
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1106284581
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/2008/08",
"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/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Faculty of IT, Monash University, Melbourne, Australia",
"id": "http://www.grid.ac/institutes/grid.1002.3",
"name": [
"Faculty of IT, Monash University, Melbourne, Australia"
],
"type": "Organization"
},
"familyName": "Zeighami",
"givenName": "Kiana",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Faculty of IT, Monash University, Melbourne, Australia",
"id": "http://www.grid.ac/institutes/grid.1002.3",
"name": [
"Faculty of IT, Monash University, Melbourne, Australia"
],
"type": "Organization"
},
"familyName": "Leo",
"givenName": "Kevin",
"id": "sg:person.01303145667.62",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01303145667.62"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Data61/CSIRO, Melbourne, Australia",
"id": "http://www.grid.ac/institutes/None",
"name": [
"Faculty of IT, Monash University, Melbourne, Australia",
"Data61/CSIRO, Melbourne, Australia"
],
"type": "Organization"
},
"familyName": "Tack",
"givenName": "Guido",
"id": "sg:person.01235032467.07",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235032467.07"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Data61/CSIRO, Melbourne, Australia",
"id": "http://www.grid.ac/institutes/None",
"name": [
"Faculty of IT, Monash University, Melbourne, Australia",
"Data61/CSIRO, Melbourne, Australia"
],
"type": "Organization"
},
"familyName": "de la Banda",
"givenName": "Maria Garcia",
"id": "sg:person.016350443307.93",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016350443307.93"
],
"type": "Person"
}
],
"datePublished": "2018-08-23",
"datePublishedReg": "2018-08-23",
"description": "Recently,\u00a0[16] showed that the nogoods inferred by learning solvers can be used to improve a problem model, by detecting constraints that can be strengthened and new redundant constraints. However, the detection process was manual and required in-depth knowledge of both the learning solver and the model transformations performed by the compiler. In this paper we provide the first steps towards a (largely) automatic detection process. In particular, we discuss how nogoods can be automatically simplified, connected back to the constraints in the model, and grouped into more general \u201cpatterns\u201d for which common facts might be found. These patterns are easier to understand and provide stronger evidence of the importance of particular constraints. We also show how nogoods generated by different search strategies and problem instances can increase our confidence in the usefulness of these patterns. Finally, we identify significant challenges and avenues for future research.",
"editor": [
{
"familyName": "Hooker",
"givenName": "John",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-319-98334-9_27",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-319-98333-2",
"978-3-319-98334-9"
],
"name": "Principles and Practice of Constraint Programming",
"type": "Book"
},
"keywords": [
"model transformation",
"detection process",
"automatic detection process",
"semi-automatic learning",
"different search strategies",
"problem instances",
"nogoods",
"problem model",
"redundant constraints",
"search strategy",
"particular constraints",
"common fact",
"constraints",
"significant challenge",
"solver",
"compiler",
"first step",
"depth knowledge",
"learning",
"instances",
"model",
"challenges",
"process",
"knowledge",
"step",
"transformation",
"usefulness",
"confidence",
"future research",
"research",
"patterns",
"strategies",
"fact",
"importance",
"avenues",
"strong evidence",
"evidence",
"paper"
],
"name": "Towards Semi-Automatic Learning-Based Model Transformation",
"pagination": "403-419",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1106284581"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-319-98334-9_27"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-319-98334-9_27",
"https://app.dimensions.ai/details/publication/pub.1106284581"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-05-20T07:42",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/chapter/chapter_152.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/978-3-319-98334-9_27"
}
]
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.10.1007/978-3-319-98334-9_27'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-98334-9_27'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-98334-9_27'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-98334-9_27'
This table displays all metadata directly associated to this object as RDF triples.
122 TRIPLES
23 PREDICATES
63 URIs
56 LITERALS
7 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/978-3-319-98334-9_27 | schema:about | anzsrc-for:08 |
2 | ″ | ″ | anzsrc-for:0801 |
3 | ″ | schema:author | N4ad7b7eaebbe441589aa9b7c7660f809 |
4 | ″ | schema:datePublished | 2018-08-23 |
5 | ″ | schema:datePublishedReg | 2018-08-23 |
6 | ″ | schema:description | Recently, [16] showed that the nogoods inferred by learning solvers can be used to improve a problem model, by detecting constraints that can be strengthened and new redundant constraints. However, the detection process was manual and required in-depth knowledge of both the learning solver and the model transformations performed by the compiler. In this paper we provide the first steps towards a (largely) automatic detection process. In particular, we discuss how nogoods can be automatically simplified, connected back to the constraints in the model, and grouped into more general “patterns” for which common facts might be found. These patterns are easier to understand and provide stronger evidence of the importance of particular constraints. We also show how nogoods generated by different search strategies and problem instances can increase our confidence in the usefulness of these patterns. Finally, we identify significant challenges and avenues for future research. |
7 | ″ | schema:editor | N1bb0eac885a24201ab557a76f5c7e221 |
8 | ″ | schema:genre | chapter |
9 | ″ | schema:inLanguage | en |
10 | ″ | schema:isAccessibleForFree | false |
11 | ″ | schema:isPartOf | N1f06739887384e43b1a608de81b02fcd |
12 | ″ | schema:keywords | automatic detection process |
13 | ″ | ″ | avenues |
14 | ″ | ″ | challenges |
15 | ″ | ″ | common fact |
16 | ″ | ″ | compiler |
17 | ″ | ″ | confidence |
18 | ″ | ″ | constraints |
19 | ″ | ″ | depth knowledge |
20 | ″ | ″ | detection process |
21 | ″ | ″ | different search strategies |
22 | ″ | ″ | evidence |
23 | ″ | ″ | fact |
24 | ″ | ″ | first step |
25 | ″ | ″ | future research |
26 | ″ | ″ | importance |
27 | ″ | ″ | instances |
28 | ″ | ″ | knowledge |
29 | ″ | ″ | learning |
30 | ″ | ″ | model |
31 | ″ | ″ | model transformation |
32 | ″ | ″ | nogoods |
33 | ″ | ″ | paper |
34 | ″ | ″ | particular constraints |
35 | ″ | ″ | patterns |
36 | ″ | ″ | problem instances |
37 | ″ | ″ | problem model |
38 | ″ | ″ | process |
39 | ″ | ″ | redundant constraints |
40 | ″ | ″ | research |
41 | ″ | ″ | search strategy |
42 | ″ | ″ | semi-automatic learning |
43 | ″ | ″ | significant challenge |
44 | ″ | ″ | solver |
45 | ″ | ″ | step |
46 | ″ | ″ | strategies |
47 | ″ | ″ | strong evidence |
48 | ″ | ″ | transformation |
49 | ″ | ″ | usefulness |
50 | ″ | schema:name | Towards Semi-Automatic Learning-Based Model Transformation |
51 | ″ | schema:pagination | 403-419 |
52 | ″ | schema:productId | Naf2b9abe0e734b269cfade2e98a0cbda |
53 | ″ | ″ | Nd5f673e8e74c4d898b38e4f9460240ae |
54 | ″ | schema:publisher | Nd0c3c3f5efbe4ed986cdef9d56d66de4 |
55 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1106284581 |
56 | ″ | ″ | https://doi.org/10.1007/978-3-319-98334-9_27 |
57 | ″ | schema:sdDatePublished | 2022-05-20T07:42 |
58 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
59 | ″ | schema:sdPublisher | N008eb26e1e6c4ae58902c51d635664c1 |
60 | ″ | schema:url | https://doi.org/10.1007/978-3-319-98334-9_27 |
61 | ″ | sgo:license | sg:explorer/license/ |
62 | ″ | sgo:sdDataset | chapters |
63 | ″ | rdf:type | schema:Chapter |
64 | N008eb26e1e6c4ae58902c51d635664c1 | schema:name | Springer Nature - SN SciGraph project |
65 | ″ | rdf:type | schema:Organization |
66 | N03af3709e0af466fa6e1b46ebbeb6c25 | rdf:first | sg:person.016350443307.93 |
67 | ″ | rdf:rest | rdf:nil |
68 | N1bb0eac885a24201ab557a76f5c7e221 | rdf:first | Neab17122e9284b91b391067b3a3653b2 |
69 | ″ | rdf:rest | rdf:nil |
70 | N1f06739887384e43b1a608de81b02fcd | schema:isbn | 978-3-319-98333-2 |
71 | ″ | ″ | 978-3-319-98334-9 |
72 | ″ | schema:name | Principles and Practice of Constraint Programming |
73 | ″ | rdf:type | schema:Book |
74 | N42cdc97aad2644f7b94ec0c03d943c31 | rdf:first | sg:person.01303145667.62 |
75 | ″ | rdf:rest | N4b69fd4767a745feb203f6a9746df090 |
76 | N4a31fcd0f7d847ff81fb5c1da6fbd9b1 | schema:affiliation | grid-institutes:grid.1002.3 |
77 | ″ | schema:familyName | Zeighami |
78 | ″ | schema:givenName | Kiana |
79 | ″ | rdf:type | schema:Person |
80 | N4ad7b7eaebbe441589aa9b7c7660f809 | rdf:first | N4a31fcd0f7d847ff81fb5c1da6fbd9b1 |
81 | ″ | rdf:rest | N42cdc97aad2644f7b94ec0c03d943c31 |
82 | N4b69fd4767a745feb203f6a9746df090 | rdf:first | sg:person.01235032467.07 |
83 | ″ | rdf:rest | N03af3709e0af466fa6e1b46ebbeb6c25 |
84 | Naf2b9abe0e734b269cfade2e98a0cbda | schema:name | doi |
85 | ″ | schema:value | 10.1007/978-3-319-98334-9_27 |
86 | ″ | rdf:type | schema:PropertyValue |
87 | Nd0c3c3f5efbe4ed986cdef9d56d66de4 | schema:name | Springer Nature |
88 | ″ | rdf:type | schema:Organisation |
89 | Nd5f673e8e74c4d898b38e4f9460240ae | schema:name | dimensions_id |
90 | ″ | schema:value | pub.1106284581 |
91 | ″ | rdf:type | schema:PropertyValue |
92 | Neab17122e9284b91b391067b3a3653b2 | schema:familyName | Hooker |
93 | ″ | schema:givenName | John |
94 | ″ | rdf:type | schema:Person |
95 | anzsrc-for:08 | schema:inDefinedTermSet | anzsrc-for: |
96 | ″ | schema:name | Information and Computing Sciences |
97 | ″ | rdf:type | schema:DefinedTerm |
98 | anzsrc-for:0801 | schema:inDefinedTermSet | anzsrc-for: |
99 | ″ | schema:name | Artificial Intelligence and Image Processing |
100 | ″ | rdf:type | schema:DefinedTerm |
101 | sg:person.01235032467.07 | schema:affiliation | grid-institutes:None |
102 | ″ | schema:familyName | Tack |
103 | ″ | schema:givenName | Guido |
104 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235032467.07 |
105 | ″ | rdf:type | schema:Person |
106 | sg:person.01303145667.62 | schema:affiliation | grid-institutes:grid.1002.3 |
107 | ″ | schema:familyName | Leo |
108 | ″ | schema:givenName | Kevin |
109 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01303145667.62 |
110 | ″ | rdf:type | schema:Person |
111 | sg:person.016350443307.93 | schema:affiliation | grid-institutes:None |
112 | ″ | schema:familyName | de la Banda |
113 | ″ | schema:givenName | Maria Garcia |
114 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016350443307.93 |
115 | ″ | rdf:type | schema:Person |
116 | grid-institutes:None | schema:alternateName | Data61/CSIRO, Melbourne, Australia |
117 | ″ | schema:name | Data61/CSIRO, Melbourne, Australia |
118 | ″ | ″ | Faculty of IT, Monash University, Melbourne, Australia |
119 | ″ | rdf:type | schema:Organization |
120 | grid-institutes:grid.1002.3 | schema:alternateName | Faculty of IT, Monash University, Melbourne, Australia |
121 | ″ | schema:name | Faculty of IT, Monash University, Melbourne, Australia |
122 | ″ | rdf:type | schema:Organization |