A context-aware convention formation framework for large-scale networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Mohammad Rashedul Hasan, Anita Raja, Ana Bazzan

ABSTRACT

In this article, we present a decentralized convention formation framework for creating social conventions within large multiagent convention spaces. We study the role of the topological characteristics of the network in forming conventions with an emphasis on scale-free topologies. We hypothesize that contextual knowledge encapsulated in the topology can help improve both the quality of the emergent convention and the speed of forming such a convention. We also investigate the influence of network diversity. While recent research on diversity indicates that it improves organizational productivity, we observe that not all diversity is equally useful and identify the necessary conditions to maximize the benefit of diversity. We validate our convention formation framework using a language coordination problem in which agents in a multiagent system construct a common lexicon in a decentralized fashion. Agent interactions are modeled using a language game where every agent repeatedly plays with its neighbors. Each agent stochastically updates its lexicon based on the utility values of the lexicons received from its immediate neighbors. We introduce a novel context-aware utility computation mechanism and equip the agents with the ability to reorganize their neighborhood based on this utility estimate to expedite the convention formation process. A key idea behind our approach is the ability of socially influential high-utility-lexicon agents to bias their neighbors towards accepting their lexicons. Extensive experimentation results indicate that our proposed solution is both effective (able to converge into a large majority convention state with more than 90% agents sharing a high-quality lexicon) and efficient (faster) as compared to state-of-the-art approaches for social conventions in large convention spaces. More... »

PAGES

1-34

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10458-018-9397-9

DOI

http://dx.doi.org/10.1007/s10458-018-9397-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107899625


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Nebraska\u2013Lincoln", 
          "id": "https://www.grid.ac/institutes/grid.24434.35", 
          "name": [
            "University of Nebraska-Lincoln, 68588, Lincoln, NE, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hasan", 
        "givenName": "Mohammad Rashedul", 
        "id": "sg:person.012567351734.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012567351734.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cooper Union", 
          "id": "https://www.grid.ac/institutes/grid.254672.0", 
          "name": [
            "The Cooper Union, 10003, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Raja", 
        "givenName": "Anita", 
        "id": "sg:person.01176306202.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176306202.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "PPGC/UFRGS, CP 15064, 91501-970, Porto Alegre, RS, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bazzan", 
        "givenName": "Ana", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/scientificamerican0714-36", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000576054", 
          "https://doi.org/10.1038/scientificamerican0714-36"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-21268-0_14", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001132244", 
          "https://doi.org/10.1007/978-3-642-21268-0_14"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/artl.1995.2.3.319", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003920767"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.71.066107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005066634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.71.066107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005066634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.85.056109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009427359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.85.056109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009427359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.66.021907", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009539472"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.66.021907", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009539472"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.286.5439.509", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010080128"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3233/mgs-2011-0167", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011748634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature04605", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017620878", 
          "https://doi.org/10.1038/nature04605"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature04605", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017620878", 
          "https://doi.org/10.1038/nature04605"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature04605", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017620878", 
          "https://doi.org/10.1038/nature04605"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.122653799", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018411012"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.77.011904", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021437599"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.77.011904", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021437599"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/359826a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030106977", 
          "https://doi.org/10.1038/359826a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11493402_13", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030362025", 
          "https://doi.org/10.1007/11493402_13"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11493402_13", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030362025", 
          "https://doi.org/10.1007/11493402_13"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0011976", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030639307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.97.258103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034007587"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.97.258103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034007587"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4471-4054-2_12", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034971246", 
          "https://doi.org/10.1007/978-1-4471-4054-2_12"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-93920-7_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036962552", 
          "https://doi.org/10.1007/978-3-540-93920-7_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-93920-7_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036962552", 
          "https://doi.org/10.1007/978-3-540-93920-7_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/30918", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041985305", 
          "https://doi.org/10.1038/30918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/30918", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041985305", 
          "https://doi.org/10.1038/30918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10458-013-9240-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044458129", 
          "https://doi.org/10.1007/s10458-013-9240-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10458-012-9193-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045393059", 
          "https://doi.org/10.1007/s10458-012-9193-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jtbi.1999.0981", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050803743"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0004-3702(02)00262-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052425993"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2396761.2396820", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053174729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.78.026117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060737808"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.78.026117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060737808"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mprv.2009.49", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061418533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/130914218", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062870600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.21236/ada325130", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091735743"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1209/0295-5075/119/30002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092383924"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/saso.2010.38", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093598744"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/greencom-cpscom.2010.122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093635226"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/infcom.2007.327", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094549975"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wi-iat.2013.130", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095436827"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wi-iat.2009.155", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095484295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511791383", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098667199"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/acprof:oso/9780199206650.001.0001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098762313"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/acprof:oso/9780198515906.001.0001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098793632"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1098907933", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13278-018-0490-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101081263", 
          "https://doi.org/10.1007/s13278-018-0490-5"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03", 
    "datePublishedReg": "2019-03-01", 
    "description": "In this article, we present a decentralized convention formation framework for creating social conventions within large multiagent convention spaces. We study the role of the topological characteristics of the network in forming conventions with an emphasis on scale-free topologies. We hypothesize that contextual knowledge encapsulated in the topology can help improve both the quality of the emergent convention and the speed of forming such a convention. We also investigate the influence of network diversity. While recent research on diversity indicates that it improves organizational productivity, we observe that not all diversity is equally useful and identify the necessary conditions to maximize the benefit of diversity. We validate our convention formation framework using a language coordination problem in which agents in a multiagent system construct a common lexicon in a decentralized fashion. Agent interactions are modeled using a language game where every agent repeatedly plays with its neighbors. Each agent stochastically updates its lexicon based on the utility values of the lexicons received from its immediate neighbors. We introduce a novel context-aware utility computation mechanism and equip the agents with the ability to reorganize their neighborhood based on this utility estimate to expedite the convention formation process. A key idea behind our approach is the ability of socially influential high-utility-lexicon agents to bias their neighbors towards accepting their lexicons. Extensive experimentation results indicate that our proposed solution is both effective (able to converge into a large majority convention state with more than 90% agents sharing a high-quality lexicon) and efficient (faster) as compared to state-of-the-art approaches for social conventions in large convention spaces.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10458-018-9397-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1030506", 
        "issn": [
          "1387-2532", 
          "1573-7454"
        ], 
        "name": "Autonomous Agents and Multi-Agent Systems", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1-2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "33"
      }
    ], 
    "name": "A context-aware convention formation framework for large-scale networks", 
    "pagination": "1-34", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2e0ed9fea57d7967e1ee6af108ad090503fbf44eb36b152551e2f782499609fa"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10458-018-9397-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1107899625"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10458-018-9397-9", 
      "https://app.dimensions.ai/details/publication/pub.1107899625"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:43", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000358_0000000358/records_127448_00000011.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10458-018-9397-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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/s10458-018-9397-9'

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/s10458-018-9397-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10458-018-9397-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10458-018-9397-9'


 

This table displays all metadata directly associated to this object as RDF triples.

203 TRIPLES      21 PREDICATES      65 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10458-018-9397-9 schema:about anzsrc-for:08
2 anzsrc-for:0806
3 schema:author Ne446582f1f374ad0a548da5b38e70d86
4 schema:citation sg:pub.10.1007/11493402_13
5 sg:pub.10.1007/978-1-4471-4054-2_12
6 sg:pub.10.1007/978-3-540-93920-7_2
7 sg:pub.10.1007/978-3-642-21268-0_14
8 sg:pub.10.1007/s10458-012-9193-x
9 sg:pub.10.1007/s10458-013-9240-2
10 sg:pub.10.1007/s13278-018-0490-5
11 sg:pub.10.1038/30918
12 sg:pub.10.1038/359826a0
13 sg:pub.10.1038/nature04605
14 sg:pub.10.1038/scientificamerican0714-36
15 https://app.dimensions.ai/details/publication/pub.1098907933
16 https://doi.org/10.1006/jtbi.1999.0981
17 https://doi.org/10.1016/s0004-3702(02)00262-x
18 https://doi.org/10.1017/cbo9780511791383
19 https://doi.org/10.1073/pnas.122653799
20 https://doi.org/10.1093/acprof:oso/9780198515906.001.0001
21 https://doi.org/10.1093/acprof:oso/9780199206650.001.0001
22 https://doi.org/10.1103/physreve.66.021907
23 https://doi.org/10.1103/physreve.71.066107
24 https://doi.org/10.1103/physreve.77.011904
25 https://doi.org/10.1103/physreve.78.026117
26 https://doi.org/10.1103/physreve.85.056109
27 https://doi.org/10.1103/physrevlett.97.258103
28 https://doi.org/10.1109/greencom-cpscom.2010.122
29 https://doi.org/10.1109/infcom.2007.327
30 https://doi.org/10.1109/mprv.2009.49
31 https://doi.org/10.1109/saso.2010.38
32 https://doi.org/10.1109/wi-iat.2009.155
33 https://doi.org/10.1109/wi-iat.2013.130
34 https://doi.org/10.1126/science.286.5439.509
35 https://doi.org/10.1137/130914218
36 https://doi.org/10.1145/2396761.2396820
37 https://doi.org/10.1162/artl.1995.2.3.319
38 https://doi.org/10.1209/0295-5075/119/30002
39 https://doi.org/10.1371/journal.pone.0011976
40 https://doi.org/10.21236/ada325130
41 https://doi.org/10.3233/mgs-2011-0167
42 schema:datePublished 2019-03
43 schema:datePublishedReg 2019-03-01
44 schema:description In this article, we present a decentralized convention formation framework for creating social conventions within large multiagent convention spaces. We study the role of the topological characteristics of the network in forming conventions with an emphasis on scale-free topologies. We hypothesize that contextual knowledge encapsulated in the topology can help improve both the quality of the emergent convention and the speed of forming such a convention. We also investigate the influence of network diversity. While recent research on diversity indicates that it improves organizational productivity, we observe that not all diversity is equally useful and identify the necessary conditions to maximize the benefit of diversity. We validate our convention formation framework using a language coordination problem in which agents in a multiagent system construct a common lexicon in a decentralized fashion. Agent interactions are modeled using a language game where every agent repeatedly plays with its neighbors. Each agent stochastically updates its lexicon based on the utility values of the lexicons received from its immediate neighbors. We introduce a novel context-aware utility computation mechanism and equip the agents with the ability to reorganize their neighborhood based on this utility estimate to expedite the convention formation process. A key idea behind our approach is the ability of socially influential high-utility-lexicon agents to bias their neighbors towards accepting their lexicons. Extensive experimentation results indicate that our proposed solution is both effective (able to converge into a large majority convention state with more than 90% agents sharing a high-quality lexicon) and efficient (faster) as compared to state-of-the-art approaches for social conventions in large convention spaces.
45 schema:genre research_article
46 schema:inLanguage en
47 schema:isAccessibleForFree false
48 schema:isPartOf N26ffe9e0c9e84c4d92654424923c42ce
49 N3aa8c17c10cc4081adf3f09ce91f0424
50 sg:journal.1030506
51 schema:name A context-aware convention formation framework for large-scale networks
52 schema:pagination 1-34
53 schema:productId N3b0c4cd09065491a807f97ddaff1d6c1
54 Na6f9cb61362c458e9281840895c75342
55 Ncad886c17739455496acc69d17a7b391
56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107899625
57 https://doi.org/10.1007/s10458-018-9397-9
58 schema:sdDatePublished 2019-04-11T11:43
59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
60 schema:sdPublisher N1279b02f58b1478c9041de8b8b128970
61 schema:url https://link.springer.com/10.1007%2Fs10458-018-9397-9
62 sgo:license sg:explorer/license/
63 sgo:sdDataset articles
64 rdf:type schema:ScholarlyArticle
65 N1279b02f58b1478c9041de8b8b128970 schema:name Springer Nature - SN SciGraph project
66 rdf:type schema:Organization
67 N26ffe9e0c9e84c4d92654424923c42ce schema:volumeNumber 33
68 rdf:type schema:PublicationVolume
69 N3aa8c17c10cc4081adf3f09ce91f0424 schema:issueNumber 1-2
70 rdf:type schema:PublicationIssue
71 N3b0c4cd09065491a807f97ddaff1d6c1 schema:name doi
72 schema:value 10.1007/s10458-018-9397-9
73 rdf:type schema:PropertyValue
74 N7912e628716c488c834ced2e78980779 schema:name PPGC/UFRGS, CP 15064, 91501-970, Porto Alegre, RS, Brazil
75 rdf:type schema:Organization
76 N822a7b622cce4080bb0e3a69a1773586 rdf:first Ne252a1b943214167b8227950780ee703
77 rdf:rest rdf:nil
78 Na6f9cb61362c458e9281840895c75342 schema:name dimensions_id
79 schema:value pub.1107899625
80 rdf:type schema:PropertyValue
81 Ncad886c17739455496acc69d17a7b391 schema:name readcube_id
82 schema:value 2e0ed9fea57d7967e1ee6af108ad090503fbf44eb36b152551e2f782499609fa
83 rdf:type schema:PropertyValue
84 Ne252a1b943214167b8227950780ee703 schema:affiliation N7912e628716c488c834ced2e78980779
85 schema:familyName Bazzan
86 schema:givenName Ana
87 rdf:type schema:Person
88 Ne446582f1f374ad0a548da5b38e70d86 rdf:first sg:person.012567351734.32
89 rdf:rest Nf1bea0cc2ba14460a473d5c24d6da510
90 Nf1bea0cc2ba14460a473d5c24d6da510 rdf:first sg:person.01176306202.81
91 rdf:rest N822a7b622cce4080bb0e3a69a1773586
92 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
93 schema:name Information and Computing Sciences
94 rdf:type schema:DefinedTerm
95 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
96 schema:name Information Systems
97 rdf:type schema:DefinedTerm
98 sg:journal.1030506 schema:issn 1387-2532
99 1573-7454
100 schema:name Autonomous Agents and Multi-Agent Systems
101 rdf:type schema:Periodical
102 sg:person.01176306202.81 schema:affiliation https://www.grid.ac/institutes/grid.254672.0
103 schema:familyName Raja
104 schema:givenName Anita
105 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176306202.81
106 rdf:type schema:Person
107 sg:person.012567351734.32 schema:affiliation https://www.grid.ac/institutes/grid.24434.35
108 schema:familyName Hasan
109 schema:givenName Mohammad Rashedul
110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012567351734.32
111 rdf:type schema:Person
112 sg:pub.10.1007/11493402_13 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030362025
113 https://doi.org/10.1007/11493402_13
114 rdf:type schema:CreativeWork
115 sg:pub.10.1007/978-1-4471-4054-2_12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034971246
116 https://doi.org/10.1007/978-1-4471-4054-2_12
117 rdf:type schema:CreativeWork
118 sg:pub.10.1007/978-3-540-93920-7_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036962552
119 https://doi.org/10.1007/978-3-540-93920-7_2
120 rdf:type schema:CreativeWork
121 sg:pub.10.1007/978-3-642-21268-0_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001132244
122 https://doi.org/10.1007/978-3-642-21268-0_14
123 rdf:type schema:CreativeWork
124 sg:pub.10.1007/s10458-012-9193-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1045393059
125 https://doi.org/10.1007/s10458-012-9193-x
126 rdf:type schema:CreativeWork
127 sg:pub.10.1007/s10458-013-9240-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044458129
128 https://doi.org/10.1007/s10458-013-9240-2
129 rdf:type schema:CreativeWork
130 sg:pub.10.1007/s13278-018-0490-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101081263
131 https://doi.org/10.1007/s13278-018-0490-5
132 rdf:type schema:CreativeWork
133 sg:pub.10.1038/30918 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041985305
134 https://doi.org/10.1038/30918
135 rdf:type schema:CreativeWork
136 sg:pub.10.1038/359826a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030106977
137 https://doi.org/10.1038/359826a0
138 rdf:type schema:CreativeWork
139 sg:pub.10.1038/nature04605 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017620878
140 https://doi.org/10.1038/nature04605
141 rdf:type schema:CreativeWork
142 sg:pub.10.1038/scientificamerican0714-36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000576054
143 https://doi.org/10.1038/scientificamerican0714-36
144 rdf:type schema:CreativeWork
145 https://app.dimensions.ai/details/publication/pub.1098907933 schema:CreativeWork
146 https://doi.org/10.1006/jtbi.1999.0981 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050803743
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/s0004-3702(02)00262-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1052425993
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1017/cbo9780511791383 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098667199
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1073/pnas.122653799 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018411012
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1093/acprof:oso/9780198515906.001.0001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098793632
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1093/acprof:oso/9780199206650.001.0001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098762313
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1103/physreve.66.021907 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009539472
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1103/physreve.71.066107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005066634
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1103/physreve.77.011904 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021437599
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1103/physreve.78.026117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060737808
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1103/physreve.85.056109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009427359
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1103/physrevlett.97.258103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034007587
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1109/greencom-cpscom.2010.122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093635226
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1109/infcom.2007.327 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094549975
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1109/mprv.2009.49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061418533
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1109/saso.2010.38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093598744
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1109/wi-iat.2009.155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095484295
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1109/wi-iat.2013.130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095436827
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1126/science.286.5439.509 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010080128
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1137/130914218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062870600
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1145/2396761.2396820 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053174729
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1162/artl.1995.2.3.319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003920767
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1209/0295-5075/119/30002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092383924
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1371/journal.pone.0011976 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030639307
193 rdf:type schema:CreativeWork
194 https://doi.org/10.21236/ada325130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091735743
195 rdf:type schema:CreativeWork
196 https://doi.org/10.3233/mgs-2011-0167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011748634
197 rdf:type schema:CreativeWork
198 https://www.grid.ac/institutes/grid.24434.35 schema:alternateName University of Nebraska–Lincoln
199 schema:name University of Nebraska-Lincoln, 68588, Lincoln, NE, USA
200 rdf:type schema:Organization
201 https://www.grid.ac/institutes/grid.254672.0 schema:alternateName Cooper Union
202 schema:name The Cooper Union, 10003, New York, NY, USA
203 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...