Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-12-22

AUTHORS

T. Schmickl, K. Crailsheim

ABSTRACT

This article presents a bio-inspired communication strategy for large-scale robotic swarms. The strategy is based purely on robot-to-robot interactions without any central unit of communication. Thus, the emerging swarm regulates itself in a purely self-organized way. The strategy is biologically inspired by the trophallactic behavior (mouth-to-mouth feedings) performed by social insects. We show how this strategy can be used in a collective foraging scenario and how the efficiency of this strategy can be shaped by evolutionary computation. Although the algorithm works stable enough that it can be easily parameterized by hand, we found that artificial evolution could further increase the efficiency of the swarm’s behavior. We investigated the suggested communication strategy by simulation of robotic swarms in several arena scenarios and studied the properties of some of the emergent collective decisions made by the robots. We found that our control algorithm led to a nonlinear, but graduated path selection of the emerging trail of loaded robots. They favored the shortest path, but not all robots converged to this trail, except in arena setups with extreme differences in the length of the two possible paths. Finally, we demonstrate how the flexibility of collective decisions that arise through this new strategy can be used in changing environments. We furthermore show the importance of a negative feedback in an environment with changing foraging targets. Such feedback loops allow outdated information to decay over time. We found that task efficiency is constrained by a lower and an upper boundary concerning the strength of this negative feedback. More... »

PAGES

171-188

References to SciGraph publications

  • 2007-01-01. An Analytical and Spatial Model of Foraging in a Swarm of Robots in SWARM ROBOTICS
  • 2001-11. Pheromone Robotics in AUTONOMOUS ROBOTS
  • 1990-09. Collective decision making through food recruitment in INSECTES SOCIAUX
  • 1993-04. The regulation of pollen foraging by honey bees: how foragers assess the colony's need for pollen in BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY
  • 1998. Trophallactic interactions in the adult honeybee (Apis mellifera L.) in APIDOLOGIE
  • 1959-03. La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs in INSECTES SOCIAUX
  • 2007-01-01. A Macroscopic Model for Self-organized Aggregation in Swarm Robotic Systems in SWARM ROBOTICS
  • 2005. Pheromone Robotics and the Logic of Virtual Pheromones in SWARM ROBOTICS
  • 2005. The I-SWARM Project: Intelligent Small World Autonomous Robots for Micro-manipulation in SWARM ROBOTICS
  • 1991-04. Collective decision-making in honey bees: how colonies choose among nectar sources in BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY
  • 1992-12. The flow of jelly within a honeybee colony in JOURNAL OF COMPARATIVE PHYSIOLOGY B
  • 1967-03. Nouvelles expériences sur le Termite de Müller (Macrotermes mülleri) et considérations sur la théorie de la stigmergie in INSECTES SOCIAUX
  • 2003-02. A modelling framework for understanding social insect foraging in BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY
  • 1998. Protein trophallaxis and the regulation of pollen foraging by honey bees (Apis mellifera L.) in APIDOLOGIE
  • 2000-11. The flow of incoming nectar through a honey bee (Apis mellifera L.) colony as revealed by a protein marker in INSECTES SOCIAUX
  • 2007-01-01. Collective Perception in a Robot Swarm in SWARM ROBOTICS
  • 2006-03-09. How to Construct Dense Objects with Self-Recondfigurable Robots in EUROPEAN ROBOTICS SYMPOSIUM 2006
  • 2007-01-01. A Navigation Algorithm for Swarm Robotics Inspired by Slime Mold Aggregation in SWARM ROBOTICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10514-007-9073-4

    DOI

    http://dx.doi.org/10.1007/s10514-007-9073-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1046474988


    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/2008/08", 
            "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": "Department for Zoology, Karl-Franzens-University Graz, Universitaetsplatz 2, 8010, Graz, Austria", 
              "id": "http://www.grid.ac/institutes/grid.5110.5", 
              "name": [
                "Department for Zoology, Karl-Franzens-University Graz, Universitaetsplatz 2, 8010, Graz, Austria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Schmickl", 
            "givenName": "T.", 
            "id": "sg:person.07417426760.84", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07417426760.84"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department for Zoology, Karl-Franzens-University Graz, Universitaetsplatz 2, 8010, Graz, Austria", 
              "id": "http://www.grid.ac/institutes/grid.5110.5", 
              "name": [
                "Department for Zoology, Karl-Franzens-University Graz, Universitaetsplatz 2, 8010, Graz, Austria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Crailsheim", 
            "givenName": "K.", 
            "id": "sg:person.01366315645.25", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01366315645.25"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/11681120_3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025773291", 
              "https://doi.org/10.1007/11681120_3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-71541-2_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038109082", 
              "https://doi.org/10.1007/978-3-540-71541-2_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-71541-2_3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050316734", 
              "https://doi.org/10.1007/978-3-540-71541-2_3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-71541-2_10", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021375493", 
              "https://doi.org/10.1007/978-3-540-71541-2_10"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-30552-1_7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050843158", 
              "https://doi.org/10.1007/978-3-540-30552-1_7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1051/apido:19980106", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040617905", 
              "https://doi.org/10.1051/apido:19980106"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02223791", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002829173", 
              "https://doi.org/10.1007/bf02223791"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02224053", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032193509", 
              "https://doi.org/10.1007/bf02224053"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00175101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005082071", 
              "https://doi.org/10.1007/bf00175101"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/pl00001720", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016335375", 
              "https://doi.org/10.1007/pl00001720"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-71541-2_1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040572979", 
              "https://doi.org/10.1007/978-3-540-71541-2_1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-30552-1_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008382347", 
              "https://doi.org/10.1007/978-3-540-30552-1_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02222755", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015267716", 
              "https://doi.org/10.1007/bf02222755"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1012411712038", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035287834", 
              "https://doi.org/10.1023/a:1012411712038"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1051/apido:19980107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037449866", 
              "https://doi.org/10.1051/apido:19980107"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00301617", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000466431", 
              "https://doi.org/10.1007/bf00301617"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00265-002-0549-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085152879", 
              "https://doi.org/10.1007/s00265-002-0549-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00166516", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034021708", 
              "https://doi.org/10.1007/bf00166516"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2007-12-22", 
        "datePublishedReg": "2007-12-22", 
        "description": "Abstract\nThis article presents a bio-inspired communication strategy for large-scale robotic swarms. The strategy is based purely on robot-to-robot interactions without any central unit of communication. Thus, the emerging swarm regulates itself in a purely self-organized way. The strategy is biologically inspired by the trophallactic behavior (mouth-to-mouth feedings) performed by social insects. We show how this strategy can be used in a collective foraging scenario and how the efficiency of this strategy can be shaped by evolutionary computation. Although the algorithm works stable enough that it can be easily parameterized by hand, we found that artificial evolution could further increase the efficiency of the swarm\u2019s behavior. We investigated the suggested communication strategy by simulation of robotic swarms in several arena scenarios and studied the properties of some of the emergent collective decisions made by the robots. We found that our control algorithm led to a nonlinear, but graduated path selection of the emerging trail of loaded robots. They favored the shortest path, but not all robots converged to this trail, except in arena setups with extreme differences in the length of the two possible paths. Finally, we demonstrate how the flexibility of collective decisions that arise through this new strategy can be used in changing environments. We furthermore show the importance of a negative feedback in an environment with changing foraging targets. Such feedback loops allow outdated information to decay over time. We found that task efficiency is constrained by a lower and an upper boundary concerning the strength of this negative feedback.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s10514-007-9073-4", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1031086", 
            "issn": [
              "0929-5593", 
              "1573-7527"
            ], 
            "name": "Autonomous Robots", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1-2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "25"
          }
        ], 
        "keywords": [
          "robotic swarm", 
          "self-organized way", 
          "robot interaction", 
          "evolutionary computation", 
          "swarm behavior", 
          "path selection", 
          "robot", 
          "artificial evolution", 
          "collective decision", 
          "shortest path", 
          "outdated information", 
          "task efficiency", 
          "social insects", 
          "foraging scenario", 
          "swarm", 
          "control algorithm", 
          "such feedback loops", 
          "algorithm", 
          "possible paths", 
          "central unit", 
          "trophallactic behavior", 
          "communication", 
          "communication strategies", 
          "scenarios", 
          "negative feedback", 
          "extreme differences", 
          "feedback loop", 
          "environment", 
          "insects", 
          "path", 
          "feedback", 
          "computation", 
          "decisions", 
          "trophallaxis", 
          "efficiency", 
          "information", 
          "flexibility", 
          "new strategy", 
          "strategies", 
          "trails", 
          "target", 
          "setup", 
          "simulations", 
          "evolution", 
          "selection", 
          "way", 
          "interaction", 
          "bio", 
          "loop", 
          "behavior", 
          "hand", 
          "time", 
          "importance", 
          "length", 
          "units", 
          "article", 
          "boundaries", 
          "differences", 
          "properties", 
          "upper boundary", 
          "strength"
        ], 
        "name": "Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm", 
        "pagination": "171-188", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1046474988"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10514-007-9073-4"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10514-007-9073-4", 
          "https://app.dimensions.ai/details/publication/pub.1046474988"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:26", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_439.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s10514-007-9073-4"
      }
    ]
     

    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/s10514-007-9073-4'

    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/s10514-007-9073-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10514-007-9073-4'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10514-007-9073-4'


     

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

    197 TRIPLES      21 PREDICATES      102 URIs      76 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10514-007-9073-4 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N5f1a2e54c36740d0b93fa7c096b31d99
    4 schema:citation sg:pub.10.1007/11681120_3
    5 sg:pub.10.1007/978-3-540-30552-1_5
    6 sg:pub.10.1007/978-3-540-30552-1_7
    7 sg:pub.10.1007/978-3-540-71541-2_1
    8 sg:pub.10.1007/978-3-540-71541-2_10
    9 sg:pub.10.1007/978-3-540-71541-2_3
    10 sg:pub.10.1007/978-3-540-71541-2_4
    11 sg:pub.10.1007/bf00166516
    12 sg:pub.10.1007/bf00175101
    13 sg:pub.10.1007/bf00301617
    14 sg:pub.10.1007/bf02222755
    15 sg:pub.10.1007/bf02223791
    16 sg:pub.10.1007/bf02224053
    17 sg:pub.10.1007/pl00001720
    18 sg:pub.10.1007/s00265-002-0549-0
    19 sg:pub.10.1023/a:1012411712038
    20 sg:pub.10.1051/apido:19980106
    21 sg:pub.10.1051/apido:19980107
    22 schema:datePublished 2007-12-22
    23 schema:datePublishedReg 2007-12-22
    24 schema:description Abstract This article presents a bio-inspired communication strategy for large-scale robotic swarms. The strategy is based purely on robot-to-robot interactions without any central unit of communication. Thus, the emerging swarm regulates itself in a purely self-organized way. The strategy is biologically inspired by the trophallactic behavior (mouth-to-mouth feedings) performed by social insects. We show how this strategy can be used in a collective foraging scenario and how the efficiency of this strategy can be shaped by evolutionary computation. Although the algorithm works stable enough that it can be easily parameterized by hand, we found that artificial evolution could further increase the efficiency of the swarm’s behavior. We investigated the suggested communication strategy by simulation of robotic swarms in several arena scenarios and studied the properties of some of the emergent collective decisions made by the robots. We found that our control algorithm led to a nonlinear, but graduated path selection of the emerging trail of loaded robots. They favored the shortest path, but not all robots converged to this trail, except in arena setups with extreme differences in the length of the two possible paths. Finally, we demonstrate how the flexibility of collective decisions that arise through this new strategy can be used in changing environments. We furthermore show the importance of a negative feedback in an environment with changing foraging targets. Such feedback loops allow outdated information to decay over time. We found that task efficiency is constrained by a lower and an upper boundary concerning the strength of this negative feedback.
    25 schema:genre article
    26 schema:isAccessibleForFree false
    27 schema:isPartOf N95b717d7406549e2afa55c1abfddb1c5
    28 N9e4c9edd4f1146a3bfca86372e60eb95
    29 sg:journal.1031086
    30 schema:keywords algorithm
    31 article
    32 artificial evolution
    33 behavior
    34 bio
    35 boundaries
    36 central unit
    37 collective decision
    38 communication
    39 communication strategies
    40 computation
    41 control algorithm
    42 decisions
    43 differences
    44 efficiency
    45 environment
    46 evolution
    47 evolutionary computation
    48 extreme differences
    49 feedback
    50 feedback loop
    51 flexibility
    52 foraging scenario
    53 hand
    54 importance
    55 information
    56 insects
    57 interaction
    58 length
    59 loop
    60 negative feedback
    61 new strategy
    62 outdated information
    63 path
    64 path selection
    65 possible paths
    66 properties
    67 robot
    68 robot interaction
    69 robotic swarm
    70 scenarios
    71 selection
    72 self-organized way
    73 setup
    74 shortest path
    75 simulations
    76 social insects
    77 strategies
    78 strength
    79 such feedback loops
    80 swarm
    81 swarm behavior
    82 target
    83 task efficiency
    84 time
    85 trails
    86 trophallactic behavior
    87 trophallaxis
    88 units
    89 upper boundary
    90 way
    91 schema:name Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm
    92 schema:pagination 171-188
    93 schema:productId N5b0507a961534427a2d2c150b577b180
    94 Nb95a25834fa849dbad148e6bea05e4b3
    95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046474988
    96 https://doi.org/10.1007/s10514-007-9073-4
    97 schema:sdDatePublished 2022-12-01T06:26
    98 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    99 schema:sdPublisher Na23468fee09e44a080e96f8305bf25c6
    100 schema:url https://doi.org/10.1007/s10514-007-9073-4
    101 sgo:license sg:explorer/license/
    102 sgo:sdDataset articles
    103 rdf:type schema:ScholarlyArticle
    104 N09a0d71d70dc4e049d31a11737aafc60 rdf:first sg:person.01366315645.25
    105 rdf:rest rdf:nil
    106 N5b0507a961534427a2d2c150b577b180 schema:name dimensions_id
    107 schema:value pub.1046474988
    108 rdf:type schema:PropertyValue
    109 N5f1a2e54c36740d0b93fa7c096b31d99 rdf:first sg:person.07417426760.84
    110 rdf:rest N09a0d71d70dc4e049d31a11737aafc60
    111 N95b717d7406549e2afa55c1abfddb1c5 schema:volumeNumber 25
    112 rdf:type schema:PublicationVolume
    113 N9e4c9edd4f1146a3bfca86372e60eb95 schema:issueNumber 1-2
    114 rdf:type schema:PublicationIssue
    115 Na23468fee09e44a080e96f8305bf25c6 schema:name Springer Nature - SN SciGraph project
    116 rdf:type schema:Organization
    117 Nb95a25834fa849dbad148e6bea05e4b3 schema:name doi
    118 schema:value 10.1007/s10514-007-9073-4
    119 rdf:type schema:PropertyValue
    120 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    121 schema:name Information and Computing Sciences
    122 rdf:type schema:DefinedTerm
    123 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    124 schema:name Artificial Intelligence and Image Processing
    125 rdf:type schema:DefinedTerm
    126 sg:journal.1031086 schema:issn 0929-5593
    127 1573-7527
    128 schema:name Autonomous Robots
    129 schema:publisher Springer Nature
    130 rdf:type schema:Periodical
    131 sg:person.01366315645.25 schema:affiliation grid-institutes:grid.5110.5
    132 schema:familyName Crailsheim
    133 schema:givenName K.
    134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01366315645.25
    135 rdf:type schema:Person
    136 sg:person.07417426760.84 schema:affiliation grid-institutes:grid.5110.5
    137 schema:familyName Schmickl
    138 schema:givenName T.
    139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07417426760.84
    140 rdf:type schema:Person
    141 sg:pub.10.1007/11681120_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025773291
    142 https://doi.org/10.1007/11681120_3
    143 rdf:type schema:CreativeWork
    144 sg:pub.10.1007/978-3-540-30552-1_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008382347
    145 https://doi.org/10.1007/978-3-540-30552-1_5
    146 rdf:type schema:CreativeWork
    147 sg:pub.10.1007/978-3-540-30552-1_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050843158
    148 https://doi.org/10.1007/978-3-540-30552-1_7
    149 rdf:type schema:CreativeWork
    150 sg:pub.10.1007/978-3-540-71541-2_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040572979
    151 https://doi.org/10.1007/978-3-540-71541-2_1
    152 rdf:type schema:CreativeWork
    153 sg:pub.10.1007/978-3-540-71541-2_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021375493
    154 https://doi.org/10.1007/978-3-540-71541-2_10
    155 rdf:type schema:CreativeWork
    156 sg:pub.10.1007/978-3-540-71541-2_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050316734
    157 https://doi.org/10.1007/978-3-540-71541-2_3
    158 rdf:type schema:CreativeWork
    159 sg:pub.10.1007/978-3-540-71541-2_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038109082
    160 https://doi.org/10.1007/978-3-540-71541-2_4
    161 rdf:type schema:CreativeWork
    162 sg:pub.10.1007/bf00166516 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034021708
    163 https://doi.org/10.1007/bf00166516
    164 rdf:type schema:CreativeWork
    165 sg:pub.10.1007/bf00175101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005082071
    166 https://doi.org/10.1007/bf00175101
    167 rdf:type schema:CreativeWork
    168 sg:pub.10.1007/bf00301617 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000466431
    169 https://doi.org/10.1007/bf00301617
    170 rdf:type schema:CreativeWork
    171 sg:pub.10.1007/bf02222755 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015267716
    172 https://doi.org/10.1007/bf02222755
    173 rdf:type schema:CreativeWork
    174 sg:pub.10.1007/bf02223791 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002829173
    175 https://doi.org/10.1007/bf02223791
    176 rdf:type schema:CreativeWork
    177 sg:pub.10.1007/bf02224053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032193509
    178 https://doi.org/10.1007/bf02224053
    179 rdf:type schema:CreativeWork
    180 sg:pub.10.1007/pl00001720 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016335375
    181 https://doi.org/10.1007/pl00001720
    182 rdf:type schema:CreativeWork
    183 sg:pub.10.1007/s00265-002-0549-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085152879
    184 https://doi.org/10.1007/s00265-002-0549-0
    185 rdf:type schema:CreativeWork
    186 sg:pub.10.1023/a:1012411712038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035287834
    187 https://doi.org/10.1023/a:1012411712038
    188 rdf:type schema:CreativeWork
    189 sg:pub.10.1051/apido:19980106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040617905
    190 https://doi.org/10.1051/apido:19980106
    191 rdf:type schema:CreativeWork
    192 sg:pub.10.1051/apido:19980107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037449866
    193 https://doi.org/10.1051/apido:19980107
    194 rdf:type schema:CreativeWork
    195 grid-institutes:grid.5110.5 schema:alternateName Department for Zoology, Karl-Franzens-University Graz, Universitaetsplatz 2, 8010, Graz, Austria
    196 schema:name Department for Zoology, Karl-Franzens-University Graz, Universitaetsplatz 2, 8010, Graz, Austria
    197 rdf:type schema:Organization
     




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


    ...