Verification and control of partially observable probabilistic systems View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-03-08

AUTHORS

Gethin Norman, David Parker, Xueyi Zou

ABSTRACT

We present automated techniques for the verification and control of partially observable, probabilistic systems for both discrete and dense models of time. For the discrete-time case, we formally model these systems using partially observable Markov decision processes; for dense time, we propose an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give probabilistic temporal logics that can express a range of quantitative properties of these models, relating to the probability of an event’s occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or synthesise a controller for the model which makes it true. Our approach is based on a grid-based abstraction of the uncountable belief space induced by partial observability and, for dense-time models, an integer discretisation of real-time behaviour. The former is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies from the domains of task and network scheduling, computer security and planning. More... »

PAGES

354-402

References to SciGraph publications

  • 2012-06-08. A survey of point-based POMDP solvers in AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS
  • 2015-04-08. Wireless scheduling with partial channel state information: large deviations and optimality in QUEUEING SYSTEMS
  • 2015-08-22. Verification and Control of Partially Observable Probabilistic Real-Time Systems in FORMAL MODELING AND ANALYSIS OF TIMED SYSTEMS
  • 1994. Probabilistic simulations for probabilistic processes in CONCUR '94: CONCURRENCY THEORY
  • 2006-06-07. Performance analysis of probabilistic timed automata using digital clocks in FORMAL METHODS IN SYSTEM DESIGN
  • 1984. A Course in Triangulations for Solving Equations with Deformations in NONE
  • 2011. Automated Verification Techniques for Probabilistic Systems in FORMAL METHODS FOR ETERNAL NETWORKED SOFTWARE SYSTEMS
  • 1994-09. A logic for reasoning about time and reliability in FORMAL ASPECTS OF COMPUTING
  • 1992. What good are digital clocks? in AUTOMATA, LANGUAGES AND PROGRAMMING
  • 2012-10-12. Model checking for probabilistic timed automata in FORMAL METHODS IN SYSTEM DESIGN
  • 1999-04-30. Verifying Progress in Timed Systems in FORMAL METHODS FOR REAL-TIME AND PROBABILISTIC SYSTEMS
  • 2012. Template-Based Controller Synthesis for Timed Systems in TOOLS AND ALGORITHMS FOR THE CONSTRUCTION AND ANALYSIS OF SYSTEMS
  • 2007. Timed Control with Observation Based and Stuttering Invariant Strategies in AUTOMATED TECHNOLOGY FOR VERIFICATION AND ANALYSIS
  • 2012. Verification of Partial-Information Probabilistic Systems Using Counterexample-Guided Refinements in AUTOMATED TECHNOLOGY FOR VERIFICATION AND ANALYSIS
  • 2004-01-01. Controller Synthesis for Probabilistic Systems (Extended Abstract) in EXPLORING NEW FRONTIERS OF THEORETICAL INFORMATICS
  • 2001-03-21. Minimum-Cost Reachability for Priced Time Automata in HYBRID SYSTEMS: COMPUTATION AND CONTROL
  • 1999-07. Reactive Modules in FORMAL METHODS IN SYSTEM DESIGN
  • 2011. Quantitative Synthesis for Concurrent Programs in COMPUTER AIDED VERIFICATION
  • 2005-05. Checking Timed Büchi Automata Emptiness Efficiently in FORMAL METHODS IN SYSTEM DESIGN
  • 1976. Denumerable Markov Chains, with a chapter of Markov Random Fields by David Griffeath in NONE
  • 2003. Timed Control with Partial Observability in COMPUTER AIDED VERIFICATION
  • 2011. PRISM 4.0: Verification of Probabilistic Real-Time Systems in COMPUTER AIDED VERIFICATION
  • 2008-01-01. On Decision Problems for Probabilistic Büchi Automata in FOUNDATIONS OF SOFTWARE SCIENCE AND COMPUTATIONAL STRUCTURES
  • 1988-01. The dining cryptographers problem: Unconditional sender and recipient untraceability in JOURNAL OF CRYPTOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11241-017-9269-4

    DOI

    http://dx.doi.org/10.1007/s11241-017-9269-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1084030499


    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"
          }, 
          {
            "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": "School of Computing Science, University of Glasgow, Glasgow, UK", 
              "id": "http://www.grid.ac/institutes/grid.8756.c", 
              "name": [
                "School of Computing Science, University of Glasgow, Glasgow, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Norman", 
            "givenName": "Gethin", 
            "id": "sg:person.016323171577.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016323171577.36"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of Computer Science, University of Birmingham, Birmingham, UK", 
              "id": "http://www.grid.ac/institutes/grid.6572.6", 
              "name": [
                "School of Computer Science, University of Birmingham, Birmingham, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Parker", 
            "givenName": "David", 
            "id": "sg:person.014007552600.37", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014007552600.37"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Computer Science, University of York, York, UK", 
              "id": "http://www.grid.ac/institutes/grid.5685.e", 
              "name": [
                "Department of Computer Science, University of York, York, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zou", 
            "givenName": "Xueyi", 
            "id": "sg:person.013264431037.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013264431037.49"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/3-540-45351-2_15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002788038", 
              "https://doi.org/10.1007/3-540-45351-2_15"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11134-015-9439-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004357064", 
              "https://doi.org/10.1007/s11134-015-9439-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01211866", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052049375", 
              "https://doi.org/10.1007/bf01211866"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/1-4020-8141-3_38", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005039352", 
              "https://doi.org/10.1007/1-4020-8141-3_38"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-75596-8_15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033876286", 
              "https://doi.org/10.1007/978-3-540-75596-8_15"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10703-005-1632-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017423314", 
              "https://doi.org/10.1007/s10703-005-1632-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-33386-6_26", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033136361", 
              "https://doi.org/10.1007/978-3-642-33386-6_26"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-22110-1_20", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044108353", 
              "https://doi.org/10.1007/978-3-642-22110-1_20"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-55719-9_103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006689972", 
              "https://doi.org/10.1007/3-540-55719-9_103"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00206326", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041027293", 
              "https://doi.org/10.1007/bf00206326"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-28756-5_27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004752029", 
              "https://doi.org/10.1007/978-3-642-28756-5_27"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-46516-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043772236", 
              "https://doi.org/10.1007/978-3-642-46516-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-21455-4_3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043781609", 
              "https://doi.org/10.1007/978-3-642-21455-4_3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4684-9455-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003855982", 
              "https://doi.org/10.1007/978-1-4684-9455-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-45069-6_18", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002688616", 
              "https://doi.org/10.1007/978-3-540-45069-6_18"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bfb0015027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023196586", 
              "https://doi.org/10.1007/bfb0015027"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-22975-1_16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008952778", 
              "https://doi.org/10.1007/978-3-319-22975-1_16"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10703-012-0177-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047241468", 
              "https://doi.org/10.1007/s10703-012-0177-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-22110-1_47", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027900693", 
              "https://doi.org/10.1007/978-3-642-22110-1_47"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1008739929481", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040056258", 
              "https://doi.org/10.1023/a:1008739929481"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10458-012-9200-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037579583", 
              "https://doi.org/10.1007/s10458-012-9200-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-48778-6_18", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026111332", 
              "https://doi.org/10.1007/3-540-48778-6_18"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-78499-9_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027448880", 
              "https://doi.org/10.1007/978-3-540-78499-9_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10703-006-0005-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002395349", 
              "https://doi.org/10.1007/s10703-006-0005-2"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-03-08", 
        "datePublishedReg": "2017-03-08", 
        "description": "We present automated techniques for the verification and control of partially observable, probabilistic systems for both discrete and dense models of time. For the discrete-time case, we formally model these systems using partially observable Markov decision processes; for dense time, we propose an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give probabilistic temporal logics that can express a range of quantitative properties of these models, relating to the probability of an event\u2019s occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or synthesise a controller for the model which makes it true. Our approach is based on a grid-based abstraction of the uncountable belief space induced by partial observability and, for dense-time models, an integer discretisation of real-time behaviour. The former is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies from the domains of task and network scheduling, computer security and planning.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11241-017-9269-4", 
        "inLanguage": "en", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.3497570", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.6711417", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3560167", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1136406", 
            "issn": [
              "0922-6443", 
              "1573-1383"
            ], 
            "name": "Real-Time Systems", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "53"
          }
        ], 
        "keywords": [
          "probabilistic systems", 
          "PRISM model checker", 
          "dense time model", 
          "probabilistic temporal logic", 
          "real-time behavior", 
          "domains of tasks", 
          "observable Markov decision process", 
          "extension of probabilistic", 
          "computer security", 
          "Markov decision process", 
          "model checker", 
          "temporal logic", 
          "network scheduling", 
          "dense model", 
          "belief space", 
          "partial observability", 
          "dense time", 
          "decision process", 
          "reward measures", 
          "event occurrence", 
          "verification", 
          "quantitative properties", 
          "upper bounds", 
          "checker", 
          "controller", 
          "case study", 
          "scheduling", 
          "security", 
          "abstraction", 
          "system", 
          "probabilistic", 
          "local state", 
          "task", 
          "automata", 
          "logic", 
          "technique", 
          "model", 
          "planning", 
          "observability", 
          "effectiveness", 
          "bounds", 
          "discrete-time case", 
          "domain", 
          "space", 
          "extension", 
          "time", 
          "numerical results", 
          "probability", 
          "control", 
          "process", 
          "discretisation", 
          "state", 
          "results", 
          "measures", 
          "behavior", 
          "observer", 
          "cases", 
          "properties", 
          "values", 
          "range", 
          "study", 
          "occurrence", 
          "problem", 
          "approach"
        ], 
        "name": "Verification and control of partially observable probabilistic systems", 
        "pagination": "354-402", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1084030499"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11241-017-9269-4"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11241-017-9269-4", 
          "https://app.dimensions.ai/details/publication/pub.1084030499"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-05-10T10:19", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_751.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11241-017-9269-4"
      }
    ]
     

    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/s11241-017-9269-4'

    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/s11241-017-9269-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11241-017-9269-4'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11241-017-9269-4'


     

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

    248 TRIPLES      22 PREDICATES      114 URIs      81 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11241-017-9269-4 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 anzsrc-for:0802
    4 schema:author N2f3ef2fd34df4aacbdc153b41124c291
    5 schema:citation sg:pub.10.1007/1-4020-8141-3_38
    6 sg:pub.10.1007/3-540-45351-2_15
    7 sg:pub.10.1007/3-540-48778-6_18
    8 sg:pub.10.1007/3-540-55719-9_103
    9 sg:pub.10.1007/978-1-4684-9455-6
    10 sg:pub.10.1007/978-3-319-22975-1_16
    11 sg:pub.10.1007/978-3-540-45069-6_18
    12 sg:pub.10.1007/978-3-540-75596-8_15
    13 sg:pub.10.1007/978-3-540-78499-9_21
    14 sg:pub.10.1007/978-3-642-21455-4_3
    15 sg:pub.10.1007/978-3-642-22110-1_20
    16 sg:pub.10.1007/978-3-642-22110-1_47
    17 sg:pub.10.1007/978-3-642-28756-5_27
    18 sg:pub.10.1007/978-3-642-33386-6_26
    19 sg:pub.10.1007/978-3-642-46516-1
    20 sg:pub.10.1007/bf00206326
    21 sg:pub.10.1007/bf01211866
    22 sg:pub.10.1007/bfb0015027
    23 sg:pub.10.1007/s10458-012-9200-2
    24 sg:pub.10.1007/s10703-005-1632-8
    25 sg:pub.10.1007/s10703-006-0005-2
    26 sg:pub.10.1007/s10703-012-0177-x
    27 sg:pub.10.1007/s11134-015-9439-9
    28 sg:pub.10.1023/a:1008739929481
    29 schema:datePublished 2017-03-08
    30 schema:datePublishedReg 2017-03-08
    31 schema:description We present automated techniques for the verification and control of partially observable, probabilistic systems for both discrete and dense models of time. For the discrete-time case, we formally model these systems using partially observable Markov decision processes; for dense time, we propose an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give probabilistic temporal logics that can express a range of quantitative properties of these models, relating to the probability of an event’s occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or synthesise a controller for the model which makes it true. Our approach is based on a grid-based abstraction of the uncountable belief space induced by partial observability and, for dense-time models, an integer discretisation of real-time behaviour. The former is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies from the domains of task and network scheduling, computer security and planning.
    32 schema:genre article
    33 schema:inLanguage en
    34 schema:isAccessibleForFree true
    35 schema:isPartOf N0ceb86ebfabd479dab1a452283c85d0d
    36 N899fa4fcede54e118a7a8c1710a97496
    37 sg:journal.1136406
    38 schema:keywords Markov decision process
    39 PRISM model checker
    40 abstraction
    41 approach
    42 automata
    43 behavior
    44 belief space
    45 bounds
    46 case study
    47 cases
    48 checker
    49 computer security
    50 control
    51 controller
    52 decision process
    53 dense model
    54 dense time
    55 dense time model
    56 discrete-time case
    57 discretisation
    58 domain
    59 domains of tasks
    60 effectiveness
    61 event occurrence
    62 extension
    63 extension of probabilistic
    64 local state
    65 logic
    66 measures
    67 model
    68 model checker
    69 network scheduling
    70 numerical results
    71 observability
    72 observable Markov decision process
    73 observer
    74 occurrence
    75 partial observability
    76 planning
    77 probabilistic
    78 probabilistic systems
    79 probabilistic temporal logic
    80 probability
    81 problem
    82 process
    83 properties
    84 quantitative properties
    85 range
    86 real-time behavior
    87 results
    88 reward measures
    89 scheduling
    90 security
    91 space
    92 state
    93 study
    94 system
    95 task
    96 technique
    97 temporal logic
    98 time
    99 upper bounds
    100 values
    101 verification
    102 schema:name Verification and control of partially observable probabilistic systems
    103 schema:pagination 354-402
    104 schema:productId N0dbf2b712eae4e74b6f5bcc8ff3a21c2
    105 N49fe9423f35d41529d1053d181a84fea
    106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084030499
    107 https://doi.org/10.1007/s11241-017-9269-4
    108 schema:sdDatePublished 2022-05-10T10:19
    109 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    110 schema:sdPublisher N77062a1e12224f268632782fb7092c3b
    111 schema:url https://doi.org/10.1007/s11241-017-9269-4
    112 sgo:license sg:explorer/license/
    113 sgo:sdDataset articles
    114 rdf:type schema:ScholarlyArticle
    115 N0bc0a7f41df945ef8f577ef93ae3ce0b rdf:first sg:person.013264431037.49
    116 rdf:rest rdf:nil
    117 N0ceb86ebfabd479dab1a452283c85d0d schema:volumeNumber 53
    118 rdf:type schema:PublicationVolume
    119 N0dbf2b712eae4e74b6f5bcc8ff3a21c2 schema:name doi
    120 schema:value 10.1007/s11241-017-9269-4
    121 rdf:type schema:PropertyValue
    122 N2f3ef2fd34df4aacbdc153b41124c291 rdf:first sg:person.016323171577.36
    123 rdf:rest N35892c6afb3746db9fb1bd44b12561c8
    124 N35892c6afb3746db9fb1bd44b12561c8 rdf:first sg:person.014007552600.37
    125 rdf:rest N0bc0a7f41df945ef8f577ef93ae3ce0b
    126 N49fe9423f35d41529d1053d181a84fea schema:name dimensions_id
    127 schema:value pub.1084030499
    128 rdf:type schema:PropertyValue
    129 N77062a1e12224f268632782fb7092c3b schema:name Springer Nature - SN SciGraph project
    130 rdf:type schema:Organization
    131 N899fa4fcede54e118a7a8c1710a97496 schema:issueNumber 3
    132 rdf:type schema:PublicationIssue
    133 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    134 schema:name Information and Computing Sciences
    135 rdf:type schema:DefinedTerm
    136 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    137 schema:name Artificial Intelligence and Image Processing
    138 rdf:type schema:DefinedTerm
    139 anzsrc-for:0802 schema:inDefinedTermSet anzsrc-for:
    140 schema:name Computation Theory and Mathematics
    141 rdf:type schema:DefinedTerm
    142 sg:grant.3497570 http://pending.schema.org/fundedItem sg:pub.10.1007/s11241-017-9269-4
    143 rdf:type schema:MonetaryGrant
    144 sg:grant.3560167 http://pending.schema.org/fundedItem sg:pub.10.1007/s11241-017-9269-4
    145 rdf:type schema:MonetaryGrant
    146 sg:grant.6711417 http://pending.schema.org/fundedItem sg:pub.10.1007/s11241-017-9269-4
    147 rdf:type schema:MonetaryGrant
    148 sg:journal.1136406 schema:issn 0922-6443
    149 1573-1383
    150 schema:name Real-Time Systems
    151 schema:publisher Springer Nature
    152 rdf:type schema:Periodical
    153 sg:person.013264431037.49 schema:affiliation grid-institutes:grid.5685.e
    154 schema:familyName Zou
    155 schema:givenName Xueyi
    156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013264431037.49
    157 rdf:type schema:Person
    158 sg:person.014007552600.37 schema:affiliation grid-institutes:grid.6572.6
    159 schema:familyName Parker
    160 schema:givenName David
    161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014007552600.37
    162 rdf:type schema:Person
    163 sg:person.016323171577.36 schema:affiliation grid-institutes:grid.8756.c
    164 schema:familyName Norman
    165 schema:givenName Gethin
    166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016323171577.36
    167 rdf:type schema:Person
    168 sg:pub.10.1007/1-4020-8141-3_38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005039352
    169 https://doi.org/10.1007/1-4020-8141-3_38
    170 rdf:type schema:CreativeWork
    171 sg:pub.10.1007/3-540-45351-2_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002788038
    172 https://doi.org/10.1007/3-540-45351-2_15
    173 rdf:type schema:CreativeWork
    174 sg:pub.10.1007/3-540-48778-6_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026111332
    175 https://doi.org/10.1007/3-540-48778-6_18
    176 rdf:type schema:CreativeWork
    177 sg:pub.10.1007/3-540-55719-9_103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006689972
    178 https://doi.org/10.1007/3-540-55719-9_103
    179 rdf:type schema:CreativeWork
    180 sg:pub.10.1007/978-1-4684-9455-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003855982
    181 https://doi.org/10.1007/978-1-4684-9455-6
    182 rdf:type schema:CreativeWork
    183 sg:pub.10.1007/978-3-319-22975-1_16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008952778
    184 https://doi.org/10.1007/978-3-319-22975-1_16
    185 rdf:type schema:CreativeWork
    186 sg:pub.10.1007/978-3-540-45069-6_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002688616
    187 https://doi.org/10.1007/978-3-540-45069-6_18
    188 rdf:type schema:CreativeWork
    189 sg:pub.10.1007/978-3-540-75596-8_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033876286
    190 https://doi.org/10.1007/978-3-540-75596-8_15
    191 rdf:type schema:CreativeWork
    192 sg:pub.10.1007/978-3-540-78499-9_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027448880
    193 https://doi.org/10.1007/978-3-540-78499-9_21
    194 rdf:type schema:CreativeWork
    195 sg:pub.10.1007/978-3-642-21455-4_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043781609
    196 https://doi.org/10.1007/978-3-642-21455-4_3
    197 rdf:type schema:CreativeWork
    198 sg:pub.10.1007/978-3-642-22110-1_20 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044108353
    199 https://doi.org/10.1007/978-3-642-22110-1_20
    200 rdf:type schema:CreativeWork
    201 sg:pub.10.1007/978-3-642-22110-1_47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027900693
    202 https://doi.org/10.1007/978-3-642-22110-1_47
    203 rdf:type schema:CreativeWork
    204 sg:pub.10.1007/978-3-642-28756-5_27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004752029
    205 https://doi.org/10.1007/978-3-642-28756-5_27
    206 rdf:type schema:CreativeWork
    207 sg:pub.10.1007/978-3-642-33386-6_26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033136361
    208 https://doi.org/10.1007/978-3-642-33386-6_26
    209 rdf:type schema:CreativeWork
    210 sg:pub.10.1007/978-3-642-46516-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043772236
    211 https://doi.org/10.1007/978-3-642-46516-1
    212 rdf:type schema:CreativeWork
    213 sg:pub.10.1007/bf00206326 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041027293
    214 https://doi.org/10.1007/bf00206326
    215 rdf:type schema:CreativeWork
    216 sg:pub.10.1007/bf01211866 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052049375
    217 https://doi.org/10.1007/bf01211866
    218 rdf:type schema:CreativeWork
    219 sg:pub.10.1007/bfb0015027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023196586
    220 https://doi.org/10.1007/bfb0015027
    221 rdf:type schema:CreativeWork
    222 sg:pub.10.1007/s10458-012-9200-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037579583
    223 https://doi.org/10.1007/s10458-012-9200-2
    224 rdf:type schema:CreativeWork
    225 sg:pub.10.1007/s10703-005-1632-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017423314
    226 https://doi.org/10.1007/s10703-005-1632-8
    227 rdf:type schema:CreativeWork
    228 sg:pub.10.1007/s10703-006-0005-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002395349
    229 https://doi.org/10.1007/s10703-006-0005-2
    230 rdf:type schema:CreativeWork
    231 sg:pub.10.1007/s10703-012-0177-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1047241468
    232 https://doi.org/10.1007/s10703-012-0177-x
    233 rdf:type schema:CreativeWork
    234 sg:pub.10.1007/s11134-015-9439-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004357064
    235 https://doi.org/10.1007/s11134-015-9439-9
    236 rdf:type schema:CreativeWork
    237 sg:pub.10.1023/a:1008739929481 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040056258
    238 https://doi.org/10.1023/a:1008739929481
    239 rdf:type schema:CreativeWork
    240 grid-institutes:grid.5685.e schema:alternateName Department of Computer Science, University of York, York, UK
    241 schema:name Department of Computer Science, University of York, York, UK
    242 rdf:type schema:Organization
    243 grid-institutes:grid.6572.6 schema:alternateName School of Computer Science, University of Birmingham, Birmingham, UK
    244 schema:name School of Computer Science, University of Birmingham, Birmingham, UK
    245 rdf:type schema:Organization
    246 grid-institutes:grid.8756.c schema:alternateName School of Computing Science, University of Glasgow, Glasgow, UK
    247 schema:name School of Computing Science, University of Glasgow, Glasgow, UK
    248 rdf:type schema:Organization
     




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


    ...