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Building compositional learning and optimization applications for mobile sensor networks with PILOT
Ilge Akkaya

Citation
Ilge Akkaya. "Building compositional learning and optimization applications for mobile sensor networks with PILOT". Talk or presentation, 16, October, 2015; Presented at the Eleventh Biennial Ptolemy Miniconference, Berkeley.

Abstract
Emerging distributed cyber-physical systems (CPS) are integrating a variety of sensors, actuators, communication networks, and computation resources, giving rise to a highly complex and heterogeneous system architecture. Building integrated applications that involve estimation, inference, machine learning, and coordination is becoming a difficult task due to the heterogeneous na.ture of involved components. We present PILOT (Ptolemy Inference, Learning, and Optimization Toolkit), an actor-oriented machine learning and optimization toolkit that is designed for developing data intensive distributed applications for heterogeneous distributed CPS. PILOT gives clear semantics to the learning and optimization workflow and leverages compositionality in this framework. Moreover, the actor interfaces and algorithmic abstractions provide accessibility to system designers who are non-experts in machine learning. The toolkit builds upon the aspect-oriented modeling (AOM) paradigm to provide compositional interfaces and separation.of-concerns during the development process and enables sensed environmental constraints to be dynamically integrated into learning and optimization tasks in run time via the accessor framework. We showcase PILOT on a simulation based robotic disaster response scenario, where a heterogeneous team of cooperative robots carry out a simultaneous localization and mapping task in an unknown environment. PILOT enables mobile target localization with map constraints in such scenario, where the map data is modified in real time via an accessor interface. Using a variety of heterogeneous sensor inputs (e.g., microphones, rangefinders, cameras), the learning application can perform target state estimation and path planning given the most up-to-date mapping information obtained from the robots.

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Citation formats  
  • HTML
    Ilge Akkaya. <a
    href="http://chess.eecs.berkeley.edu/pubs/1124.html"><i>Building
    compositional learning and optimization applications for
    mobile sensor networks with PILOT</i></a>, Talk
    or presentation,  16, October, 2015; Presented at the <a
    href="http://ptolemy.eecs.berkeley.edu/conferences/15/"
    >Eleventh Biennial Ptolemy Miniconference</a>,
    Berkeley.
  • Plain text
    Ilge Akkaya. "Building compositional learning and
    optimization applications for mobile sensor networks with
    PILOT". Talk or presentation,  16, October, 2015;
    Presented at the <a
    href="http://ptolemy.eecs.berkeley.edu/conferences/15/"
    >Eleventh Biennial Ptolemy Miniconference</a>,
    Berkeley.
  • BibTeX
    @presentation{Akkaya15_BuildingCompositionalLearningOptimizationApplications,
        author = {Ilge Akkaya},
        title = {Building compositional learning and optimization
                  applications for mobile sensor networks with PILOT},
        day = {16},
        month = {October},
        year = {2015},
        note = {Presented at the <a
                  href="http://ptolemy.eecs.berkeley.edu/conferences/15/"
                  >Eleventh Biennial Ptolemy Miniconference</a>,
                  Berkeley},
        abstract = {Emerging distributed cyber-physical systems (CPS)
                  are integrating a variety of sensors, actuators,
                  communication networks, and computation resources,
                  giving rise to a highly complex and heterogeneous
                  system architecture. Building integrated
                  applications that involve estimation, inference,
                  machine learning, and coordination is becoming a
                  difficult task due to the heterogeneous na.ture of
                  involved components. We present PILOT (Ptolemy
                  Inference, Learning, and Optimization Toolkit), an
                  actor-oriented machine learning and optimization
                  toolkit that is designed for developing data
                  intensive distributed applications for
                  heterogeneous distributed CPS. PILOT gives clear
                  semantics to the learning and optimization
                  workflow and leverages compositionality in this
                  framework. Moreover, the actor interfaces and
                  algorithmic abstractions provide accessibility to
                  system designers who are non-experts in machine
                  learning. The toolkit builds upon the
                  aspect-oriented modeling (AOM) paradigm to provide
                  compositional interfaces and
                  separation.of-concerns during the development
                  process and enables sensed environmental
                  constraints to be dynamically integrated into
                  learning and optimization tasks in run time via
                  the accessor framework. We showcase PILOT on a
                  simulation based robotic disaster response
                  scenario, where a heterogeneous team of
                  cooperative robots carry out a simultaneous
                  localization and mapping task in an unknown
                  environment. PILOT enables mobile target
                  localization with map constraints in such
                  scenario, where the map data is modified in real
                  time via an accessor interface. Using a variety of
                  heterogeneous sensor inputs (e.g., microphones,
                  rangefinders, cameras), the learning application
                  can perform target state estimation and path
                  planning given the most up-to-date mapping
                  information obtained from the robots. },
        URL = {http://chess.eecs.berkeley.edu/pubs/1124.html}
    }
    

Posted by Christopher Brooks on 19 Oct 2015.
Groups: ptolemy
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