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        <contrib contrib-type="author">
          <string-name>Davide Calvaresi</string-name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Aldo Franco Dragoni</string-name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Giorgio Buttazzo</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Scuola Superiore Sant'Anna</institution>
          ,
          <country country="IT">Italy</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>Universita Politecnica delle Marche</institution>
          ,
          <country country="IT">Italy</country>
        </aff>
        <aff id="aff2">
          <label>2</label>
          <institution>University of Applied Sciences Western Switzerland</institution>
          ,
          <country country="CH">Switzerland</country>
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      <p>Since Arti cial Intelligence applications became mature, there has been
growing interest in applying them into complex real equipments, especially in order to
implement \Cyber-Physical Systems" (CPS). Unfortunately, most AI algorithms
are characterized by unpredictable or high-variance performances, making them
almost unusable for real-time control under hard deadlines.</p>
      <p>Recently some researches focused on tailoring AI techniques to make them
more predictable by explicitly reasoning \within and about" strict timing
constraints (deadlines and precedence constraints between di erent tasks), but
unfortunately, as far as we know, little e ort has been spent to transfer these
approaches over Multi-Agents Systems (MAS), where additional constraints
deriving from concurrent use of mutually exclusive resources hold (internal memory,
communication channels and peripherals such as sensors and actuators).</p>
      <p>MAS have always been a relevant topic within AI, since its very beginning,
and nowadays their technological advancements are leading to a concrete
adoption of decentralized systems with ever increasing connections, interactions, and
computational capabilities. Radically new challenges are arising for MAS from
the domains of the \Internet of Things" (IoT), CPS and \safety-critical" systems
but, unfortunately, in these regards MAS tend to reproduce the same myopic
approach of their parent discipline: high-quality of reasoning and human-like
interaction with little regard to concrete temporal and resources constraints. In
\safety-critical" systems MAS have not only to exhibit rational human-standard
behaviors, but they must also guarantee the completions of tasks within their
deadlines without violating priorities and precedences constraints in accessing
mutually exclusive resources.</p>
      <p>The 1st International Workshop on Real-Time compliant Multi-Agent
Systems (RTcMAS) gathers contributions from both theoretical and pragmatic
perspectives, targeting the employment of MAS in IoT and CPS through the
exploitation of methodologies, algorithms and technologies from the Real-Time
Community. As such, RTcMAS has the potential to gather the attention of the
AI-interested audience from IJCAI-ECAI and AAMAS, with the goal of
building the grounds for the next-generation Intelligent CPS, capable to face the
challenges of the \ever more connected" IoT era.</p>
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