peter-2021.bib

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@inproceedings{Baumgartner:Combining:EC:DL:LP:FroCoS:2021,
  title = {{Combining Event Calculus and Description Logic Reasoning via Logic Programming}},
  booktitle = {{FroCoS 2021 - The 13th International Symposium on Frontiers of Combining Systems}},
  author = {Peter Baumgartner},
  year = {2021},
  editor = {Giles Reger and Boris Konev},
  url = {Combining-FroCoS-2021.pdf},
  series = {LNAI},
  url = {https://arxiv.org/abs/2109.04803},
  pages = {98--117},
  publisher = {Springer International Publishing},
  copyright = {Copyright Springer Verlag \url{http://www.springer.de/comp/lncs/index.html}},
  abstract = {The paper introduces a knowledge representation language that combines the event
  calculus with description logic in a logic programming framework. The purpose is to
  provide the user with an expressive language for modelling and analysing systems that
  evolve over time.
  The approach is exemplified with the logic programming language as implemented in the
  Fusemate system. The paper extends Fusemate's rule language with a weakly DL-safe
  interface to the description logic ALCIF and adapts the event calculus to this extended language.
  This way, time-stamped ABoxes can be manipulated as fluents in the event
  calculus. All that is done in the frame of Fusemate's concept of stratification by time.
  The paper provides conditions for soundness and completeness where appropriate.
  Using an elaborated example it demonstrates the interplay of the event calculus,
  description logic and
  logic programming rules for computing possible models as plausible explanations of the current state
  of the modelled system.}
}

@inproceedings{Baumgartner:Krumpholz:anomaly-detection-beef-supply-chain:ICCMS:2021,
  title = {Anomaly Detection in a Boxed Beef Supply Chain},
  booktitle = {{ICCMS 2021 - The 13th International Conference on Computer Modeling and Simulation}},
  author = {Peter Baumgartner and Alexander Krumpholz},
  month = {June},
  year = {2021},
  doi = {10.1145/3474963.3474964},
  url = {ICCMS-2021.pdf},
  publisher = {ACM},
  abstract = {An approach to simulating and analysing sensor events in a boxed beef supply chain is
  presented. The simulation component reflects our industrial partner's  transport routes and
  parameters under normal and abnormal conditions. The simulated transport events are fed
  into our situational awareness system for detecting temperature  anomalies or potential
  box tampering. The situational awareness system features a logic-based modeling language and an
  inference engine that tolerates incomplete or erroneous observations.
  The paper describes the approach and experimental results in more detail.}
}

@inproceedings{Baumgartner:Fusemate:SystemDescription:CADE:2021,
  title = {The Fusemate Logic Programming System (System Description)},
  booktitle = {{CADE-28 - The 28th International Conference on Automated Deduction}},
  author = {Peter Baumgartner},
  year = {2021},
  editor = {A. Platzer and G. Sutcliffe},
  url = {https://dx.doi.org/10.1007/978-3-030-79876-5_34},
  volume = {12699},
  series = {LNAI},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {589--601},
  copyright = {Copyright Springer Verlag \url{http://www.springer.de/comp/lncs/index.html}},
  abstract = {Fusemate is a logic programming system that implements the possible model semantics for disjunctive logic programs. 
Its input language is centered around a weak notion of stratification with comprehension and aggregation operators on top of it.
Fusemate is implemented as a shallow embedding in the Scala programming language.
This enables using Scala data types natively as terms, a tight interface with external systems, 
and it makes model computation available as an ordinary container data structure constructor.
The paper describes the above features and implementation aspects. 
It also demonstrates them with a non-trivial use-case, the embedding of the description logic ALCIF  into Fusemate’s input language.},
  note = {A version with minor corrections is available at \url{https://arxiv.org/abs/2103.01395}}
}

@inproceedings{Baumgartner:Haslum:situational-awareness-industrial-operations:ASOR:2018,
  author = {Peter Baumgartner and Patrik Haslum},
  title = {{Situational Awareness for Industrial Operations}},
  booktitle = {Data and Decision Sciences in Action 2},
  editor = {Ernst, Andreas T.
and Dunstall, Simon
and Garc{\'i}a-Flores, Rodolfo
and Grobler, Marthie
and Marlow, David},
  year = 2021,
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {125--137},
  url = {ASOR-2018.pdf},
  copyright = {Copyright Springer Verlag \url{http://www.springer.de/comp/lncs/index.html}},
  abstract = {The smooth operation of industrial or business enterprises rests on constantly
  monitoring, evaluating and projecting their current state into the near future.  Such
  \emph{situational awareness} problems are not well supported by today's software
  solutions, which often lack higher-level analytic capabilities. To address these issues
  we propose a modular and re-usable system architecture for monitoring systems in terms
  of their state evolution. As a main novelty, states are represented explicitly and are
  amenable to external analysis. Moreovoer, different state trajectories can be derived
  and analysed simultaneously, for dealing with incomplete or noisy input
  data. In the paper we describe the
  system architecture and our implementation of a core component, the state inference
  engine, through a shallow embedding in Scala.
  The implementation of our modelling language as an embeded domain-specific language
  grants the modeller expressive power and flexibility, yet allows us to abstract a
  significant part of the complexity of the model's execution into the common inference
  engine core.}
}