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.}
}