2 Search Results for "Chen, Zhuo"

Document
DOI: 10.4230/LITES.8.2.5
Bayesian Hybrid Automata: A Formal Model of Justified Belief in Interacting Hybrid Systems Subject to Imprecise Observation

Authors: Paul Kröger and Martin Fränzle

Published in: LITES, Volume 8, Issue 2 (2022): Special Issue on Distributed Hybrid Systems. Leibniz Transactions on Embedded Systems, Volume 8, Issue 2

Abstract
Hybrid discrete-continuous system dynamics arises when discrete actions, e.g. by a decision algorithm, meet continuous behaviour, e.g. due to physical processes and continuous control. A natural domain of such systems are emerging smart technologies which add elements of intelligence, co-operation, and adaptivity to physical entities, enabling them to interact with each other and with humans as systems of (human-)cyber-physical systems or (H)CPSes.Various flavours of hybrid automata have been suggested as a means to formally analyse CPS dynamics. In a previous article, we demonstrated that all these variants of hybrid automata provide inaccurate, in the sense of either overly pessimistic or overly optimistic, verdicts for engineered systems operating under imprecise observation of their environment due to, e.g., measurement error. We suggested a revised formal model, called Bayesian hybrid automata, that is able to represent state tracking and estimation in hybrid systems and thereby enhances precision of verdicts obtained from the model in comparison to traditional model variants.In this article, we present an extended definition of Bayesian hybrid automata which incorporates a new class of guard and invariant functions that allow to evaluate traditional guards and invariants over probability distributions. The resulting framework allows to model observers with knowledge about the control strategy of an observed agent but with imprecise estimates of the data on which the control decisions are based.
Cite as

Paul Kröger and Martin Fränzle. Bayesian Hybrid Automata: A Formal Model of Justified Belief in Interacting Hybrid Systems Subject to Imprecise Observation. In LITES, Volume 8, Issue 2 (2022): Special Issue on Distributed Hybrid Systems. Leibniz Transactions on Embedded Systems, Volume 8, Issue 2, pp. 05:1-05:27, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)

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@Article{kroger_et_al:LITES.8.2.5,
  author =	{Kr\"{o}ger, Paul and Fr\"{a}nzle, Martin},
  title =	{{Bayesian Hybrid Automata: A Formal Model of Justified Belief in Interacting Hybrid Systems Subject to Imprecise Observation}},
  booktitle =	{LITES, Volume 8, Issue 2 (2022): Special Issue on Distributed Hybrid Systems},
  pages =	{05:1--05:27},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2022},
  volume =	{8},
  number =	{2},
  editor =	{Kr\"{o}ger, Paul and Fr\"{a}nzle, Martin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES.8.2.5},
  doi =		{10.4230/LITES.8.2.5},
  annote =	{Keywords: }
}
Document
DOI: 10.4230/OASIcs.ICLP.2016.22
Automating Disease Management Using Answer Set Programming

Authors: Zhuo Chen

Published in: OASIcs, Volume 52, Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)

Abstract
Management of chronic diseases such as heart failure, diabetes, and chronic obstructive pulmonary disease (COPD) is a major problem in health care. A standard approach that the medical community has devised to manage widely prevalent chronic diseases such as chronic heart failure (CHF) is to have a committee of experts develop guidelines that all physicians should follow. These guidelines typically consist of a series of complex rules that make recommendations based on a patient's information. Due to their complexity, often the guidelines are either ignored or not complied with at all, which can result in poor medical practices. It is not even clear whether it is humanly possible to follow these guidelines due to their length and complexity. In the case of CHF management, the guidelines run nearly 80 pages. In this paper we describe a physician-advisory system for CHF management that codes the entire set of clinical practice guidelines for CHF using answer set programming. Our approach is based on developing reasoning templates (that we call knowledge patterns) and using these patterns to systemically code the clinical guidelines for CHF as ASP rules. Use of the knowledge patterns greatly facilitates the development of our system. Given a patient's medical information, our system generates a recommendation for treatment just as a human physician would, using the guidelines. Our system will work even in the presence of incomplete information. Our work makes two contributions: (i) it shows that highly complex guidelines can be successfully coded as ASP rules, and (ii) it develops a series of knowledge patterns that facilitate the coding of knowledge expressed in a natural language and that can be used for other application domains.
Cite as

Zhuo Chen. Automating Disease Management Using Answer Set Programming. In Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016). Open Access Series in Informatics (OASIcs), Volume 52, pp. 22:1-22:10, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)

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@InProceedings{chen:OASIcs.ICLP.2016.22,
  author =	{Chen, Zhuo},
  title =	{{Automating Disease Management Using Answer Set Programming}},
  booktitle =	{Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016)},
  pages =	{22:1--22:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-007-1},
  ISSN =	{2190-6807},
  year =	{2016},
  volume =	{52},
  editor =	{Carro, Manuel and King, Andy and Saeedloei, Neda and De Vos, Marina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2016.22},
  URN =		{urn:nbn:de:0030-drops-67502},
  doi =		{10.4230/OASIcs.ICLP.2016.22},
  annote =	{Keywords: chronic disease management, knowledge pattern, answer set programming, knowledge representation, automated reasoning}
}
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