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@comment{{Command line: bib2bib -c 'year=2004 and not annote:"skip_html" and not annote:"unpublished"' -ob peter-2004.bib -oc peter-2004.cite peter.bib}}
  author = {Peter Baumgartner and Anupam Mediratta},
  title = {{I}mproving {S}table {M}odels {B}ased {P}lanning by {B}idirectional {S}earch},
  booktitle = {International Conference on Knowledge Based Computer Systems (KBCS)},
  optcrossref = {},
  optkey = {},
  optpages = {},
  year = {2004},
  opteditor = {},
  optvolume = {},
  optnumber = {},
  optseries = {},
  address = {Hyderabad, India},
  month = {December},
  optorganization = {},
  optpublisher = {},
  url = {kbcs.pdf},
  abstract = {Solving AI planning problems by transformation into (normal)
  programs and computing answer sets (stable models) has gained
  considerable interest over the last years. We investigate in this
  context a classical AI search technique, bidirectional search, where
  search is performed both from the initial facts towards the goal and
  vice versa. Our contribution is to show how bidirectional search can
  be realized in the logic programming/answer set paradigm to
  planning. This seems not having been investigated so far.  We report
  on practical experiments on planning problems from an AIPS
  competition and show how our approach helps speeding up the planning
  process. We perceive our contribution mainly as a {\em technique\/} that
  is compatible with and complementary to existing extensions and
  improvements, rather than as a concrete planning system.}
  author = {Peter Baumgartner and Ulrich Furbach},
  title = {Living Books, Automated Deduction and other Strange Things},
  booktitle = {Mechanizing Mathematical Reasoning:  
                   Techniques, Tools and Applications -- 
                   Essays in honour of J{\"o}rg H. Siekmann},
  volume = {2605},
  series = {LNCS},
  pages = {255-274},
  publisher = {Springer-Verlag},
  year = {2004},
  editor = {Dieter Hutter and Werner Stephan},
  url = {}
  author = {Peter Baumgartner and Ulrich Furbach and Margret
		  Gross-Hardt and Alex Sinner},
  title = {{Living Book -- Deduction, Slicing, and Interaction}},
  journal = {Journal of Automated Reasoning},
  year = {2004},
  publisher = {Kluwer Academic Publishers},
  volume = {32},
  number = {3},
  pages = {259-286},
  era = {A},
  optmonth = {},
  abstract = {The Living Book is a system for the management of
		  personalized and scenario specific teaching material. The
		  main goal of the system is to support the active,
		  explorative and self-determined learning in lectures,
		  tutorials and self study. Living Book includes a course on
		  ``Logic for Computer Scientists'' with a uniform access to
		  various tools like theorem provers and an interactive
		  tableau editor. It is routinely used within teaching
		  undergraduate courses at our university. \par This paper
		  describes both, the Living Book together with its use of
		  theorem proving technology as a core component in the
		  knowledge management system (KMS), and the use of this new
		  concept in academic teaching. The KMS provides a {\em
		  scenario management\/} component where teachers may
		  describe those parts of given documents that are relevant
		  in order to achieve a certain learning goal. The task of
		  the KMS is to assemble new documents from a database of
		  elementary units called ``slices'' (definitions, theorems,
		  and so on) in a scenario-based way (like ``I want to
		  prepare for an exam and need to learn about resolution'').
		  \par The computation of such assemblies is carried out by a
		  model-generating theorem prover for first-order logic with
		  a default negation principle. Its input consists of meta
		  data that describe the dependencies between different
		  slices, and logic-programming style rules that describe the
		  scenario-specific composition of slices. Additionally,
		  users may assess what units they know or don't know. This
		  information is stored in a user model, which is taken into
		  account to compute a model that specifies the assembly of a
		  personalized document. \par This paper introduces the
		  e-learning context we are faced with, motivates our choice
		  of logic, sketches the newly developed calculus used in the
		  KMS. Finally, the application and evaluation of Living
		  Books is presented.}
  author = {Peter Baumgartner and Aljoscha Burchardt},
  title = {{L}ogic {P}rogramming {I}nfrastructure for {I}nferences on
  booktitle = {Logics in Artificial Intelligence, Ninth European
		  Conference, JELIA'04},
  optcrossref = {},
  optkey = {},
  pages = {591--603},
  year = {2004},
  editor = {Jos{\'e} Alferes and Jo{\~a}o Leite},
  volume = {3229},
  optnumber = {},
  series = {Lecture Notes in Artificial Intelligence},
  optaddress = {},
  optmonth = {},
  era = {A},
  optorganization = {},
  url = {jelia2004.pdf},
  publisher = {Springer Verlag, Berlin, Heidelberg, New-York},
  abstract = {The growing size of electronically available text corpora
		  like companies' intranets or the WWW has made
		  \emph{information access} a hot topic within Computational
		  Linguistics. Despite the success of statistical or keyword
		  based methods, deeper Knowledge Representation (KR)
		  techniques along with ``inference'' are often mentioned as
		  mandatory, e.g.\ within the Semantic Web context, to enable
		  e.g.\ better query answering based on ``semantical''
		  information. In this paper we try to contribute to the open
		  question how to operationalize semantic information on a
		  larger scale. As a basis we take the \emph{frame}
		  structures of the Berkeley FrameNet~II project, which is a
		  structured dictionary to explain the meaning of words from
		  a lexicographic perspective. Our main contribution is a
		  transformation of the FrameNet~II frames into the
		  \emph{answer set programming paradigm} of logic
		  programming. \par Because a number of different reasoning
		  tasks are subsumed under ``inference'' in the context of
		  natural language processing, we emphasize the flexibility
		  of our transformation. Together with methods for automatic
		  annotation of text documents with frame semantics which are
		  currently developed at various sites, we arrive at an
		  infrastructure that supports experimentation with semantic
		  information access as is currently demanded for.}
  author = {Peter Baumgartner and Ulrich Furbach and Margret
		  Gross-Hardt and Thomas Kleemann},
  title = {Model Based Deduction for Database Schema Reasoning},
  booktitle = {KI 2004: Advances in Artificial Intelligence},
  optcrossref = {},
  optkey = {},
  pages = {168--182},
  year = {2004},
  editor = {Susanne Biundo and Thom Fr{\"u}hwirth and G{\"u}nther Palm},
  volume = {3238},
  optnumber = {},
  optseries = {Lecture Notes in Computer Science},
  optaddress = {},
  optmonth = {},
  optorganization = {},
  url = {ki2004.pdf},
  publisher = {Springer Verlag, Berlin, Heidelberg, New-York},
  abstract = {We aim to demonstrate that automated deduction techniques,
		  in particular those following the model computation
		  paradigm, are very well suited for database schema/query
		  reasoning. Specifically, we present an approach to compute
		  completed paths for database or XPath queries. The database
		  schema and a query are transformed to disjunctive logic
		  programs with default negation, using a description logic
		  as an intermediate language. Our underlying deduction
		  system, {\em KRHyper\/}, then detects if a query is
		  satisfiable or not. In case of a satisfiable query, all
		  completed paths -- those that fulfill all given constraints
		  -- are returned as part of the computed models. \par The
		  purpose of computing completed paths is to reduce the
		  workload on a query processor. Without the path completion,
		  a usual XPath query processor would search the whole
		  database for solutions to the query, which need not be the
		  case when using completed paths instead. \par We understand
		  this paper as a first step, that covers a basic
		  schema/query reasoning task by model-based deduction. Due
		  to the underlying expressive logic formalism we expect our
		  approach to easily adapt to more sophisticated problem
		  settings, like type hierarchies as they evolve within the
		  XML world.}
  author = {Peter Baumgartner and Alexander Fuchs and Cesare Tinelli},
  title = {Darwin: A Theorem Prover for the Model Evolution
  booktitle = {Proceedings of the 1st Workshop on Empirically
                  Successful First Order Reasoning (ESFOR'04), Cork,
                  Ireland, 2004},
  optcrossref = {},
  optkey = {},
  optpages = {},
  year = {2004},
  url = {darwin.pdf},
  editor = {Stephan Schulz and Geoff Sutcliffe and Tanel Tammet},
  optvolume = {},
  optnumber = {},
  series = {Electronic Notes in Theoretical Computer Science},
  abstract = {Darwin is the first implementation of the Model Evolution
		  Calculus by Baumgartner and Tinelli. The Model Evolution
		  Calculus lifts the DPLL procedure to first-order logic.
		  Darwin is meant to be a fast and clean implementation of
		  the calculus, showing its effectiveness and providing a
		  base for further improvements and extensions. Based on a
		  brief summary of the Model Evolution Calculus, we describe
		  in the main part of the paper Darwin's proof procedure and
		  its data structures and algorithms, discussing the main
		  design decisions and features that influence Darwin's
		  performance. We also report on practical experiments
		  carried out with problems from the CADE-18 and CADE-19
		  system competitions, as well as on results on parts of the
		  TPTP problem library.},
  publisher = {Elsevier}
  author = {Peter Baumgartner and Barbara Grabowski and Walter Oevel
		  and Erica Melis},
  title = {{I}n2Math - {I}nteraktive {M}athematik- und
  journal = {Softwaretechnik-Trends},
  year = {2004},
  optkey = {},
  url = {},
  volume = {24},
  number = {1},
  pages = {36-45},
  optmonth = {},
  optnote = {},
  optannote = {}