4 Search Results for "Bauckhage, Christian"

Document
Practitioner Track
DOI: 10.4230/OASIcs.SAIA.2024.2
Trustworthy Generative AI for Financial Services (Practitioner Track)

Authors: Marc-André Zöller, Anastasiia Iurshina, and Ines Röder

Published in: OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024)

Abstract
This work introduces GFT EnterpriseGPT, a regulatory-compliant, trustworthy generative AI (GenAI) platform tailored for the financial services sector. We discuss the unique challenges of applying GenAI in highly regulated environments. In the financial sector data privacy, ethical considerations, and regulatory compliance are paramount. Our solution addresses these challenges through multi-level safeguards, including robust guardrails, privacy-preserving techniques, and grounding mechanisms. Robust guardrails prevent unsafe inputs and outputs, and privacy-preserving techniques reduce the need for data transmission to third-party providers. In contrast, grounding mechanisms ensure the accuracy and reliability of artificial intelligence (AI) generated content. By incorporating these measures, we propose a path forward for safely harnessing the transformative potential of GenAI in finance, ensuring reliability, transparency, and adherence to ethical and regulatory standards. We demonstrate the practical application of GFT EnterpriseGPT within a large-scale financial institution, where it successfully improves operational efficiency and compliance.
Cite as

Marc-André Zöller, Anastasiia Iurshina, and Ines Röder. Trustworthy Generative AI for Financial Services (Practitioner Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 2:1-2:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{zoller_et_al:OASIcs.SAIA.2024.2,
  author =	{Z\"{o}ller, Marc-Andr\'{e} and Iurshina, Anastasiia and R\"{o}der, Ines},
  title =	{{Trustworthy Generative AI for Financial Services}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{2:1--2:5},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-357-7},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{126},
  editor =	{G\"{o}rge, Rebekka and Haedecke, Elena and Poretschkin, Maximilian and Schmitz, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.2},
  URN =		{urn:nbn:de:0030-drops-227428},
  doi =		{10.4230/OASIcs.SAIA.2024.2},
  annote =	{Keywords: Generative AI, GenAI, Trustworthy AI, Finance, Guardrails, Grounding}
}
Document
DOI: 10.4230/DFU.Vol6.12191.21
Pathfinding in Games

Authors: Adi Botea, Bruno Bouzy, Michael Buro, Christian Bauckhage, and Dana Nau

Published in: Dagstuhl Follow-Ups, Volume 6, Artificial and Computational Intelligence in Games (2013)

Abstract
Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup.
Cite as

Adi Botea, Bruno Bouzy, Michael Buro, Christian Bauckhage, and Dana Nau. Pathfinding in Games. In Artificial and Computational Intelligence in Games. Dagstuhl Follow-Ups, Volume 6, pp. 21-31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InCollection{botea_et_al:DFU.Vol6.12191.21,
  author =	{Botea, Adi and Bouzy, Bruno and Buro, Michael and Bauckhage, Christian and Nau, Dana},
  title =	{{Pathfinding in Games}},
  booktitle =	{Artificial and Computational Intelligence in Games},
  pages =	{21--31},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-62-0},
  ISSN =	{1868-8977},
  year =	{2013},
  volume =	{6},
  editor =	{Lucas, Simon M. and Mateas, Michael and Preuss, Mike and Spronck, Pieter and Togelius, Julian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol6.12191.21},
  URN =		{urn:nbn:de:0030-drops-43334},
  doi =		{10.4230/DFU.Vol6.12191.21},
  annote =	{Keywords: path finding, search, games}
}
Document
DOI: 10.4230/DFU.Vol6.12191.33
Learning and Game AI

Authors: Hector Muñoz-Avila, Christian Bauckhage, Michal Bida, Clare Bates Congdon, and Graham Kendall

Published in: Dagstuhl Follow-Ups, Volume 6, Artificial and Computational Intelligence in Games (2013)

Abstract
The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games, car and motorcycle racing games, board games such as Go, an even traditional game-theoretic problems such as the prisoners dilemma. A common trait of these works is the potential of machine learning to reduce the burden of game developers. However a number of challenges exists that hinder the use of machine learning more broadly. We discuss some of these challenges while at the same time exploring opportunities for a wide use of machine learning in games.
Cite as

Hector Muñoz-Avila, Christian Bauckhage, Michal Bida, Clare Bates Congdon, and Graham Kendall. Learning and Game AI. In Artificial and Computational Intelligence in Games. Dagstuhl Follow-Ups, Volume 6, pp. 33-43, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InCollection{munozavila_et_al:DFU.Vol6.12191.33,
  author =	{Mu\~{n}oz-Avila, Hector and Bauckhage, Christian and Bida, Michal and Congdon, Clare Bates and Kendall, Graham},
  title =	{{Learning and Game AI}},
  booktitle =	{Artificial and Computational Intelligence in Games},
  pages =	{33--43},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-62-0},
  ISSN =	{1868-8977},
  year =	{2013},
  volume =	{6},
  editor =	{Lucas, Simon M. and Mateas, Michael and Preuss, Mike and Spronck, Pieter and Togelius, Julian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol6.12191.33},
  URN =		{urn:nbn:de:0030-drops-43348},
  doi =		{10.4230/DFU.Vol6.12191.33},
  annote =	{Keywords: Games, machine learning, artificial intelligence, computational intelligence}
}
Document
DOI: 10.4230/DagSemProc.08391.3
08391 Group Summary – Mining for Social Serendipity

Authors: Alexandre Passant, Ian Mulvany, Peter Mika, Nicolas Maisonneuve, Alexander Löser, Ciro Cattuto, Christian Bizer, Christian Bauckhage, and Harith Alani

Published in: Dagstuhl Seminar Proceedings, Volume 8391, Social Web Communities (2008)

Abstract
A common social problem at an event in which people do not personally know all of the other participants is the natural tendency for cliques to form and for discussions to mainly happen between people who already know each other. This limits the possibility for people to make interesting new acquaintances and acts as a retarding force in the creation of new links in the social web. Encouraging users to socialize with people they don't know by revealing to them hidden surprising links could help to improve the diversity of interactions at an event. The goal of this paper is to propose a method for detecting extit{"surprising"} relationships between people attending an event. By extit{"surprising"} relationship we mean those relationships that are not known a-priori, and that imply shared information not directly related with the local context of the event (location, interests, contacts) at which the meeting takes place. To demonstrate and test our concept we used the Flickr community. We focused on a community of users associated with a social event (a computer science conference) and represented in Flickr by means of a photo pool devoted to the event. We use Flickr metadata (tags) to mine for user similarity not related to the context of the event, as represented in the corresponding Flickr group. For example, we look for two group members who have been in the same highly specific place (identified by means of geo-tagged photos), but are not friends of each other and share no other common interests or, social neighborhood.
Cite as

Alexandre Passant, Ian Mulvany, Peter Mika, Nicolas Maisonneuve, Alexander Löser, Ciro Cattuto, Christian Bizer, Christian Bauckhage, and Harith Alani. 08391 Group Summary – Mining for Social Serendipity. In Social Web Communities. Dagstuhl Seminar Proceedings, Volume 8391, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

Copy BibTex To Clipboard

@InProceedings{passant_et_al:DagSemProc.08391.3,
  author =	{Passant, Alexandre and Mulvany, Ian and Mika, Peter and Maisonneuve, Nicolas and L\"{o}ser, Alexander and Cattuto, Ciro and Bizer, Christian and Bauckhage, Christian and Alani, Harith},
  title =	{{08391 Group Summary – Mining for Social Serendipity}},
  booktitle =	{Social Web Communities},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8391},
  editor =	{Harith Alani and Steffen Staab and Gerd Stumme},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08391.3},
  URN =		{urn:nbn:de:0030-drops-17910},
  doi =		{10.4230/DagSemProc.08391.3},
  annote =	{Keywords: Serendipity, online activity, context, ubiquitous computing}
}
  • Refine by Type
  • 4 Document/PDF
  • 1 Document/HTML

  • Refine by Publication Year
  • 1 2025
  • 2 2013
  • 1 2008

  • Refine by Author
  • 3 Bauckhage, Christian
  • 1 Alani, Harith
  • 1 Bida, Michal
  • 1 Bizer, Christian
  • 1 Botea, Adi
  • Show More...

  • Refine by Series/Journal
  • 1 OASIcs
  • 2 DFU
  • 1 DagSemProc

  • Refine by Classification
  • 1 Applied computing → Document management and text processing
  • 1 Applied computing → Online banking
  • 1 Computing methodologies → Artificial intelligence
  • 1 Human-centered computing → Collaborative and social computing

  • Refine by Keyword
  • 1 Finance
  • 1 Games
  • 1 GenAI
  • 1 Generative AI
  • 1 Grounding
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact