2 Search Results for "Wellman, Michael"

Document
DOI: 10.4230/DagMan.10.1.1
Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)

Authors: James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter

Published in: Dagstuhl Manifestos, Volume 10, Issue 1 (2024)

Abstract
Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022,sser a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade.
Cite as

James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter. Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282). In Dagstuhl Manifestos, Volume 10, Issue 1, pp. 1-61, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{delgrande_et_al:DagMan.10.1.1,
  author =	{Delgrande, James P. and Glimm, Birte and Meyer, Thomas and Truszczynski, Miroslaw and Wolter, Frank},
  title =	{{Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)}},
  pages =	{1--61},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2024},
  volume =	{10},
  number =	{1},
  editor =	{Delgrande, James P. and Glimm, Birte and Meyer, Thomas and Truszczynski, Miroslaw and Wolter, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.10.1.1},
  URN =		{urn:nbn:de:0030-drops-201403},
  doi =		{10.4230/DagMan.10.1.1},
  annote =	{Keywords: Knowledge representation and reasoning, Applications of logics, Declarative representations, Formal logic}
}
Document
DOI: 10.4230/DagSemProc.05011.14
Self-Confirming Price Prediction for Bidding in Simultaneous Ascending Auctions

Authors: Anna Osepayshvili, Michael Wellman, Daniel Reeves, and Jeffrey MacKie-Mason

Published in: Dagstuhl Seminar Proceedings, Volume 5011, Computing and Markets (2005)

Abstract
Simultaneous, separate ascending auctions are ubiquitous, even when agents have preferences over combinations of goods, from which arises the emph{exposure problem}. Little is known about strategies that perform well when the exposure problem is important. We present a new family of bidding strategies for this situation, in which agents form and utilize various amounts of information from predictions of the distribution of final prices. The predictor strategies we define differ in their choice of method for generating the initial (pre-auction) prediction. We explore several methods, but focus on emph{self-confirming} predictions. An agents prediction of characteristics of the distribution of closing prices is self-confirming if, when all agents follow the same predictor bidding strategy, the final price distributions that actually result are consistent with the utilized characteristics of the prediction. We extensively analyze an auction environment with five goods, and five agents who each can choose from 53 different bidding strategies (resulting in over 4.2 million distinct strategy combinations). We find that the self-confirming distribution predictor is a highly stable, pure-strategy Nash equilibrium. We have been unable to find any other Nash strategies in this environment. In limited experiments in other environments the self-confirming distribution predictor consistently performs well, but is not generally a pure-strategy Nash equilibrium.
Cite as

Anna Osepayshvili, Michael Wellman, Daniel Reeves, and Jeffrey MacKie-Mason. Self-Confirming Price Prediction for Bidding in Simultaneous Ascending Auctions. In Computing and Markets. Dagstuhl Seminar Proceedings, Volume 5011, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{osepayshvili_et_al:DagSemProc.05011.14,
  author =	{Osepayshvili, Anna and Wellman, Michael and Reeves, Daniel and MacKie-Mason, Jeffrey},
  title =	{{Self-Confirming Price Prediction for Bidding in Simultaneous Ascending Auctions}},
  booktitle =	{Computing and Markets},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5011},
  editor =	{Daniel Lehmann and Rudolf M\"{u}ller and Tuomas Sandholm},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05011.14},
  URN =		{urn:nbn:de:0030-drops-2020},
  doi =		{10.4230/DagSemProc.05011.14},
  annote =	{Keywords: compact representation of games, congestion games, local-effect games, action-graph gamescomputational markets; auctions; bidding strategies}
}
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