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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.

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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)

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@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-dev.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.

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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)

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@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-dev.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.

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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)

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@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-dev.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}
}

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Pathfinding in Games

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Learning and Game AI

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08391 Group Summary – Mining for Social Serendipity

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