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Documents authored by Schaul, Tom

Document
DOI: 10.4230/DagRep.12.2.134
AI for the Social Good (Dagstuhl Seminar 22091)

Authors: Claudia Clopath, Ruben De Winne, and Tom Schaul

Published in: Dagstuhl Reports, Volume 12, Issue 2 (2022)

Abstract
Progress in the field of Artificial intelligence (AI) and machine learning (ML) has not slowed down in recent years. Long-standing challenges like Go have fallen and the technology has entered daily use via the vision, speech or translation capabilities in billions of smartphones. The pace of research progress shows no signs of slowing down, and demand for talent is unprecedented. AI for Social Good in general is trying to ensure that the social good does not become an afterthought, but that society benefits as a whole. In this Dagstuhl Seminar, which can be considered a follow-up edition of Dagstuhl Seminar 19082, we brought together AI and machine learning researchers with non-governmental organisations (NGOs), as they already pursue a social good goal, have rich domain knowledge, and vast networks with (non-)governmental actors in developing countries. Such collaborations benefit both sides: on the one hand, the new techniques can help with prediction, data analysis, modelling, or decision making. On the other hand, the NGOs' domains contain many non-standard conditions, like missing data, side-effects, or multiple competing objectives, all of which are fascinating research challenges in themselves. And of course, publication impact is substantially enhanced when a method has real-world impact. In this seminar, researchers and practitioners from diverse areas of machine learning joined stakeholders from a range of NGOs to spend a week together. We first pursued an improved understanding of each side’s challenges and established a common language, via presentations and discussion groups. Building on this foundation, we organised a hackathon around some existing technical questions within the NGOs to scope the applicability of AI methods and seed collaborations. Finally, we defined guidelines and next steps for future AI for Social Good initiatives.
Cite as

Claudia Clopath, Ruben De Winne, and Tom Schaul. AI for the Social Good (Dagstuhl Seminar 22091). In Dagstuhl Reports, Volume 12, Issue 2, pp. 134-142, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{clopath_et_al:DagRep.12.2.134,
  author =	{Clopath, Claudia and De Winne, Ruben and Schaul, Tom},
  title =	{{AI for the Social Good (Dagstuhl Seminar 22091)}},
  pages =	{134--142},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{2},
  editor =	{Clopath, Claudia and De Winne, Ruben and Schaul, Tom},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.134},
  URN =		{urn:nbn:de:0030-drops-169345},
  doi =		{10.4230/DagRep.12.2.134},
  annote =	{Keywords: Machine Learning, Artificial Intelligence, Social Good, NGO, sustainable development goals, Non-governmental organisation}
}
Document
DOI: 10.4230/DagRep.9.12.67
Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511)

Authors: Jialin Liu, Tom Schaul, Pieter Spronck, and Julian Togelius

Published in: Dagstuhl Reports, Volume 9, Issue 12 (2020)

Abstract
The 2016 success of Google DeepMind’s AlphaGo, which defeated the Go world champion, and its follow-up program AlphaZero, has sparked a renewed interest of the general public in computational game playing. Moreover, game AI researchers build upon these results to construct stronger game AI implementations. While there is high enthusiasm for the rapid advances to the state-of-the-art in game AI, most researchers realize that they do not suffice to solve many of the challenges in game AI which have been recognized for decades. The Dagstuhl Seminar 19511 "Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI" seminar was aimed at getting a clear view on the unsolved problems in game AI, determining which problems remain outside the reach of the state-of-the-art, and coming up with novel approaches to game AI construction to deal with these unsolved problems. This report documents the program and its outcomes.
Cite as

Jialin Liu, Tom Schaul, Pieter Spronck, and Julian Togelius. Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511). In Dagstuhl Reports, Volume 9, Issue 12, pp. 67-114, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{liu_et_al:DagRep.9.12.67,
  author =	{Liu, Jialin and Schaul, Tom and Spronck, Pieter and Togelius, Julian},
  title =	{{Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511)}},
  pages =	{67--114},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{12},
  editor =	{Liu, Jialin and Schaul, Tom 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/DagRep.9.12.67},
  URN =		{urn:nbn:de:0030-drops-120113},
  doi =		{10.4230/DagRep.9.12.67},
  annote =	{Keywords: artificial intelligence, computational intelligence, game theory, games, optimization}
}
Document
DOI: 10.4230/DagRep.9.2.111
AI for the Social Good (Dagstuhl Seminar 19082)

Authors: Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, and Tom Schaul

Published in: Dagstuhl Reports, Volume 9, Issue 2 (2019)

Abstract
Artificial intelligence (AI) and machine learning (ML) have made impressive progress in the last few years. Long-standing challenges like Go have fallen and the technology has entered daily use via the vision, speech or translation capabilities in billions of smartphones. The pace of research progress shows no signs of slowing down, and demand for talent is unprecedented. AI for Social Good in general is trying to ensure that the social good does not become an afterthought, but that society benefits as a whole. In this Dagstuhl seminar, we brought together AI and machine learning researchers with non-governmental organisations (NGOs), as they already pursue a social good goal, have rich domain knowledge, and vast networks with (non)-governmental actors in developing countries. Such collaborations benefit both sides: on the one hand, the new techniques can help with prediction, data analysis, modelling, or decision making. On the other hand, the NGOs' domains contain many non-standard conditions, like missing data, side-effects, or multiple competing objectives, all of which are fascinating research challenges in themselves. And of course, publication impact is substantially enhanced when a method has real-world impact. In this workshop, researchers and practitioners from diverse areas of machine learning joined stakeholders from a range of NGOs to spend a week together. We first pursued an improved understanding of each side's challenges and established a common language, via presentations and discussion groups. We identified ten key challenges for AI for Social Good initiatives. To make matters concrete, we organised a hackathon around some existing technical questions within the NGOs to scope the applicability of AI methods and seed collaborations. Finally, we defined guidelines and next steps for future AI for Social Good initiatives.
Cite as

Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, and Tom Schaul. AI for the Social Good (Dagstuhl Seminar 19082). In Dagstuhl Reports, Volume 9, Issue 2, pp. 111-122, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{clopath_et_al:DagRep.9.2.111,
  author =	{Clopath, Claudia and De Winne, Ruben and Khan, Mohammad Emtiyaz and Schaul, Tom},
  title =	{{AI for the Social Good (Dagstuhl Seminar 19082)}},
  pages =	{111--122},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{9},
  number =	{2},
  editor =	{Clopath, Claudia and De Winne, Ruben and Khan, Mohammad Emtiyaz and Schaul, Tom},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.2.111},
  URN =		{urn:nbn:de:0030-drops-108620},
  doi =		{10.4230/DagRep.9.2.111},
  annote =	{Keywords: Machine Learning, Artificial Intelligence, Social Good, NGO, sustainable development goals, Non-governmental organisation}
}
Document
DOI: 10.4230/DFU.Vol6.12191.77
General Video Game Playing

Authors: John Levine, Clare Bates Congdon, Marc Ebner, Graham Kendall, Simon M. Lucas, Risto Miikkulainen, Tom Schaul, and Tommy Thompson

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

Abstract
One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made, how the scoring system works, how the winner is declared, and so on). The AI player then has to work out how to play the game and how to win. In this work, we seek to extend the idea of General Game Playing into the realm of video games, thus forming the area of General Video Game Playing (GVGP). In GVGP, computational agents will be asked to play video games that they have not seen before. At the minimum, the agent will be given the current state of the world and told what actions are applicable. Every game tick the agent will have to decide on its action, and the state will be updated, taking into account the actions of the other agents in the game and the game physics. We envisage running a competition based on GVGP playing, using arcadestyle (e.g. similar to Atari 2600) games as our starting point. These games are rich enough to be a formidable challenge to a GVGP agent, without introducing unnecessary complexity. The competition that we envisage could have a number of tracks, based on the form of the state (frame buffer or object model) and whether or not a forward model of action execution is available. We propose that the existing Physical Travelling Salesman (PTSP) software could be extended for our purposes and that a variety of GVGP games could be created in this framework by AI and Games students and other developers. Beyond this, we envisage the development of a Video Game Description Language (VGDL) as a way of concisely specifying video games. For the competition, we see this as being an interesting challenge in terms of deliberative search, machine learning and transfer of existing knowledge into new domains.
Cite as

John Levine, Clare Bates Congdon, Marc Ebner, Graham Kendall, Simon M. Lucas, Risto Miikkulainen, Tom Schaul, and Tommy Thompson. General Video Game Playing. In Artificial and Computational Intelligence in Games. Dagstuhl Follow-Ups, Volume 6, pp. 77-83, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InCollection{levine_et_al:DFU.Vol6.12191.77,
  author =	{Levine, John and Congdon, Clare Bates and Ebner, Marc and Kendall, Graham and Lucas, Simon M. and Miikkulainen, Risto and Schaul, Tom and Thompson, Tommy},
  title =	{{General Video Game Playing}},
  booktitle =	{Artificial and Computational Intelligence in Games},
  pages =	{77--83},
  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.Vol6.12191.77},
  URN =		{urn:nbn:de:0030-drops-43374},
  doi =		{10.4230/DFU.Vol6.12191.77},
  annote =	{Keywords: Video games, artificial intelligence, artificial general intelligence}
}
Document
DOI: 10.4230/DFU.Vol6.12191.85
Towards a Video Game Description Language

Authors: Marc Ebner, John Levine, Simon M. Lucas, Tom Schaul, Tommy Thompson, and Julian Togelius

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

Abstract
This chapter is a direct follow-up to the chapter on General Video Game Playing (GVGP). As that group recognised the need to create a Video Game Description Language (VGDL), we formed a group to address that challenge and the results of that group is the current chapter. Unlike the VGDL envisioned in the previous chapter, the language envisioned here is not meant to be supplied to the game-playing agent for automatic reasoning; instead we argue that the agent should learn this from interaction with the system. The main purpose of the language proposed here is to be able to specify complete video games, so that they could be compiled with a special VGDL compiler. Implementing such a compiler could provide numerous opportunities; users could modify existing games very quickly, or have a library of existing implementations defined within the language (e.g. an Asteroids ship or a Mario avatar) that have pre-existing, parameterised behaviours that can be customised for the users specific purposes. Provided the language is fit for purpose, automatic game creation could be explored further through experimentation with machine learning algorithms, furthering research in game creation and design. In order for both of these perceived functions to be realised and to ensure it is suitable for a large user base we recognise that the language carries several key requirements. Not only must it be human-readable, but retain the capability to be both expressive and extensible whilst equally simple as it is general. In our preliminary discussions, we sought to define the key requirements and challenges in constructing a new VGDL that will become part of the GVGP process. From this we have proposed an initial design to the semantics of the language and the components required to define a given game. Furthermore, we applied this approach to represent classic games such as Space Invaders, Lunar Lander and Frogger in an attempt to identify potential problems that may come to light. Work is ongoing to realise the potential of the VGDL for the purposes of Procedural Content Generation, Automatic Game Design and Transfer Learning.
Cite as

Marc Ebner, John Levine, Simon M. Lucas, Tom Schaul, Tommy Thompson, and Julian Togelius. Towards a Video Game Description Language. In Artificial and Computational Intelligence in Games. Dagstuhl Follow-Ups, Volume 6, pp. 85-100, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InCollection{ebner_et_al:DFU.Vol6.12191.85,
  author =	{Ebner, Marc and Levine, John and Lucas, Simon M. and Schaul, Tom and Thompson, Tommy and Togelius, Julian},
  title =	{{Towards a Video Game Description Language}},
  booktitle =	{Artificial and Computational Intelligence in Games},
  pages =	{85--100},
  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.Vol6.12191.85},
  URN =		{urn:nbn:de:0030-drops-43385},
  doi =		{10.4230/DFU.Vol6.12191.85},
  annote =	{Keywords: Video games, description language, language construction}
}
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