3 Search Results for "Vu, Van"

Document
DOI: 10.4230/DagRep.12.6.112
Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262)

Authors: Wendy E. Mackay, John Shawe-Taylor, and Frank van Harmelen

Published in: Dagstuhl Reports, Volume 12, Issue 6 (2023)

Abstract
This report documents the program and the outcomes of Dagstuhl Perspectives Workshop 22262 "Human-Centered Artificial Intelligence". The goal of this Dagstuhl Perspectives Workshops is to provide the scientific and technological foundations for designing and deploying hybrid human-centered AI systems that work in partnership with human beings and that enhance human capabilities rather than replace human intelligence. Fundamentally new solutions are needed for core research problems in AI and human-computer interaction (HCI), especially to help people understand actions recommended or performed by AI systems and to facilitate meaningful interaction between humans and AI systems. Specific challenges include: learning complex world models; building effective and explainable machine learning systems; developing human-controllable intelligent systems; adapting AI systems to dynamic, open-ended real-world environments (in particular robots and autonomous systems); achieving in-depth understanding of humans and complex social contexts; and enabling self-reflection within AI systems.
Cite as

Wendy E. Mackay, John Shawe-Taylor, and Frank van Harmelen. Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262). In Dagstuhl Reports, Volume 12, Issue 6, pp. 112-117, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{mackay_et_al:DagRep.12.6.112,
  author =	{Mackay, Wendy E. and Shawe-Taylor, John and van Harmelen, Frank},
  title =	{{Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262)}},
  pages =	{112--117},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{6},
  editor =	{Mackay, Wendy E. and Shawe-Taylor, John and van Harmelen, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.6.112},
  URN =		{urn:nbn:de:0030-drops-174579},
  doi =		{10.4230/DagRep.12.6.112},
  annote =	{Keywords: Human-centered Artificial Intelligence, Human-Computer Interaction, Hybrid Intelligence}
}
Document
DOI: 10.4230/DagRep.11.8.11
Structure and Learning (Dagstuhl Seminar 21362)

Authors: Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, and Frank van Harmelen

Published in: Dagstuhl Reports, Volume 11, Issue 8 (2022)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 Year Roadmap for AI" at AAAI 2019. In this Dagstuhl seminar, we discussed related problems from an interdiscplinary perspective, in particular, Cognitive Science, Cognitive Psychology, Physics, Computational Humor, Linguistic, Machine Learning, and AI. This report overviews presentations and working groups during the seminar, and lists two open problems.
Cite as

Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, and Frank van Harmelen. Structure and Learning (Dagstuhl Seminar 21362). In Dagstuhl Reports, Volume 11, Issue 8, pp. 11-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{dong_et_al:DagRep.11.8.11,
  author =	{Dong, Tiansi and Rettinger, Achim and Tang, Jie and Tversky, Barbara and van Harmelen, Frank},
  title =	{{Structure and Learning (Dagstuhl Seminar 21362)}},
  pages =	{11--34},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{8},
  editor =	{Dong, Tiansi and Rettinger, Achim and Tang, Jie and Tversky, Barbara and van Harmelen, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.11.8.11},
  URN =		{urn:nbn:de:0030-drops-157670},
  doi =		{10.4230/DagRep.11.8.11},
  annote =	{Keywords: Knowledge graph, Machine learning, Neural-symbol unification}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2020.20
Reaching a Consensus on Random Networks: The Power of Few

Authors: Linh Tran and Van Vu

Published in: LIPIcs, Volume 176, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)

Abstract
A community of n individuals splits into two camps, Red and Blue. The individuals are connected by a social network, which influences their colors. Everyday, each person changes his/her color according to the majority of his/her neighbors. Red (Blue) wins if everyone in the community becomes Red (Blue) at some point. We study this process when the underlying network is the random Erdos-Renyi graph G(n, p). With a balanced initial state (n/2 persons in each camp), it is clear that each color wins with the same probability. Our study reveals that for any constants p and ε, there is a constant c such that if one camp has n/2 + c individuals at the initial state, then it wins with probability at least 1 - ε. The surprising fact here is that c does not depend on n, the population of the community. When p = 1/2 and ε = .1, one can set c = 6, meaning one camp has n/2 + 6 members initially. In other words, it takes only 6 extra people to win an election with overwhelming odds. We also generalize the result to p = p_n = o(1) in a separate paper.
Cite as

Linh Tran and Van Vu. Reaching a Consensus on Random Networks: The Power of Few. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 176, pp. 20:1-20:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{tran_et_al:LIPIcs.APPROX/RANDOM.2020.20,
  author =	{Tran, Linh and Vu, Van},
  title =	{{Reaching a Consensus on Random Networks: The Power of Few}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020)},
  pages =	{20:1--20:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-164-1},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{176},
  editor =	{Byrka, Jaros{\l}aw and Meka, Raghu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2020.20},
  URN =		{urn:nbn:de:0030-drops-126239},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2020.20},
  annote =	{Keywords: Random Graphs Majority Dynamics Consensus}
}
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