A well-known knowledge acquisition method in the field of Formal Concept Analysis (FCA) is attribute exploration. It is used to reveal dependencies in a set of attributes with help of a domain expert. In most applications no single expert is capable (time- and knowledge-wise) of exploring the knowledge domain alone. However, there is up to now no theory that models the interaction of multiple experts for the task of attribute exploration with incomplete knowledge. To this end, we to develop a theoretical framework that allows multiple experts to explore domains together. We use a representation of incomplete knowledge as three-valued contexts. We then adapt the corresponding version of attribute exploration to fit the setting of multiple experts. We suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. In particular, we define an order that allows to compare the results of different exploration strategies on the same task with respect to their information completeness. Furthermore we discuss other ways of comparing collaboration strategies and suggest avenues for future research.
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement
University of Notre Dame（圣母大学）；University of California, Irvine（加州大学欧文分校）；Georgia Institute of Technology（佐治亚理工学院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Washington（华盛顿大学）；IBM Research（IBM研究院）
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
Microsoft Research（微软研究院）；University of Amsterdam（阿姆斯特丹大学）；Alexa AI（Alexa人工智能）；MIPT（莫斯科物理技术学院）；AIRI（人工智能研究院）；Facebook AI（脸书人工智能）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Kalashnikov Izhevsk State Technical University（卡拉什尼科夫伊热夫斯克国立技术大学）；Universiteit Leiden（莱顿大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
人类智能具有快速适应新任务和新环境的能力。从很小的时候开始，人类通过模仿他人的行为或遵循提供的自然语言指令来获得新技能并学习如何解决新任务。为了促进这一方向的研究，我们提出了\emph{IGLU: Interactive Grounded Language Understanding in a Collaborative Environment}。比赛的主要目标是探讨如何建立交互式代理，学习解决任务，同时提供接地自然语言指令在协作环境中的问题。了解挑战的复杂性，我们将其分解为子任务，使其对参与者可行。
Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication
Institute of Neurosciences of the University of Barcelona Spain；Artificial Intelligence Research Institute (IIIA-CSIC) Barcelona Spain；Massachusetts Institute of Technology Cambridge USA；ESPRONCEDA Institute of Art \& Culture Barcelona Spain；Institute of Neurosciences of the University of；Artificial Intelligence Research Institute (IIIA-CSIC)；Massachusetts Institute of Technology；ESPRONCEDA Institute of Art \& Culture
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Computer Engineering Department, İstanbul Commerce University（伊斯坦布尔商业大学计算机工程系）；Turkcell Teknoloji（Turkcell技术公司）；Grid Communications and Security Group, Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory（橡树岭国家实验室电网安全与能源基础设施部门）
针对联邦学习中的噪声标签、类别缺失与不均衡问题，提出结合自适应噪声清洗、条件 GAN 合成补全和鲁棒训练的隐私保护框架，并在 MNIST 与 Fashion-MNIST 上验证其性能提升。
Benchmarking Interaction, Beyond Policy: a Reproducible Benchmark for Collaborative Instance Object Navigation
Sapienza University of Rome（罗马大学）；University of Verona（维罗纳大学）；Polytechnic of Turin（都灵理工大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Reykjavik University（雷克雅未克大学）
