Spain

ECAI 2020

Fair in the eyes of others

envy-freeness

fair division

multiagent resource allocation

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Foreground detection by probabilistic mixture models using semantic information from deep networks

Exploiting Incomparability in Solution Dominance: Improving General Purpose Constraint-Based Mining

The Impact of Linguistic Knowledge in Different Strategies to Learn Cross-Lingual Distributional Models

Speeding up Word Mover’s Distance and its variants via properties of distances between embeddings

Learning First-Order Symbolic Representations from the Structure of the State Space

Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives

Basic Utility Theory for Belief Functions

Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation

Mid-weight Image Super-Resolution with Bypass Connection Attention Network

Learning to Reuse Translations: Guiding Neural Machine Translation with Examples

https://d3smihljt9218e.cloudfront.net/transcript/1047/subtitle/b090e0e9639cca8bf91b809e4519276e.vtt

English (automatic)

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Foreground detection by probabilistic mixture models using semantic information from deep networks

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Fair Allocation of Resources with Uncertain Availability

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Next from ECAI 2020

Foreground detection by probabilistic mixture models using semantic information from deep networks

Similar lecture

Fair Allocation of Resources with Uncertain Availability

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