Spain

ECAI 2020

New computational models for the Choquet Integral

capacity and core

multi-objective combinatorial optimization

computational social choice

preferences

choquet integral

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.

technical paper

What makes a Better Companion? Towards Social & Engaging Peer Learning

Transformer-based Argument Mining for Healthcare Applications

Lifted Heuristics for Timeline-based Planning

Macro Operator Synthesis for ADL Domains

Verification of Recurrent Neural Networks for Cognitive Tasks via Reachability Analysis

Reuse, Reduce and Recycle: Optimizing Reiter’s HS-Tree for Sequential Diagnosis

On Partial Multi-Task Learning

Toward Metrics for Differentiating Out-of-Distribution Sets

Planning for Compilation of a Quantum Algorithm for Graph Coloring

Document and Word Representations Generated by Graph Convolutional Network and BERT for Short Text Classification

https://d3smihljt9218e.cloudfront.net/transcript/1139/subtitle/65f0d07610177389dbb13aa63aa4aa5c.vtt

English (automatic)

New computational models for the Choquet Integral

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What makes a Better Companion? Towards Social & Engaging Peer Learning

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New computational models for the Choquet Integral

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Next from ECAI 2020

What makes a Better Companion? Towards Social & Engaging Peer Learning

Similar lecture

Analysing Irresolute Multiwinner Voting Rules via SAT solving

Downloads