Spain

ECAI 2020

Maximal Information Propagation with Budgets

information propagation

mechanism design

social network

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

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.

On the Complexity of Constructive Control under Nearly Single-Peaked Preferences

RPN: A Residual Pooling Network for Efficient Federated Learning

Logic is responsible for scientific progress in many disciplines.  In particular, computer science and AI would be impossible  without it. Classical logics have long been accepted as a normative
 framework for human reasoning to capture correct reasoning. However,  many psychological findings such as the Wason Selection Task  have demonstrated that classical logic cannot serve as a possible  descriptive language for the human inference process, which is the   aim of what we call a cognitive logic. Recently, some nonmonotonic  logics have been employed to explain human inferences for some  well-known examples. In this paper, we discuss possible features of
 cognitive logics, present first results, and highlight new challenges.

Cognitive Logics: Features, Formalisms, and Challenges

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computation, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations

Adaptive learning in multiagent systems for automated energy contacts negotiation

In the past few years, several large companies have published ethical principles of Artificial Intelligence (AI). National governments, the European Commission, and inter-governmental organizations have come up with requirements to ensure the good use of AI. However, individual organizations that want to join this effort, are faced with many unsolved questions. This paper proposes guidelines for organizations committed to the responsible use of AI, but lack the required knowledge and experience. The guidelines consist of two parts: i) helping organizations to decide what principles to adopt, and ii) a methodology for implementing the principles in organizational processes. In case of future AI regulation, organizations following this approach will be well- prepared.

Towards organizational guidelines for the responsible use of AI

Robot self/other distinction: active inference meets neural networks learning in a mirror

In this video, we introduce through 8 questions the Human Rights approach to AI regulation and comment its superior societal performance compared to the regulation outlined in the European Commision white paper on AI. https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf  

A wide Human-Rights approach to Artificial Intelligence regulation in Europe

The modeling of a new generation of agent-based simulation systems supporting pedestrian and crowd management taking into account affective states represents a new research frontier. As in the case of any study of pedestrian dynamics, adding an affective component implies the rigorous design of experimental protocols and data acquisition sets. The integration of multi-modal signal sources considering both data coming from physical activity and uncontrolled reactions related to affective responses provides new perspectives to study pedestrian dynamics and pedestrian interaction with traditional vehicles as well as with autonomic and autonomous transportation systems. The designed in-vivo experimental protocol devoted to the collection of movement and physiological data as reliable stress indicators during walking and road crossing, and the related analysis will be illustrated.

Experimental approach to study pedestrian dynamics towards affective agents modeling

Artificial Intelligence (AI) is omnipresent today, but, what is AI capable of doing and where are its boundaries? Benchmarks, competitions and challenges are behind much of the recent progress in AI, but the dynamics of rushing breakthroughs at the expense of massive data and compute has led to a more complex AI landscape, in terms of what can be achieved and how. As a result, policy makers and other stakeholders have no way of assessing what AI systems can do today and in the future. In this paper, we identify a series of problems to track and understand what AI is capable of. Then we present the AIcollaboratory, a  data-driven framework to collect and explore data about AI results, progress and ultimately capabilities, which is being developed in the context of AI Watch, the European Commission knowledge service to monitor the development, uptake and impact of AI in Europe.

Tracking the Impact and Evolution of AI: TheAIcollaboratory

Lung nodule detection and segmentation play a critical role in detecting and determining the stage of lung cancer. This paper proposes a two-stage segmentation method which is capable of improving the accuracy of detecting and segmentation of lung nodules from 2D CT images. The first stage of our approach proposes multiple regions, potentially containing the tumour, and the second stage performs the pixel-level segmentation from the resultant regions. Moreover, we propose an adaptive weighting loss to effectively address the issue of class imbalance in lung CT image segmentation. We evaluate our proposed solution on a widely adopted benchmark dataset of LIDC. We have achieved a promising result of 92.78% for average DCS that puts our method among the top lung nodule segmentation methods.

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Next from ECAI 2020

On the Complexity of Constructive Control under Nearly Single-Peaked Preferences

Similar lecture

Structure-aware Propagation Generation with Large Language Models for Fake News Detection

Stay up to date with the latest Underline news!

PRESENTATIONS

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COMPANY

RESOURCES

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