Spain

ECAI 2020

A Fuzzy Inference System for a Visually Grounded Robot State of Mind

object recognition

human-robot interaction

visual grounding

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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.

Neural Network Heuristics for Classical Planning: A Study of Hyperparameter Space

Maximal Information Propagation with Budgets

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.

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Neural Network Heuristics for Classical Planning: A Study of Hyperparameter Space

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Next from ECAI 2020

Neural Network Heuristics for Classical Planning: A Study of Hyperparameter Space

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Median-shape Representation Learning for Category-level Object Pose Estimation in Cluttered Environments

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