Spain

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

ECAI 2020

Adaptive learning in multiagent systems for automated energy contacts negotiation

electricity markets

adaptive learning

multiagent systems

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

poster

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!

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.

Region Proposal Network for Lung Nodule Detection and Segmentation 

Improved forecasting of blood glucose could aid in the management of diabetes. Recently proposed neural network architectures that use stacked fully connected layers with residual backcasting have achieved state-of-the-art performance on benchmark time-series forecasting tasks. Though promising, previous work ignores opportunities for additional supervision, and the use of fully connected layers fails to account for the temporal nature of the signal. Here, we propose a new architecture that builds on previous work by learning to forecast gradually in stages or blocks. Our updates include replacing the fully connected block structure with a recurrent neural network and adding additional losses to provide auxiliary supervision. In addition, we leverage important context in the form of additional input signals. Applied to the task of glucose forecasting, we find that each of these modifications offers an improvement in performance. On the task of predicting blood glucose values 30 minutes into the future, our proposed approach achieves a mean rMSE of 18.2 mg/dL, versus 21.2 mg/dL achieved by the baseline. These improvements get us closer to predictions that are reliable enough to be used in CGMs or insulin pumps to manage diabetes.

Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction

"Artificial intelligence-driven Chatbot for Astronauts by
 taking advantage of Natural Language processing with the support of healthcare professionals/ doctors 
These Chatbots created with the expert advice of doctors can learn and evolve over a period of time and answer  medical questions from a pre-trained Database which can be updated periodically
 Astronauts would be able to list down their symptoms and the Chatbots could give medical support by suggesting some medicines or other remedies which helps in diagnosis and treatment of simple medical conditions without much delay
"

Adaptive learning in multiagent systems for automated energy contacts negotiation

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