Spain

ECAI 2020

Towards Human-Based Models of Behaviour in Social Robots: Exploring Age-Related Differences in the Processing of Gaze Cues in Human-Robot Interaction

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.

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.

String constraint solving is an important emerging field, given the ubiquity of strings over different fields such as formal analysis, automated testing, database query processing, and cybersecurity. This paper highlights the current state-of-the-art for string constraint solving, and identifies future challenges in this field.

String constraint solving: past, present and future

A Machine Learning Approach to Boredom Detection using Smartphone's Sensors

We introduce and study SL[F] — a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours. The satisfaction value of an SL[F] formula is a real value in [0, 1], reflecting “how much” or “how well” the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of SL[F] in quantitative reasoning about multi-agent systems, by showing how it can express concepts of stability in multi-agent systems, and how it generalises some fuzzy temporal logics. We also provide a model-checking algorithm for our logic, based on a quantitative extension of Quantified CTL* .

Reasoning about Quality and Fuzziness of Strategic Behaviours

We consider the problem of locating a single facility on a vertex in a given graph based on agents’ preferences, where the domain of the preferences is either single-peaked or single-dipped, depending on whether they want to access the facility (a public good) or be far from it (a public bad). Our main interest is the existence of deterministic social choice functions that are Pareto efficient and false-name-proof, i.e., resistant to fake votes. We show that regardless of whether preferences are single-peaked or single-dipped, such a social choice function exists (i) for any tree graph, and (ii) for a cycle graph if and only if its length is less than six. We also show that when the preferences are single-peaked, such a social choice function exists for any ladder (i.e., 2*m grid) graph, and does not exist for any larger (hyper)grid.

False-Name-Proof Facility Location on Discrete Structures

This paper studies minimum cost spanning tree (MCST) problems, in which an agent can behave as multiple agents by adding fake accounts. Since such split manipulations may increase the cost of MCST, it is important to (i) design a cost allocation rule under which no agent has an incentive to split her accounts, and (ii) analyze the resistance of the existing cost allocation rules against split manipulations. We first show that there exists no cost allocation rule that is both efficient and split-proof under the general domain. We then focus on the MCST problems with monotonic weight functions and show that there exists a cost allocation rule that is efficient, core-selecting, and split-proof. We finally analyze the resistance of the Bird rule, one of the most studied cost allocation rules in the literature, against split manipulations from three different perspectives: the mixed price of anarchy, the computational difficulty of manipulation, and domain restrictions.

Split Manipulations in Cost Sharing of Minimum Cost Spanning Tree

The amount of satellite imagery data has been substantially growing since the start of the SENTINEL mission. However, applications of AI to EO data are still scarce. The main goal of the AiTLAS project is to facilitate the uptake of EO data by AI experts and vice versa - the uptake of (advanced) AI methods by EO experts. The main focus of AiTLAS is the development of a comprehensive toolbox with resources such as: benchmarking tools, ready-to-exploit models, tools for learning models de novo, and semantically annotated datasets prepared in a format that is easy to use by AI methods. In this talk, we will illustrate the early developments of the AiTLAS project on a case study of learning deep models from satellite images scarcely labelled with ground-based land-use data. In particular, using large amounts of unlabelled satellite images, we will first learn (pre-train) in an unsupervised manner a deep model capable of extracting visual features, and then use a substantially smaller amount of labelled data for fine-tuning the model in a supervised manner. We will show that this approach has a strong competitive advantage as compared to using only labeled data for supervised learning of deep models.

AiTLAS: A toolbox of AI methods tailored for Earth Observation data

The transition towards an electricity system with a growing presence of non-programmable Renewable Energy Sources (RESs) systems requires not only the implementation of modern control systems and equipment, able to manage bidirectional power flows according to the Smart Grid paradigm, but also the adoption of novel distribution electrical system structures and/or new frameworks for the energy management and the electricity market scenarios, where resources considered up to now unable to provide ancillary services may participate as new flexible Distributed Energy Resources (DERs) that the power system requires, including non-programmable renewable sources, and demand. A Smart Grid integrates the actions of all actors connected to it, such as generators, suppliers, aggregators, consumers, in order to efficiently deliver sustainable, economic and secure electricity supplies. It uses information and communication technology on top of the electric network to improve the reliability, security, and efficiency (both economic and energy) of the electric system. To achieve this, it is also required the deployment of sensors and smart metering for collecting data across the grid from DERs and storage systems at any point of the network, especially integration at the consumer/prosumer level. Thus, it creates the opportunity for any consumer/prosumer on the grid to become an energy/power supplier and take part in energy trading mechanism. This is the key for consumers to be converted into “active energy citizens”. An essential role is played by Demand Response which denotes variations of the electric consumption by users in response to the energy price changes over the time, or in the presence of financial incentives and reliability signals.

An IoT Architecture for a Virtual Power Plant Aggregator

Language is not only about the words, it is also about the people. While much of the work in computational linguistics has focused almost exclusively on words (and their relations), recent research in the emerging field of computational sociolinguistics has shown that we can effectively leverage the close interplay between language and people. In this talk, I will explore this interaction, and show (1) that we can develop cross-cultural language models to identify words that are used in significantly different ways by speakers from different cultures; and (2) that we can effectively use information about the people behind the words to build better language representations. 


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