Spain

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* .

ECAI 2020

Reasoning about Quality and Fuzziness of Strategic Behaviours

tree languages.

automata over infinite objects

modal and temporal logics

logic and verification

verification by model checking

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.

poster

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

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. 


Frontiers in AI talk: People-Centric Language Computing

Frontiers in AI talk: Representation Learning and Synthesis for Generalized Planning

AI on a Beautiful Island: Building the Future of Energy Networks

The pace at which AI theory is being delivered to application has accelerated in the last decade, creating some impressive value in some areas (e.g., health and legal informatics, manufacturing, supply chain management), but raising warning flags about trust and ethics.

Both the promise and the challenges are evident in the application of AI to automotives and automated autonomous systems, especially in the choice of technologies, tradeoffs in where intelligence is required (i.e., the autonomous system or the infrastructure), and the emerging role of explainable AI, both in improving the transparency, trust, and robustness of systems, and in informing social systems and regulators about how to confirm their safety.

We try to highlight salient aspects of these challenges, and provide some context for helping to manage the translation of AI theory to application.

Frontiers in AI talk: At the frontier of AI application: balancing technology push and application pull

One of the most significant developments in the area of design verification over the last three decade is the development of algorithmic methods for verifying temporal specification of finite-state designs. A frequent criticism against this approach, however, is that verification is done after significant resources have already been invested in the development of the design. Since designs invariably contains errors, verification simply becomes part of the debugging process. The critics argue that the desired goal is to use temporal specification in the design development process in order to guarantee the development of correct designs. This is called temporal synthesis. In this talk I will review 60 years of research on the temporal synthesis problem, describe the automata-theoretic approach developed to solve this problem, and describe both successes and failures of this research program.

Reasoning about Quality and Fuzziness of Strategic Behaviours

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