Spain

Information is key during a crisis such as the current COVID-19 pandemic as it greatly shapes people’s opinion, behaviour and even their psychological state. It has been acknowledged from the Secretary-General of the United Nations that the infodemic of misinformation is an important secondary crisis produced by the pandemic. Infodemics can amplify the real negative consequences of the pandemic in different dimensions: social, economic and even sanitary. For instance, infodemics can lead to hatred between population groups that fragment the society influencing its response or result in negative habits that help the pandemic propagate. On the contrary, reliable and trustful information along with messages of hope and solidarity can be used to control the pandemic, build safety nets and help promote resilience and antifragility. We propose a framework to characterize leaders in Twitter based on the analysis of the social graph derived from the activity in this social network. Centrality metrics are used to identify relevant nodes that are further characterized in terms of users’ parameters managed by Twitter. We then assess the resulting topology of clusters of leaders. Although this tool may be used for surveillance of individuals, we propose it as the basis for a constructive application to empower users with a positive influence in the collective behaviour of the network and the propagation of information.

ECAI 2020

Characterizing social leaders on Twitter during COVID-19 crisis 

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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.

"In this short article, we argue that the SDGs should figure more prominently in marking out the route to realising the potential of A/IS, while at the same time recognizing the need for a critical appraisal. We discuss how increasing awareness of an interdisciplinary research field “A/IS for development”
(A/IS4D) could contribute to achieving this, where by this term, we refer to a field that deals with the distinctive challenges posed by
A/IS technologies, particularly for more vulnerable and marginalized populations, while integrating a development studies, human-rights
centered perspective. We contend that such a perspective, and more specifically, the measuring of impact in terms of appropriate SDGs, is critical
to avoiding possible adverse effects arising from the introduction of such a disruptive technology without sufficient reflection, a real and
present danger given the current techno-optimist zeitgeist and the enormous power of the Information and Communication Technologies (ICT)
corporate sector. Unfortunately, the academic research literature of this field, like that of the more general and well-established ICT4D field, remains marginal, with contributions having little representation
in high-impact conferences and journals. This short article constitutes a modest attempt to contribute to the dissemination of this important
topic among researchers and professionals in the A/IS sector, in the way of a call for action.  "

AI4Eq: for a True Global Village not for Global Pillage

"In this article, we report on preliminary research on the
potential contributions of Artificial Intelligence to measure the social impact of social enterprises. We describe SIAMES, a recommender system of social impact indicators. The system has two phases: First, it tries to extract structured information from a corpus of impact measurement reports through ontology-based Semantic Text Mining.
Second, drawing inspiration from Case-Based Reasoning, it retrieves the most appropriate indicators for each social enterprise. In this brief
work, we outline the system and indicate the lines of research carried out to date. We also discuss some preliminary results and present the
planned next steps."

SIAMES - Social Impact Advisor and Measurement System

"The transformation to the Digital Society presents a challenge to engineer ever more complex socio-technical and cyber-physical systems. However, it is essential that these systems are designed with respect not just to conventional functional and non-functional requirements, but also with respect to satisfying qualitative human values, and assessing their impact on global challenges, such as those expressed
by the UN sustainable development goals (SDGs). In this presentation, we present a set of design principles and an associated meta-platform which formalise and codify deep social knowledge, as a basis for solving wicked societal problems and for contributing to the achievement of SDGs. The ideas are illustrated through the design of a collective action app, for plastic re-use and reducing plastic waste, called Urban Refill.  As with the inter-connectedness of the SDGs, we conclude by arguing that the inter-connectedness of the Digital Society implies that system development cannot be undertaken in isolation from other systems.
"

No App is an Island: Collective Action and Sustainable Development Goal-Sensitive Design

"Nowadays, the buildings have an important role in the high demand for electrical energy. Therefore, the energy management of the buildings may have significant influence on reducing the electricity consumption. Moreover, Electric Vehicles (EVs) have been considering as a power storage device in Smart Buildings (SBs)
aiming to reduce the cost and consuming energy. Here, an energy management framework is proposed in which by considering the flexibility of the contracted power of each apartment, an optimal charging-discharging scheduled for EVs and Battery Energy Storage System (BESS) is defined over a long time period to minimize the electricity cost of the building. The proposed model is designed by
a Mixed Binary Linear Programming formulation (MBLP) that the charging and discharging of EVs as well as BESS in each period is treated as binary decision variables. In order to validate the model, a case study involving three scenarios are considered. The obtained results indicate a 15% reduction in total electricity consumption cost and consumption energy by the grid over one year. Finally, the impact of capacity and charge/discharge rate of BESS on the power cost is analyzed and the optimal size of the BESS for assumed SB in the case study is also reported."

Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix BinaryLinear Programming model 

Power grid data are going “big” with the deployment of various sensors. The big data in power grids create huge opportunities for applying artificial intelligence technologies to improve resilience and reliability. This presentation introduces multiple real-world applications based on artificial intelligence and big data to improve power grid situational awareness and resilience. These applications include event detection, inertia estimation, event location and magnitude estimation, data authentication, control, and stability assessment. These applications are operating on a real-world system called FNET/GridEye, which is a wide-area measurement network and arguably the world-largest cyber-physical system that collects power grid big data. These applications showed much better performance compared with conventional approaches, and accomplished new tasks that are impossible to realized using conventional technologies. These encouraging results demonstrate that combining power grid big data and artificial intelligence can uncover and capture the non-linear correlation between power grid data and its stability indices, and will potentially enable many advanced applications that can significantly improve power grid resilience. 

Build Smart Grids on Big Data and Artificial Intelligence — A Real-world Example 

"Utility companies have a great interest in identifying fraudulent
activity such as Non-Technical Losses (NTL). Typically, they resort
to machine learning solutions in order to automate the identifica-
tion of such losses. In this paper, we extend and improve an NTL-
detection framework: by including knowledge-based NTL segmen-
tation, we are able to not only provide more accurate predictions but
also to include better explanations for them. Our improved models
focus on specific types of NTL and, therefore, the explanations pro-
vided are easier to interpret, allowing the Stakeholders to make more
informed decisions. Our improvements and results may benefit other
industrial frameworks."

Knowledge-Based Segmentation to Improve Accuracy and Explainability in Non-Technical Losses Detection

Buildings' energy consumption has dramatically increased over the last decade, accounting for more than 35\% of global energy use. In this article, RESPOND's approach to improve building operation is shown for reducing energy demand peaks. Namely, RESPOND develops an Artificial Intelligent system to dispatch optimal Demand Response events maximizing the renewable energy generation and leveraging energy storage units. Furthermore, with views to ensuring dwellers' engagement with the project, the proposed DR events are aimed to generate the least disturbance in their every-day activities.

An Artificial Intelligent System for Demand Response in Neighbourhoods

Milgram's reality-virtuality continuum applies to interaction in the physical space dimension, going from real to virtual. However, interaction has a social dimension as well, that can go from real to artificial depending on the companion with whom the user interacts. In this paper we present our vision of the Reality-Artificiality bidimensional Continuum (RAC), we identify some challenges in its design and development and we discuss how reliable interactions might be supported inside RAC.

Engineering Reliable Interactions in the Reality-Artificiality Continuum

"Robots are often so complex that one person may not know all the ins and outs of the system. Inheriting software and hardware infrastructure with limited documentation and/or practical robot experience presents a costly challenge for an engineer or researcher.  The choice is to either re-build existing systems, or invest in learning the existing framework. No matter the choice, a reliable system which produces expected outcomes is necessary, and while rebuilding may at first appear easier than learning the system, future users will be faced with the same choice. This paper provides a method to allow for increased documentation of the robotic system, which in turn can be used to contribute in overall robot reliability.
To do this we propose the identification of a robot's core behaviors for use in Capability Analysis Tables (CATs).  CATs are a form of tabular documentation that connect the hardware and software inputs and outputs to the robot's core behaviors.  Unlike existing methods, CATs are flexible, easy to build, and understandable by non-expert robot users. We demonstrate this documentation method with an experimental example using an Unmanned Aerial Vehicle (UAV). "

Establishing Reliable Robot Behavior using Capability Analysis Tables

Long-term autonomy requires autonomous systems to adapt as their capabilities no longer perform as expected. To achieve this, a system must first be capable of detecting such changes. In this position paper presentation, we describe a system architecture for BDI autonomous agents capable of adapting to changes in a dynamic environment and outline the required research.

Characterizing social leaders on Twitter during COVID-19 crisis

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Characterizing social leaders on Twitter during COVID-19 crisis

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AI4Eq: for a True Global Village not for Global Pillage

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Autonomous Flight Arcade Challenge: Single- and Multi-Agent Learning Environments for Aerial Vehicles

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