Spain

&quot;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.&quot;

ECAI 2020

SIAMES - Social Impact Advisor and Measurement System

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
<br><br>
* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
<br><br>
* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
<br><br>

#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

"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."

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.

"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.

Adaptable and Verifiable BDI Reasoning

The main purpose of this work is to explore and investigate the main challenges that arise when we want to handle percepts and control the actuators of a robot with agent-oriented abstractions. In particular, the presentation focuses on how to program an autonomous robot with Jadescript.

Exploratory Experiments on Programming Autonomous Robots in Jadescript

Robots are soon going to be deployed in non-industrial environments. Before society can take such a step, it is necessary to endow complex robotic systems with mechanisms that make them reliable enough to operate in situations where the human factor is predominant. This calls for the development of robotic frameworks that can soundly guarantee that a collection of properties are verified at all times during operation. While developing a mission plan, robots should take into account factors such as human physiology. In this paper, we present an example of how a robotic application that involves human interaction can be modeled through hybrid automata, and analyzed by using statistical model-checking. We exploit statistical techniques to determine the probability with which some properties are verified, thus easing the state-space explosion problem. The analysis is performed using the Uppaal tool. In addition, we used Uppaal to run simulations that allowed us to show non-trivial time dynamics that describe the behavior of the real system, including human-related variables. Overall, this process allows developers to gain useful insights into their application and to make decisions about how to improve it to balance efficiency and user satisfaction.

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