Anil Akbay Mehmet

Developing Efficient Routing Algorithms for Sustainable City Logistics

Increasing environmental concerns and legal regulations have led to the development of sustainable technologies and systems in logistics, as in many fields. The adoption of multi-echelon distribution networks and environmentally-friendly vehicles in freight distribution have become major concepts for reducing the negative impact of urban transportation activities. In this line, this thesis addresses a two-echelon electric vehicle routing problem (2E-EVRP) as a practice of sustainable city logistics. In the first echelon of the distribution network, products are transported from central warehouses to satellites located in the surroundings of cities. This is achieved by means of large conventional trucks. Subsequently, relatively smaller-sized electric vehicles distribute these products from the satellites to demand points/customers in the cities as they are less noisy and have zero direct emission. The problem addressed in this study takes into account the limited driving range of electric vehicles that need to be recharged in charging stations when necessary. Furthermore, we considered realistic charging scenarios such as partial recharging and non-linear battery charging time and observed their effect on the solution quality. In addition, the proposed problem considers some of the essential real-life constraints, i.e., time window and simultaneous pickup and delivery. A mixed-integer linear programming formulation is developed, and small-sized instances are solved using CPLEX. Due to the complexity of the problem, we have developed solution algorithms based on variable neighborhood search and construct, merge, solve and adapt to solve large-sized problem instances.

Georgios Athanasiou

Machine Learning Platform for Assisted Reproductive Technologies

In-vitro fertilization (IVF) is a domain that faces large problems in efficiency and efficacy, for human patients and mammals. So far, even though there are critical advancements in the applications of Artificial Intelligence (AI) and Machine Learning (ML) in Healthcare, but also more specifically in assistive reproduction, the use of these kinds of techniques remains low. The reasons lie in different factors, from the unsatisfactory accuracy rates to the lack of mathematical evidence for the results of some applications, and the mistrust of correctness by Healthcare scientists and professionals. The objectives of this research are the design and development of software, based on AI and ML techniques, to further support the research in assisted reproductive technologies (ART) and answer some of the existing doubts on the current applications. The contribution of this project involves (i) the design of new machine learning algorithms, (ii) the facilitation of the acquisition, analysis and validation of new knowledge through the analysis of ART data, and (iii) the introduction of new, proven AI methods for the evaluation of IVF.

Davide Audrito

Modeling the legal institutions of human and fundamental rights following a formal ontological approach

Over the last four decades, formal ontologies have represented well-rooted and effective knowledge modeling and management frameworks, which encounter the growing need to digitalize legal sources and promote interoperability. For this purpose, manifold research efforts have been put into the representation of legislation, policies, case law, administrative procedures, and further sources of legal nature, which cover manifold domains, including privacy, tenders and procurements, licenses, policies, and interdisciplinary areas. The will to pursue this research line originates from the almost complete absence, in the scientific literature, of ontologies engaged with the representation of human and fundamental rights, whose evolving nature in the digital era requires increasing in-depth analysis. Following a careful survey of the state of the art and the recognition of the most recent trends, this research effort aims to improve the semantic search for information retrieval by modeling the legal institutions concerned with the protection of human and fundamental rights. For this purpose, the dataset will include sources of conventional and constitutional nature by following a multilevel and multilingual approach. Starting from broader and generic categories of concepts, e.g. “President” or “Court”, the ontology will foster the understandability of human and fundamental rights oriented to manifold categories of users, and set an extensible framework at the crossroads of legal harmonization and interoperability.

Dimitra Anna Bourou Bourou

Camilo José Chacón Sartori

Núria Correa Mañas

Thiago Freitas Dos Santos

Athina Georgara

David Gomez Guillen

Improving simulation model calibration for Cost-Effectiveness Analysis via Bayesian methods

The use of mathematical simulation models of diseases in economic evaluation is an essential and common tool in medicine aimed at guiding decision-making in healthcare. Cost-effectiveness analyses are a type of economic evaluation that assess the balance between health benefits and the economic sustainability of different health interventions. One critical aspect of these models is the accurate representation of the disease's natural history, which requires a set of parameters such as probabilities and disease burden rates. While these parameters can be obtained from scientific literature, they often need calibration to fit the model's expected outcomes. However, the calibration process can be computationally expensive and traditional optimization methods can be time-consuming due to relatively simple heuristics that may not even guarantee feasible solutions. This thesis investigates the use of Bayesian optimization to enhance the calibration process by leveraging domain-specific knowledge and exploiting inherent structural properties in the solution space.

Andrea Guillén

Bjoern Komander

Roger Xavier Lera Leri

Explainability for optimisation-based decision support systems

In the last years there has been an increasing interest in developing Artificial Intelligence (AI) systems centered in humans that are trustworthy, meaning that they have to be ethical, lawful and robust. Within this new vision of AI, there is an strong consensus to require explainability in AI algorithms, i.e. the capacity to provide explanations of the decisions taken by such algorithms.
Hence, the goal of this PhD thesis is to develop decision support systems that not only recommend the optimal solutions for different real-world problems, but also to develop a general framework for explainable AI that provides explanations of the decisions taken by our approaches. We aim at formalising such problems as convex optimisation problems, enabling the use of commercial-off-the-shelf solvers to solve such problems, using real world data instances to evaluate our approaches.

Alejandra López de Aberasturi Gómez

Multi-Agent Reinforcement Models of Human Group Productivity in Educational Settings

We use the term "Collaborative Learning" to refer to educational approaches that involve the collective intellectual effort of students in a group. Students are organized into groups of three or more members to solve a problem, find answers to a question, search for meanings of concepts, or create a product. Each group member can work on inter-connected tasks contributing to a common overall outcome. They can also work in parallel on a shared assignment.
Given a group of students and a collaborative learning task, this thesis raises the question of how to use team modeling and simulations to facilitate the performance of human groups.
We propose that a well-conceived and trained multi-agent model can be used to make predictions about the performance of a specific group on a given task and that we can leverage this predictive capability to optimize and facilitate the work of human groups.
To do this, we understand the group as a set of individuals who interact with each other on the basis of a relationship of interdependence. We start with Steiner's group productivity model and task taxonomy. Plus, we adhere to the assumption that individuals act as maximizers of expected utility. This hypothesis opens the door to using reinforcement-trained agents as a descriptive (rather than prescriptive) model of a group of individuals within an interdependent relationship, each seeking to satisfy their own selfish goals.

Nieves Montes

Value Engineering for Autonomous Agents

Value engineering consists in the formulation, design and implementation of new value-wary functionalities for autonomous agents. In this thesis, we propose perspective-dependent value-based normative reasoning, which allows agents to reason about which norms and regulations are best aligned with respect to not only their values, but to the values they estimate that others in their community have. To achieve this objective, we start from Schwartz’s Theory of Basic Human Values and establish the consequentialist nature of the norm-value relationship. Then, we contribute the Action Situation Language, a novel norm representation language rooted in institutional analysis and with deep links to game theory. Last, we introduce Theory of Mind functionalities into an existing BDI autonomous agent architecture. Together, these three contributions are integrated in a novel functionality that enables autonomous agents to reason about prescriptive norms in a perspective-dependent manner, by switching its value system to the one it estimates that another agent has at runtime. Such perspective-dependent value-based normative reasoning functionality, with its inherent social orientation, constitutes a novel contribution to the community of values for autonomous agents.

Shuxian Pan

Automated Extraction of Online Community Norms

To develop cooperative multiagent systems effectively, we aim to create an architecture that facilitates the agents' dynamic adoption of conventions. It expands an existing agent model's action selection architecture with a component that uses Natural Language Processing techniques. This component embeds conventions into agent interaction strategies to improve the predictability of other agents' actions if all agents adopt the same conventions in their strategies.