The Doctoral Consortium will take place on July 18 and 19.
Chairs and organizers: Josep Puyol-Gruart, Jordi Sabater-Mir.
Every student will have 15 minutes to explain his/her PhD progress and plans and 10 minutes more to discuss with the committee's three members. Students should bear in mind that the level of the explanation has to be understood by an audience with knowledge in AI but not in the specific area of interest and should give particular importance to the impact that the PhD can potentially have. Presentations are to be done in English.
Possible presentation squeme:
The coffees and the lunch of the students and members of the committee will be free (students will have free lunch the day of his presentation).
After the presentations, there will be a consensus meeting of the committee members to prepare the final reports.
The schedule of the presentations is the following:
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.
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.
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.
Improving Optimization Algorithms with Deep Learning and Graphical Tools
Our research work is composed of two interconnected research areas.
Mindful AI for ART: Integrating expert knowledge to trainclinically coherent models for dose selection in IVF processes
Much like a lot of fields in healthcare, Assisted Reproduction generates lots of data that up until now has gone largely unused. Even after approximately 40 years of the first successful In Vitro Fertilization treatment and all the gigantic strides made to advance Assisted Reproduction Techniques until today, the chances of achieving pregnancy after In Vitro Fertilization remain around 30%. With the aim to improve that ratio, this research will focus on applying Artificial Intelligence and Machine Learning techniques to unfold the power of the high quantity of data generated by Assisted Reproduction Techniques. Specifically designing a modular Decision Support System aiming at improving efficacy after Controlled Ovarian Stimulation, and assessing pregnancy probabilities and multiple pregnancy risk after embryo transfer.
A Multi-scenario Approach to Continuously Learn and Understand Changes in Norm Violations
Using norms to guide and coordinate interactions has gained tremendous attention in the multiagent community. However, new challenges arise as the interest moves towards dynamic socio-technical systems, where human and software agents interact, and interactions are required to adapt to changing human needs. For instance, different agents (human or software) might not have the same understanding of what it means to violate a norm (e.g., what characterizes hate speech), or their understanding of a norm might change over time (e.g., what constitutes an acceptable response time). The challenge is to address these issues by learning to detect norm violations from the limited interaction data and to explain the reasons for such violations. To do that, we propose a framework that combines Machine Learning (ML) models and incremental learning techniques. Our proposal is equipped to solve tasks in both tabular and text classification scenarios. Incremental learning is used to continuously update the base ML models as interactions unfold, ensemble learning is used to handle the imbalance class distribution of the interaction stream, a transformer-based model is used to learn from text sentences, and Integrated Gradients (IG) and LIME are the interpretability algorithms. We evaluate the proposed approach in the use case of Wikipedia article edits, where interactions revolve around editing articles, and the norm in question is prohibiting vandalism. Results show that the proposed framework can learn to detect norm violation in a setting with data imbalance and concept drift.
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.
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.
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.
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.
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.
Elham Ali Rababa Rababah
Enhancing branch and bound with conflict-driven clause learning
The Boolean Satisfiability problem (SAT) is the problem of deciding whether a Boolean propositional formula can be satisfied. One of the most popular applications of SAT is its use as a logic-based formalism to express problems of industrial interest as a set of constraints that must be satisfied. The success of SAT as a problem-solving formalism is notorious due to the high efficiency of current SAT solvers, which let to obtain high quality solutions with competitive computation times, in spite of the extreme hardness of the tackled problems. The efficiency of SAT solvers can be mainly attributed to the use of clause learning techniques. Recent works have shown that we can make further use of clause learning by combining it with Branch and Bound in the particular case of the MaxSAT problem. In this thesis, we aim at improving and extending the use of Branch and Bound to the wider range of combinatorial problems. We start from the observation that Branch and Bound with clause learning is applicable to any problem that can be stated in terms of pseudo-Boolean (PB) constraints -a particular kind of arithmetic constraints. Therefore, the challenge that we face in this thesis is to obtain a practical benefit from this technique in different problems containing PB constraints, by developing efficient algorithms to be integrated into SAT solvers. We will identify scenarios where we can improve the state of the art in terms of required solving time thanks to the new approach. As first steps, we consider the family of scheduling problems, where PB constraints with favorable properties abound.
|15:00||Guillem Rodriguez Corominas|
Machine Learning Support for in-class Competence-based Learning
The main objective of the model presented here is to monitor the learning progress of each student, especially in the face-to-face context, guiding the teacher in the often described busy pace required by the practices of formative assessment. This is achieved by providing the necessary feedback to each student and by concentrating on-site observation on those students who need more support, through the paradigm known as the "teacher-as-a-sensor" (TaaS), the idea of which is to use the teacher's own specialised observation criteria to avoid introducing elements that violate the privacy of the students in the classroom.
|16:00||Borja Velasco Regulez|