This book contains the proceedings of the 17th International Conference on Agents and Artificial Intelligence. This year, ICAART is held in Porto, Portugal, on February 23-25, 2025. As usual it is sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC). ICAART 2025 was also organized in cooperation with other members of our AI family. We mention the ACM Special Interest Group on Artificial Intelligence, the Association for the Advancement of Artificial Intelligence, the Associação Portuguesa de Reconhecimento de Padrões, the Portuguese Association for Artificial Intelligence, the IberoAmerican Society of Artificial Intelligence and the European Society for Fuzzy Logic and Technology. The purpose of the International Conference on Agents and Artificial Intelligence is to bring together researchers, engineers and practitioners interested in the theory and applications in the areas of Agents and Artificial Intelligence, covering both applications and current (advanced) research work. On one side it focuses on Agents, Multi-Agent Systems and Software Platforms, and also Distributed Problem Solving. On the other side it focuses on Artificial Intelligence, Knowledge Representation, Planning, Learning, Scheduling, Perception. Applications are in both areas. They are using Natural Language Processing (NLP), Large Language Models (LLMs), Legal Technologies and Quantum Computing. In the last four years the research emphasis has shifted towards Explainable AI and Interpretable AI with a focus on trustworthiness, fairness, privacy, safety, security and ethical issues. A substantial amount of research work is ongoing in these knowledge areas, in an attempt to discover appropriate theories and paradigms for use in real-world applications. ICAART 2025 received 472 paper submissions from 53 countries of which 23.09% were accepted and published as full papers. A double-blind paper review was performed for each submission by at least 2 but usually 3 or more members of the International Program Committee, which is composed of established researchers and domain experts. The high quality of the ICAART 2025 program is enhanced by the keynote lecture delivered by distinguished speakers who are renowned experts in their fields: Inge Bryan (Chair of the Dutch Institute for Vulnerability Disclosure, Netherlands), Pavan Duggal (Advocate, Supreme Court of India, Chairman, International Commission on Cyber Security Law India, and Chief Executive, Artificial Intelligence Law Hub, India) and Paul Nemitz (Principal Adviser European Commission, Belgium). The conference is complemented by one workshop, two special sessions and one tutorial. They are: a Workshop on Quantum Artificial Intelligence and Optimization, chaired by Michael Kölle, a Special Session on Interpretable Artificial Intelligence Through Glass-Box Models, chaired by Mattias Wahde and a Special Session on Emotions and Affective Agents, chaired by Joaquin Taverner and Emilio Vivancos. Furthermore, a Tutorial on Self-Governing Systems will be given by Jeremy Pitt and Asimina Mertzani. All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for evaluation for indexing by SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Engineering Index and Web of Science / Conference Proceedings Citation Index. As recognition for the best contributions, several awards based on the combined marks of paper reviewing, as assessed by the Program Committee, and the quality of the presentation, as assessed by session chairs at the conference venue, are conferred at the closing session of the conference. Authors of selected papers will be invited to submit extended versions for inclusion in a forthcoming book of ICAART Selected Papers to be published by Springer, as part of the LNAI Series. Some papers will also be selected for publication of extended and revised versions in the special issue of the Springer Nature Computer Science Journal. The program for this conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research efforts are herewith recorded. Next, we thank the members of the Program Committee and the auxiliary reviewers for their diligent and professional reviewing. We would also like to deeply thank the invited speakers for their invaluable contribution and for taking the time to prepare their talks. Finally, a word of appreciation for the hard work of the INSTICC team; organizing a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly competent team. We wish you all an exciting and inspiring conference. We hope to have contributed to the development of our research community, and we look forward to having additional research results presented at the next edition of ICAART, details of which are available at https://icaart.scitevents.org.

Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage.Being capable of arbitrary function approximation, they are often referred to as Quantum Neural Networks (QNNs) when being used in analog settings as classical Artificial Neural Networks (ANNs).Similar to the early stages of classical machine learning, known schemes for efficient architectures of these networks are scarce.Exploring beyond existing design patterns, we propose a reduced-width circuit ansatz design, which is motivated by recent results gained in the analysis of dropout regularization in QNNs.More precisely, this exploits the insight, that the gates of overparameterized QNNs can be pruned substantially until their expressibility decreases.The results of our case study show, that the proposed design pattern can significantly reduce training time while maintaining the same result quality as the standard "full-width" design in the presence of noise.