Workshops

The following Workshops will be carried out at CAI 2026:

  1. W1-TEACHING: AI-Augmented Teaching and Assessment in Higher Education: Challenges, Innovations, and Evidence
  2. W2-BIOLOGY: AI for biology and biomedicine
  3. W3-CLINICAL: Clinical Care and Medical Imaging with Intelligent VR/AR/XR Systems (AI4HealthXR 2026)
  4. W4-AML: Autonomous Machine Learning in Complex Situations: Theories, Algorithms and Applications
  5. W5-BECOMLLM: BEComLLM: Bridging Evolutionary Computing and Large Language Models
  6. W6-ACD: 4th International Workshop on Adaptive Cyber Defense (ACD 2026)
  7. W7-GPAIS: Workshop on General-Purpose Artificial Intelligent Systems (GPAIS)
  8. W8-QUANTUM: Workshop on Quantum Artificial Intelligence
  9. W9-SWARM: Workshop on Swarm Intelligence and Evolutionary Computation
  10. W10-MOBILITY: Smart, Autonomous, Sustainable and Safe Mobility

 

W1-TEACHING: AI-Augmented Teaching and Assessment in Higher Education: Challenges, Innovations, and Evidence

Proposer

Alaa Marshan

Workshop Code

Please use the following code when submitting your paper to this Workshop: W1-TEACHING

Scope and Aims

As AI tools such as ChatGPT, Copilot, and education-specific agents proliferate, institutions are grappling with how to integrate them constructively, rather than reactively. Moreover, these systems aim to offer students personalised, on-demand learning support, such as answering questions, recommending resources, and simulating dialogue with expert tutors. This workshop focuses on how AI can augment teaching, assessment, and feedback practices in higher education, while ensuring pedagogical soundness, academic integrity, and student engagement. Additionally, it covers the design, development, and evaluation of AI-driven tutoring systems and learning companions for higher education.

The workshop aims to:

  • Showcase emerging AI applications that support lecturers and assessors (e.g., feedback generation, rubric alignment, formative assessment tools).
  • Explore the pedagogical impact and evidence base for AI in teaching and learning.
  • Address academic policy concerns such as plagiarism, AI-authorship, and fairness in assessment.
  • Facilitate collaboration between AI researchers, educators, EdTech developers, and instructional designers.
  • Advancing research on adaptive learning agents powered by LLMs, knowledge graphs, and dialogue systems.
  • Evaluating the pedagogical effectiveness and ethical considerations of deploying AI tutors at scale.
  • Creating guidelines and toolkits for designing transparent, reliable, and culturally sensitive AI companions.

This is significant as AI challenges traditional teaching models and opens opportunities for rethinking feedback loops, assessment design, and teaching support at scale. Furthermore, higher education increasingly serves diverse, global cohorts with varied learning needs, and AI-powered companions can address gaps in access to support, but must be aligned with curriculum goals, sensitive to student needs, and free from bias.

Key Themes

  • AI for automated feedback, marking support, and formative assessment
  • AI-supported scaffolding and personalised tutoring agents
  • Use of large language models (LLMs) to enhance learning analytics
  • Designing assessment for AI-augmented contexts (e.g., open-AI assessments)
  • Institutional policy, ethics, and detection of inappropriate AI use
  • Case studies on teacher workload reduction through AI
  • Design principles for intelligent tutoring systems (ITS) using AI and NLP
  • AI-generated explanations, feedback, and Socratic dialogue
  • Alignment of AI tutoring with curricula and learning outcomes
  • Multilingual and inclusive AI tutor systems
  • Risk of misinformation, hallucinations, and dependency on AI
  • Measurement of learning gain, engagement, and trust

Intended Outcomes

  • A whitepaper or report synthesizing AI assessment challenges and strategies
  • Draft AI-in-Assessment policy templates for universities
  • A network of AI-in-education researchers and practitioner partners
  • Prototyping roadmap for responsible AI tutor development
  • Annotated benchmark dataset for tutor-agent dialogues in education
  • Cross-institutional research agenda on evaluation and pedagogy for AI tutoring

 

 

W2-BIOLOGY: AI for biology and biomedicine

Proposer

Dr. Roman Bauer

Workshop Code

Please use the following code when submitting your paper to this Workshop: W2-BIOLOGY

Scope and Aims

With the vast improvements in computational resources, from a hardware, software as well as conceptual perspective, it has become possible to advance and accelerate fundamental biological research, as well as biomedical research. AI and advanced computational methods have become a fundamental pillar of pertinent research, rendering the scientific method more efficient and facilitating collaboration across disciplines. In particular, experimental and medical/clinical researchers can effectively work with computational experts since computational models have increasingly gained in detail, accuracy and realism. Along those lines, biological systems such as the brain, the immune system or specific organs can be captured based on experimental data from different spatial and temporal scales. Moreover, state-of-the-art AI and bioinformatics models can be employed using large-scale data-sets, which is further facilitated by the increasing availability of public databases and practicality for collaboration across labs.

The scope of this research topic includes innovative AI-assisted methods that are applied to biological and medical problems. Such problems should ideally focus on the fundamental processes pertinent to a given topic (e.g., biophysical, genetic or physiological). A broad range of biological and medical applications are relevant, such as for instance the brain, the immune system, cancer, or neurodegenerative disorders. The application can be with regards to fundamental science, to better understand the underlying disease factors, or for computational diagnosis as well as treatment optimisation. We expect the participants to consider relevance for different research communities, and formulate the research in a language that can be communicated within interdisciplinary settings.

The aims of this workshop are to present, discuss and exchange ideas on applications of state-of-the-art AI methods and techniques, for biology and medicine. Importantly, the employed approaches must incorporate contributions that go beyond classical and/or standard AI methods. In particular, given the crucial point of explainability and interpretability in biomedical modeling, we would like to study approaches that address current flaws in black-box AI techniques. We would like to achieve a general meeting where an open discussion of current challenges in AI for biology & medicine, and existing gaps is encouraged.

A focus of the meeting will be on the presentation of existing approaches, platforms and software that facilitate explainable model generation, comparison and testing. Ideally, these should be available as open-source, and support reproducibility, extendability and collaboration. Ultimately, we would like to see this meeting as a stepping stone for wider, international collaboration and grant proposals. Objectives comprise the creation of an international network of researchers who collaborate, write grant proposals and engage in cutting-edge research. Moreover, it is anticipated that researchers engage with future IEEE CAI events and contribute to relevant organisational initiatives.

 

 

W3-CLINICAL: Clinical Care and Medical Imaging with Intelligent VR/AR/XR Systems (AI4HealthXR 2026)

Proposers

Dr. Malika Bendechache, Dr. Ramin Ranjbarzadeh, Dr. Ayse Keles and Shokofeh Anari

Workshop Code

Please use the following code when submitting your paper to this Workshop: W3-CLINICAL

Scope and Aims

Medical imaging, diagnosis, surgical planning, and clinical training are all changing as a result of the quick convergence of Artificial Intelligence (AI) with Virtual Reality (VR), Augmented Reality (AR), and Extended Reality (XR). Conventional 2D visual environments limit the ability to engage with complicated anatomical data, perceive depth, and reason spatially. Clinicians and researchers can now visualize, navigate, and interpret medical images in dynamic 3D environments with the assistance of AI-powered extended reality systems.

This workshop’s main objectives are to:

  • Investigate AI-powered XR clinical imaging, diagnosis, and intervention solutions.
  • Encourage the development of sophisticated training, simulation, and visualization tools.
  • Deal with the usability, clinical, and technical issues in immersive healthcare.
  • Encourage cooperation between developers, engineers, physicians, and AI researchers.
  • Emphasize practical and translational applications in medical education and hospitals.

Content and Objectives

This workshop will feature research presentations, demonstrations, and invited lectures that concentrate on AI-assisted immersive technologies in medicine. It will provide a forum for idea sharing, technical innovation discussions, and clinical opportunity identification.

Content Areas in Plan:

  • AI-powered XR systems for surgical imaging, pathology, and radiology
  • Digital twins and deep learning-based 3D reconstruction from CT, MRI, and PET
  • Using VR/AR for skill evaluation, procedural simulation, and medical education
  • Immersion diagnostic solutions that integrate generative AI and LLM
  • Real-time interaction, haptics, and rendering for surgery and telemedicine
  • Explainable AI, privacy, safety validation, and ethical requirements in XR healthcare

Goals of the Workshop:

  • Showcase state-of-the-art intelligent XR solutions for medical applications
  • Determine clinical adoption routes and open research obstacles.
  • Promote cooperative endeavors and multi-institution programs.
  • Create an international network of topic experts and potential contributors.

Case studies, demos, short papers, and full papers are all expected submission types.

 

 

W4-AML: Autonomous Machine Learning in Complex Situations: Theories, Algorithms and Applications

Proposers

Dr Zhen Fang, Dr Hang Yu and Professor Jie Lu

Workshop Code

Please use the following code when submitting your paper to this Workshop: W4-AML

Scope and Aims

The aim of the workshop is to create an integrated and holistic computational foundation for a new research direction – autonomous learning in complex decision situations. We define a decision situation as complex if the data available for use in machine learning efforts is massive and/or uncertain and/or dynamic. Autonomous learning will advance the capability of machines to learn from complex situations and minimise human involvement in the learning process (such as to autonomously determine a threshold, a sample set, a source domain, a concept drift, and a policy). Recently, we have seen several new successful developments in the direction, such as massive stream learning algorithms, incremental and online learning for streaming data. These developments have demonstrated how the autonomous learning can be used in some complex decision situations to contribute to the implementation of machine learning capability. We have also witnessed some compelling evidence of successful investigations on the use of the autonomous learning methodology to support real-time prediction and decision making in practice. To anchor the above in real-world needs, we explicitly connect methodological advances with sector-specific challenges in domains where data are massive, uncertain and dynamic, and where autonomy can minimise manual intervention while improving timeliness and safety. Examples include healthcare (patient-flow forecasting, triage support, medical supply chains), transport and mobility (traffic-state prediction and AV/ADAS perception under distribution shift), agriculture (crop-health monitoring and yield prediction with remote sensing), logistics and supply chains (demand sensing, inventory optimisation, dynamic routing and disruption risk management), as well as business intelligence/finance and geosciences. With these observations, it is instructive, vital, and timely to offer a unified view of the current trends and form a broad forum for the fundamental and applied research as well as the practical development of autonomous learning in complex decision situations for improving machine learning and data-driven decision support systems.

Content and Objectives

Topics of interest include (but are not limited to):

  • Transfer Learning and Autonomous Transfer Learning
  • Out-of-distribution Detection and Autonomous Out-of-distribution Detection
  • Out-of-distribution Generalization and Domain Generalization
  • Concept Drift Detection, Understanding and Adaptation
  • Weakly Supervised Learning, Few-shot/Zero-shot Learning
  • Reinforcement Learning and Autonomous Reinforcement Learning
  • Meta Learning
  • Concept Drift Adaptation and Autonomous Concept Drift Adaptation
  • Stream Learning and Autonomous Stream Learning
  • Autonomous Curriculum Learning
  • Continuous Learning and Lifelong Learning
  • Large Language Model in Life-critical Decisions
  • Autonomous Intelligent Systems, Multi-agent Learning
  • Autonomous Human Preference Alignment for Foundation Models
  • Applications of Autonomous Machine Learning Techniques in Health, Transportation, Agriculture, Logistics, Supply Chain, and Beyond

Intended outcomes:

  1. Provide a platform for researchers to present new theoretical insights into autonomy in machine learning for complex situations.
  2. Encourage the development of algorithmic methods that balance robustness, adaptability, and efficiency in complex situations.
  3. Facilitate discussions on practical applications and deployment challenges in complex real-world domains.
  4. Establish a community and roadmap for future research directions in autonomous machine learning in complex situations.

 

 

W5-BECOMLLM: BEComLLM: Bridging Evolutionary Computing and Large Language Models

Proposers

Niki van Stein, Anna V. Kononova, Thomas Bäck, Roman Senkerik and Michal Pluhace

Workshop Code

Please use the following code when submitting your paper to this Workshop: W5-BECOMLLM

Scope and Aims

The primary goal of this workshop is to explore the potential of combining Large Language Models (LLMs) and Evolutionary Computing (EC) to advance research in both fields and their interconnection. This synergy aims to expand the boundaries of optimisation, artificial intelligence, and machine learning by exploring new methodologies and applications. By fostering a collaborative platform for researchers and practitioners, the session aims to:

  • Encourage innovative approaches that leverage the strengths of LLMs and EC techniques.
  • Enable the creation of more adaptive, efficient, and scalable algorithms by integrating evolutionary mechanisms with advanced LLM capabilities.
  • Inspire novel research directions that could reshape AI, specifically LLMs, and optimisation fields through this hybridisation.
  • Achieve a better understanding and explanation of how these two seemingly disparate fields are related and how knowledge of their functions and operations can be leveraged.

Content and Objectives

The session will focus on a range of topics at the intersection of LLMs and Evolutionary Computing, with the following key objectives:

  1. Evolutionary Prompt Engineering: Develop effective prompt optimisation strategies using evolutionary algorithms to maximise the utility of LLMs in tasks such as text generation, summarisation, and question answering.
  2. LLM-Guided Evolutionary Algorithms: Investigate how LLMs can be integrated into evolutionary algorithms to guide search processes, provide domain expertise, and generate candidate solutions. Exploring new ways of using LLMs for evolutionary operators (generating variation, selection,…).
  3. Evolutionary Learning: hybridizing LLMs, EC and ELO to iteratively evolve and refine solutions.
  4. Co-evolution of LLMs and EC Techniques: Examine the co-evolutionary process where both LLMs and EC techniques evolve together to solve complex, multimodal, and multi-objective problems.
  5. Benchmarking and Comparative Studies: Conduct studies to compare the effectiveness of LLM-enhanced evolutionary approaches with traditional EC methods across a variety of optimisation challenges.
  6. LLMs for Automated Code Generation in EC: Explore how LLMs can be used to automatically generate or refine code for evolutionary algorithms, potentially reducing development time and improving adaptability.
  7. Optimisation of LLM Architectures: Use evolutionary algorithms to fine-tune hyperparameters, architectures, and training processes of LLMs to boost their performance on specific tasks.
  8. Applications in Real-World Problems: Demonstrate practical applications of LLM-EC hybrid models in areas such as engineering, healthcare, finance, and creative industries, showcasing their real-world impact and utility.
  9. Better understanding, fine-tuning, model size, selection of model, and adaptation of Large Language Models for EC.

These content areas aim to push the boundaries of current research, driving both theoretical advancements and practical applications.

 

 

W6-ACD: 4th International Workshop on Adaptive Cyber Defense (ACD 2026)

Proposers

Dr. Mark Bilinski, Prof. Marco Carvalho, Dr. Ahmad Ridley and Dr. Damian Marriott

Workshop Code

Please use the following code when submitting your paper to this Workshop: W6-ACD

Summary

We believe this workshop proposal will bolster the growing field of AI/ML in Cyber Security at 2026 IEEE CAI, continuing the momentum from last year. The co-chairs have significant experience in organizing related workshops and together with the program committee bring significant expertise in AI and Cyber. At the first ACD workshop in 2021 we launched the first CAGE (Cyber Autonomy Gym for Experimentation) Challenge. There has been growing interest in successive challenges and their results. We announced the fourth challenge at last year’s workshop and this year’s proposed workshop will heavily feature the results and possibly announce the next challenge.

Scope and Aims

Building on recent advances in Artificial Intelligence (AI) and Machine Learning (ML) the Cyber defense research community has been motivated to develop new dynamic and sustainable defenses through adaptive cyber defense. The cyber domain cannot currently be reliably and effectively defended without extensive reliance on human experts. Skilled cyber defenders are in short supply and often cannot respond fast enough to cyber threats. With the growing adoption of AI and ML techniques to both cyber and non-cyber settings, there is an increasing need to bridge the critical gap between AI and Cyber research and practitioners. We must accelerate our efforts to create cyber defenses that can learn to recognize and respond to cyber attacks or discover and mitigate weaknesses in cooperation with other cyber operation systems and human experts. Furthermore, these defenses must be adaptive, and able to evolve over time to take into account changes in attacker behavior, benign changes in the systems, and expected drift in user behavior over time. The ACD Workshop will focus on sharing research that explores unique applications of AI and ML as an emerging technology underpinning foundational capabilities of adaptive cyber defense. The Workshop will be comprised of invited and accepted technical presentations, and a panel discussion focused on open problems and potential research solutions. This domain consists of challenging problems of critical importance to national and global security. Participation in this workshop will offer potentially unprecedented opportunities to stimulate research and innovation in this area.

Content and Objectives

The main objective of this proposal is to bring together both the research community and the government and industry communities to exchange experiences, discuss challenges and propose research directions. The workshop would consist of technical presentations (invited and accepted paper speakers will share their thoughts and experience on adaptive cyber defense); Academic-Industry-Government research direction discussion (top researchers in the area from academic, industry and government will discuss the current and future challenges in this area); Challenge problems (Academic challenge event results).

 

 

W7-GPAIS: Workshop on General-Purpose Artificial Intelligent Systems (GPAIS)

Proposers

Prof. Dr. Xingyu Wu, Prof. Dr. Daniel Molina, Prof. Dr. Isaac Triguero and Prof. Dr. Javier Del Ser

Workshop Code

Please use the following code when submitting your paper to this Workshop: W7-GPAIS

Scope and Aims

In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems designed to tackle a single task. In this context, a General-Purpose Artificial Intelligence System (GPAIS) refers to an advanced AI system capable of effectively performing a range of distinct tasks. Its degree of autonomy and ability is determined by several key characteristics, including the capacity to adapt or perform well on new tasks that arise at a future time, the demonstration of competence in domains for which it was not intentionally and specifically trained, the ability to learn from limited data, and the proactive acknowledgement of its own limitations in order to enhance its performance. The overarching design goal of a GPAIS is to design AI models with the ability not only to perform[1]. Several AI techniques have been identified as promising approaches to enhance GPAIS [2].

This workshop is aligned and in cooperation with the IEEE CIS TaskForce LLMs and Computational Intelligence for General Purpose Artificial Intelligent Systems (GPAIS).

[1] Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024). General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance. Information Fusion, 103. Scopus. https://doi.org/10.1016/j.inffus.2023.102135

[2] Molina, D., Poyatos, J., Ser, J. D., García, S., Ishibuchi, H., Triguero, I., Xue, B., Yao, X., & Herrera, F. (2025). Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects. IEEE Transactions on Evolutionary Computation, 1-1. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2025.3530096

Content and Objectives

In this Workshop, we encourage researchers to submit original contributions proposing new algorithmic approaches, improvements for GPAIS. Potential topics of interest for the workshop include, but are not limited to, the following:

Using Computational Intelligence (CI) to Enhance GPAIS Performance and Expand GPAIS Application Boundaries

  • Neuro-symbolic and CI-enhanced LLM architectures
  • CI-based preprocessing for GPAIS
  • Foundation models
  • Explainability and safety in GPAIS
  • Low-resource adaptation and efficiency for GPAIS
  • CI for robust multi-modal and multi-agent AI
  • Evolutionary fine-tuning and prompt optimization
  • Structural optimization of LLM for different objectives (alignment, XAI, efficiency)
  • Large scale transformers and distributed computing strategies to build GPAIS

Using GPAIS to Advance Intelligent, Explainable, and Semantic-Aware CI: Leverage GPAIS to select:

  • GPAIS-driven intelligent evolutionary algorithms
  • GPAIS-driven intelligent fuzzy systems
  • GPAIS for automated CI algorithm configuration and new CI algorithm design
  • Automated algorithm construction using LLM translated domain knowledge
  • GPAIS-driven explainable CI techniques
  • Design of more efficient CI techniques

Application scenarios for CI+GPAIS:

  • Benchmarks and validation frameworks for GPAIS
  • Robotics

Conversational agents

  • Bioinformatics
  • Healthcare diagnosis with CI-enhanced LLMs
  • Neuro-symbolic architectures in autonomous vehicle navigation systems

 

 

W8-QUANTUM: Workshop on Quantum Artificial Intelligence

Proposers

Prof. Giovanni Acampora, Prof. Autilia Vitiello, Prof. Manuel Pegalajar Cuéllar and Dr. Amir Pourabdollah

Workshop Code

Please use the following code when submitting your paper to this Workshop: W8-QUANTUM

Scope and Aims

Quantum artificial intelligence is an emerging field of computer science that combines the advantages of two prominent research domains: artificial intelligence and quantum computing. Indeed, on the one hand, the potential boost in computational speed from quantum computing could enhance the eFiciency of artificial intelligence in mimicking human abilities, thus revealing an entirely new landscape for the creation and evolution of intelligent systems. On the other hand, present artificial intelligence methods for problem solving, reasoning, and learning could aid in crafting superior methodologies for quantum technology design, accelerating the shift from the noisy intermediate-scale quantum (NISQ) phase to the era of fault-tolerant quantum computing (FTQC), marking a noteworthy advancement towards achieving quantum utility. This workshop aims to bring together researchers and practitioners from the fields of artificial intelligence and quantum computing to explore this emerging paradigm and provide a forum for discussing theoretical foundations, hybrid architectures, and practical implementations that bridge the gap between classical and quantum intelligence.

In line with the mission of IEEE Computational Intelligence Society, IEEE Computer Society, IEEE System, Man and Cybernetics Society and IEEE Quantum Technical Community, this event aims to promote interdisciplinary collaboration, stimulate discussion on benchmarking and reproducibility, and support the development of shared frameworks and standards for quantum artificial intelligence research and applications.

Content and Objectives

The workshop will feature invited talks, contributed papers, and panel discussions addressing the following topics

  • Quantum Machine Learning
  • Quantum Data Preprocessing
  • Quantum Kernels
  • Quantum Variational Models
  • Quantum Generative AI
  • Quantum Natural Language Processing
  • Quantum Game Theory
  • Quantum Automated Reasoning
  • Quantum Logic
  • Quantum Fuzzy Reasoning
  • Quantum Optimization
  • Quantum Evolutionary Algorithms
  • AI for Quantum Circuit Optimization
  • AI for Quantum Calibration
  • AI for Quantum Error Mitigation and Correction
  • AI for Automated Quantum Algorithms Design

The objectives are to:

  1. Establish a shared understanding of the current challenges and frontiers of QAI.
  2. Identify promising research directions for hybrid AI–Quantum paradigms.
  3. Encourage collaboration between AI and Quantum Computing communities.
  4. Support open and standardized approaches to software frameworks and evaluation practices in Quantum AI.

The workshop’s intended outcome is to consolidate a sustainable research community that fosters long-
term collaboration and contributes to shaping the emerging Quantum Artificial Intelligence landscape
within IEEE Computational Intelligence initiatives.

 

 

W9-SWARM: Workshop on Swarm Intelligence and Evolutionary Computation

Proposer

Prof. Saúl Zapotecas-Matínez, Diego Oliva and Abel García Nájera

Workshop Code

Please use the following code when submitting your paper to this Workshop: W9-SWARM

Abstract

Swarm Intelligence and Evolutionary Computation are two critical areas within Artificial Intelligence that focus on solving optimization problems. Swarm Intelligence draws inspiration from the collective behaviors observed in nature, such as bird flocks or fish schools. This approach has led to algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), which solve problems by leveraging simple interactions among individuals. On the other hand, Evolutionary Computation mimics the principles of natural selection and genetics by evolving populations of candidate solutions through mechanisms such as selection, crossover, and mutation. Popular algorithms in this category include Genetic Algorithms (GA) and Evolution Strategies (ES). Both methodologies are widely applied across fields like optimization, machine learning, and robotics, where they explore complex solution spaces by leveraging nature-inspired processes.

Scope and Aims

The workshop will explore the dynamic field of Swarm Intelligence and Evolutionary Computation, focusing on their theoretical foundations, recent algorithmic advancements, and practical applications. A broad range of topics will be discussed, including but not limited to:

  • Recent algorithm developments include PSO, ACO, GA, and ES. New methods for addressing complex optimization problems, including large-scale, many-objective, constrained, and mixed-variable challenges.
  • Applying swarm and evolutionary techniques in robotics, machine learning, optimization, and bioinformatics fields.
  • Interdisciplinary approaches that integrate these computational techniques with other scientific fields.
  • Current challenges and future research directions in Swarm Intelligence and Evolutionary Computation.

Given the diverse range of topics in swarm and evolutionary algorithms, the proposed workshop aims to:

  • Knowledge Dissemination: Presenting the latest research and advancements in
    Swarm Intelligence and Evolutionary Computation, offering insights into both
    fundamental principles and innovative applications.
  • Facilitating Collaboration: Creating opportunities for networking and collaboration
    among researchers, practitioners, and students to foster innovation and joint projects.
  • Exploring Challenges: Identifying and addressing current challenges in the field to
    foster new research directions and solutions.
  • Promoting Interdisciplinary Research: Encouraging the integration of swarm and
    evolutionary methods with other scientific disciplines to solve complex, real-world
    problems.

Content and Objectives

The workshop will cover various topics and activities, focusing on examining the latest advancements in Swarm Intelligence and Evolutionary Computation. The program will feature:

  1. Keynote Presentations: Renowned experts will present cutting-edge research and developments in Swarm Intelligence and Evolutionary Computation, covering theoretical foundations and practical applications.
  2. Technical Sessions: Oral presentations and discussions will highlight recent findings in Swarm Intelligence and Evolutionary Computation. These presentations are based on accepted papers, which are categorized into two submission types: regular papers (6 pages) and short papers (2 pages), both of which include figures, tables, and references.
  3. Poster Sessions: Opportunities for participants to present their research in a more
    informal setting, encouraging feedback and collaboration.

In this regard, the primary objectives of the workshop are to:

  • Disseminate Knowledge: Share the latest research and innovations, enhancing participants’ understanding of Swarm Intelligence and Evolutionary Computation.
  • Foster Collaboration: Promote networking and collaboration among the artificial intelligence community.
  • Enhance Skills: Equip participants with practical skills through workshops and tutorials regarding Swarm Intelligence and Evolutionary Computation.
  • Address Challenges: Encourage the exploration of current challenges in the field, promoting innovative solutions.
  • Support Interdisciplinary Research: Advocate for applying these methods across diverse scientific and engineering fields.

 

 

W10-MOBILITY: Smart, Autonomous, Sustainable and Safe Mobility

Proposers

A. Sanchis and Miguel Ángel Sotelo

Workshop Code

Please use the following code when submitting your paper to this Workshop: W10-MOBILITY

Scope and Aims

The proposed workshop aims to bring together researchers, engineers, and industry practitioners to discuss recent advances and challenges in Intelligent Transport Systems (ITS) powered by Artificial Intelligence (AI). The session will focus on the integration of connected, cooperative, and automated mobility (CCAM) technologies with intelligent infrastructures, drones, and electric vehicle management systems, with the goal of achieving safer, more efficient, and sustainable transport models.

AI is transforming the transport landscape by enabling advanced perception, decision-making, and control systems for vehicles and infrastructures, supported by V2X communication and Digital Twin technologies. These innovations pave the way for real-time traffic optmisation, predictive maintenance, and multimodal coordination between terrestrial and aerial systems.

The main objective of this workshop is to promote knowledge exchange and collaboration between academia, industry, and public administrations. It aims to foster the development of next-generation ITS solutions that leverage AI to address current challenges in safety, energy efficiency, and environmental sustainability.

Content and Objectives

The workshop will include technical sessions, presentations, and case studies focused on the application of Artificial Intelligence across different layers of the transport ecosystem — from perception and control, to strategic planning and infrastructure management. Key topics and objectives include:

  • Development of AI-based perception, prediction, and decision-making systems for autonomous and automated vehicles.
  • Application of machine learning and reinforcement learning for dynamic traffic control, anomaly detection, and incident management.
  • Design of intelligent infrastructures that interact through cooperative V2X communication frameworks (V2V, V2I, V2D).
  • Integration of AI-driven drones and urban air mobility into terrestrial transport networks for surveillance, inspection, and logistics.
  • Optimisation of electric vehicle management, charging strategies, and energy consumption through AI-enabled predictive models.
  • Development of AI-based fleet management and multimodal optimisation systems for public transport and logistics operations.

Expected outcomes include the definition of a shared research roadmap on AI-enabled ITS, the identification of collaboration opportunities, and the dissemination of best practices and standards for deploying cooperative, connected, and sustainable mobility technologies.

This workshop is proposed within the framework of the Thematic Network on Artificial Intelligence for Smart, Autonomous, Sustainable and Safe Mobility (IA4MOV), funded by the Spanish State Research Agency under grant RED2024-153662-T.

The network is composed of research groups from the University of Alcalá (UAH), University of Granada (UGR), European University (UE), Polytechnic University of Madrid (UPM), University of Salamanca (USAL), University of Oviedo (UNIOVI), two groups from the Polytechnic University of Valencia (UPV), the Complutense University of Madrid (UCM), the University of Málaga (UMA), and four groups from the Carlos III University of Madrid (UC3M).

Consequently, a significant number of papers are expected, at least one from each participating group —16 groups — in addition to those submiced by other research teams working in this highly relevant field.