Many real-world problems (e.g., resource management, autonomous driving, drug discovery) require optimizing multiple, conflicting objectives. Multi-objective reinforcement learning (MORL) extends classic reinforcement learning to handle multiple objectives simultaneously, yielding a set of policies that capture various trade-offs. However, the MORL field lacks complex, realistic environments and benchmarks. We introduce a water resource (Nile river basin) management case study and model it as a MORL environment. We then benchmark existing MORL algorithms on this task. Our results show that specialized water management methods outperform state-of-the-art MORL approaches, underscoring the scalability challenges MORL algorithms face in real-world scenarios.

Surrogate modeling plays a crucial role in simplifying and approximating complex physical models, making them suitable for large-scale simulations and optimization studies of industrial relevance. Machine learning models, such as neural networks (NNs), are particularly well-suited for this purpose due to their simplicity and strong regression capabilities [1]. However, despite exceptional advancements in machine learning, issues and skepticism regarding the black-box nature and physical inconsistency of these models hinder the adoption of machine learning-based tools (and, more broadly, artificial intelligence) in industrial applications [2, 3]. To mitigate this limitation, significant research has been carried out to enforce known mechanistic relationships between inputs and predictions in NNs. Soft-constrained neural networks represent an approach in which physical equations are included as penalty terms in the loss function [4, 5].

This workshop will grow and consolidate a community of interdisciplinary CSCW researchers focusing on the topic of contestable AI. As an outcome of the workshop, we will synthesize the most pressing opportunities and challenges for contestability along AI value chains in the form of a research roadmap. This roadmap will help shape and inspire imminent work in this field. Considering the length and depth of AI value chains, it will especially spur discussions around the contestability of AI systems along various sites of such chains. The workshop will serve as a platform for dialogue and demonstrations of concrete, successful, and unsuccessful examples of AI systems that (could or should) have been contested, to identify requirements, obstacles, and opportunities for designing and deploying contestable AI in various contexts. This will be held primarily as an in-person workshop, with some hybrid accommodation. The day will consist of individual presentations and group activities to stimulate ideation and inspire broad reflections on the field of contestable AI. Our aim is to facilitate interdisciplinary dialogue by bringing together researchers, practitioners, and stakeholders to foster the design and deployment of contestable AI.

Despite this progress, patients often face various health-related challenges in their long-term recovery[9, 10]. More than half of patients develop new physical, psychological, and/or cognitive problems following their ICU admission [7], collectively referred to as Post Intensive Care Syndrome (PICS) [3, 25]. Family members also experience a stressful period, potentially leading to psychological problems addressed as PICS-Family (PICS-F) [2]. Patient and family-centered care (PFCC) at the ICU, including emotional support and follow-up service, could mitigate the symptoms associated with both PICS and PICS-F. In this study, we explored how an emerging technology, i.e., large language models, could support the emotional well-being of people exposed to critical care.

Many of these services necessitate that the robots can communicate and interact with users. However, to interact with robots fluently can be a challenge, for reasons such as an inappropriate mental model of the robot [8]. For example, based on the wide range of types of sensors of the robot (e.g., camera, radar, Infra-red detector, microphone, speaker, etc.), users may assume they have multimodal communication capabilities and even expect that the robot can remember them and recall details of their previous interactions[7]. Furthermore, speech may have a variety of accents, dialects, grammatical faults, and disfluencies, making interaction more difficult. This is one of the reasons why much research on humanrobot interaction(HRI) has focused on short-term interactions and relied on Wizard of Oz or"constrained rule-based" methods to get around these issues[2, 3]. However, communication failures may also happen in these kinds of sets up, especially with elderly participants. This abstract presents a scenario for human-robot action in a home setting involving an older adult and a robot. The scenario is designed to explore the envisioned modelling of memory for communication with a SAR. The scenario will enable the gathering of data on failures of speech technology and human-robot communication involving shared memory that may occur during daily activities such as a music-listening activity.

Managing knowledge efficiently is crucial for organizational success. In manufacturing, operating factories has become increasing knowledge-intensive putting strain on the factory's capacity to train and support new operators. In this paper, we introduce a Large Language Model (LLM)-based system designed to use the extensive knowledge contained in factory documentation. The system aims to efficiently answer queries from operators and facilitate the sharing of new knowledge. To assess its effectiveness, we conducted an evaluation in a factory setting. The results of this evaluation demonstrated the system's benefits; namely, in enabling quicker information retrieval and more efficient resolution of issues. However, the study also highlighted a preference for learning from a human expert when such an option is available. Furthermore, we benchmarked several closed and open-sourced LLMs for this system. GPT-4 consistently outperformed its counterparts, with open-source models like StableBeluga2 trailing closely, presenting an attractive option given its data privacy and customization benefits. Overall, this work offers preliminary insights for factories considering using LLM-tools for knowledge management.

Design optimisation potentially leads to lightweight aircraft structures with lower environmental impact. Due to the high number of design variables and constraints, these problems are ordinarily solved using gradient-based optimisation methods, leading to a local solution in the design space while the global space is neglected. Bayesian Optimisation is a promising path towards sample-efficient, global optimisation based on probabilistic surrogate models. While Bayesian optimisation methods have demonstrated their strength for problems with a low number of design variables, the scalability to high-dimensional problems while incorporating large-scale constraints is still lacking. Especially in aeroelastic tailoring where directional stiffness properties are embodied into the structural design of aircraft, to control aeroelastic deformations and to increase the aerodynamic and structural performance, the safe operation of the system needs to be ensured by involving constraints resulting from different analysis disciplines. Hence, a global design space search becomes even more challenging. The present study attempts to tackle the problem by using high-dimensional Bayesian Optimisation in combination with a dimensionality reduction approach to solve the optimisation problem occurring in aeroelastic tailoring, presenting a novel approach for high-dimensional problems with large-scale constraints. Experiments on well-known benchmark cases with black-box constraints show that the proposed approach can incorporate large-scale constraints.

This paper introduces a compound vision system that enables robots to localize people up to 15m away using a cheap camera. And, it proposes a robust navigation stack that combines Deep Reinforcement Learning (DRL) and a probabilistic localization method. To test the efficacy of these systems, we prototyped a low-cost mobile robot that we call Covy. Covy can be used for applications such as promoting social distancing during pandemics or estimating the density of a crowd. We evaluated Covy's performance through extensive sets of experiments both in simulated and realistic environments. Our results show that Covy's compound vision algorithm doubles the range of the used depth camera, and its hybrid navigation stack is more robust than a pure DRL-based one.

A tiny turbine made from DNA looks like a windmill and is hundreds of times smaller than most bacteria. It rotates when immersed in salty water and could be used as a molecular machine for speeding up chemical reactions or transporting particles inside cells. Cees Dekker at Delft University of Technology in the Netherlands and his colleagues created the turbine after being inspired by a rotating enzyme that helps catalyse energy-storing molecules in our cells.

How human-like do conversational robots need to look to enable long-term human-robot conversation? One essential aspect of long-term interaction is a human's ability to adapt to the varying degrees of a conversational partner's engagement and emotions. Prosodically, this can be achieved through (dis)entrainment. While speech-synthesis has been a limiting factor for many years, restrictions in this regard are increasingly mitigated. These advancements now emphasise the importance of studying the effect of robot embodiment on human entrainment. In this study, we conducted a between-subjects online human-robot interaction experiment in an educational use-case scenario where a tutor was either embodied through a human or a robot face. 43 English-speaking participants took part in the study for whom we analysed the degree of acoustic-prosodic entrainment to the human or robot face, respectively. We found that the degree of subjective and objective perception of anthropomorphism positively correlates with acoustic-prosodic entrainment.