Hybrid Intelligence (HI) is defined as “the combination of human and machine intelligence” for collaboration, “achieving goals that were unreachable by either [alone]” [2], leveraging the strength of both machine intelligence (such as strong optimization capabilities, effective handling of probabilities and less fallacy for confirmation biases) and human intelligence (such as generalization capabilities, situational understanding and common sense). In their research agenda, Akata and colleagues identify three key challenges for creating HI systems that relate to the interactive process: HI should be adaptive (how can a system learn from and adapt to humans and vice versa), explainable (how to create shared and explained awareness, goals and strategies) and collaborative (how to work in synergy). A popular learning mechanism for robots and AI agents that allows agents to dynamically adapt to their environment and promises interesting opportunities for adaptation in Human-Agent Interaction scenarios is Reinforcement Learning (RL). In RL, an agent learns through exploration and optimization of reward-based feedback for future actions (e.g. through the value-function method, policy search and actor critic approaches, [26]). While RL agents have been able to demonstrate their potential for success in a broad range of narrow tasks, many real-life applications present them with high-dimensional or continuous state-spaces that are not always fully observable which renders exploration and reproduction of actions costly and slow and usually require a considerable amount of domain knowledge or common sense, in order to succeed [19]. A more hybrid approach with Human-in-the-loop Reinforcement Learning, where human users enrich task learning in the form of teaching signals, provides a compelling modification of this setting that allows to shape and improve the learning process through methods such as evaluative teaching, Learning from Demonstration and instruction [9], [21] and can also be streamlined with more specialized approaches such as the TAMER [18] or the COACH architecture [23]. However, past work has shown that simply inserting a human teacher in the RL process, yielded limited success in applications where the reward was provided by teachers [16]. While a teacher can provide insightful expert knowledge or even just common sense to a learning agent, many times they are no experts in interpreting and understanding the behavior of RL-agents. For example, is a suboptimal action of a collaborative RL-agent and exploratory move, (that would help it to explore the state-action space and bring it closer to the optimal policy), or does the agent really “think” that move is indeed optimal (i.e. exploiting its currently believed optimal policy)? This ambiguity in the interpretation of learner behavior in turn results in suboptimal teaching signals (e.g. [16], [27]), effectively creating a misalignment in the teacher-learner loop. In human-human interaction, affective signals play a vital role in synergic interaction and their influence is a core research question in the field of Affective Computing [25], [28]. This project investigates if and how agent affective signals, grounded in the RL process itself, can improve explainability and collaboration in the teacher-learner loop in interactive Reinforcement Learning.
In the social media age, platforms like X (formerly Twitter) are essential for spreading information and shaping public opinion. This initial study proposes a methodology to investigate interactions with Italian opinion leaders, forming clusters of leaders based on user engagement patterns while preserving user anonymity by analyzing the vocabulary eccentricity and sentiment analysis. Our findings reveal that positive sentiment is linked to a more eccentric vocabulary, particularly within specific clusters, indicating that sentiment is deeply linked to the community's shared language. Despite the prevalence of negative sentiment, most clusters feature a minority of leaders with eccentric, positive language. These consistent patterns over time suggest a potential universal behaviour for further study.
Affective computing, a field dedicated to enabling machines to understand human behavior and emotions, is increasingly integrated into various societal domains such as, mental healthcare. The predominant focus of affective computing in Western societies raises potential risks for implementing these technologies in the diverse, global real-world. This paper briefly describes ethical and regulatory concerns of deploying affective computing technologies globally, emphasizing on needs to address privacy, security, and cultural sensitivity concerns.
Emotion recognition is a growing area of research interest in multiple disciplines, including psychology, linguistics, and computer science. The field of affective computing has changed significantly with the advent of deep learning, which has brought in new and innovative techniques such as multimodal learning for emotion recognition. In this position paper, we explore the issues related to ambiguity in emotion annotations arising while designing and utilizing multimodal (specifically speech, vision and/or text) emotion-based datasets for applications modeling perceived human emotions. Our findings are based on observations and lessons learned while performing multimodal emotion recognition experiments focusing on oral history interviews. We categorize the ambiguity issues in emotion annotations into two categories, namely technical (emotion representation, contextual reference, segmentation, skewed annotator sample, and annotator evaluation) and socio-psychological (subject- and annotator-induced) factors. We provide eight recommendations for managing ambiguity in emotion annotation relevant for research on datasets for emotion recognition in the domain of affective computing.
Awe is an emotion characterized by the perception of vastness and the need to accommodate this vastness into one's mental framework. We propose an elicitation scene to induce awe in Virtual Reality (VR), validate it through selfreport, and explore the feasibility of using skin conductance to predict self-reported awe and vastness as labeled from the stimuli in VR. Sixty-two participants took part in the study comparing the awe-eliciting space scene and a neutral scene. The space scene was confirmed as more awe-eliciting. A k-nearest neighbor algorithm confirmed high and low-awe score clusters used to label the data. A Random Forest algorithm achieved 65% accuracy (F1 = 0:56;AUC = 0:73) when predicting the self-reported low and high awe categories from continuous skin conductance data. A similar approach achieved 55% accuracy (F1 = 0:59;AUC = 0:56) when predicting the perception of vastness. These results underscore the potential of skin-conductance-based algorithms to predict awe.
