The transformative impact of artificial intelligence (AI) over the last three decades has led to the emergence of human-agent teaming (HAT) as a rapidly growing field. HAT aims to improve the performance of human-agent systems by combining the strengths of humans and machines to tackle complex problems and achieve innovative outcomes. Understanding HAT is crucial for effective collaboration, communication, and decision-making between humans and AI and for addressing socio-technological concerns. This paper aims to provide a holistic, systems-theoretic, and interdisciplinary perspective of HAT that will bridge gaps between the AI and human-machine interaction communities, create a common language to enable effective collaboration and lead to new insights and innovations.

In the burgeoning field of human-AI interaction, trust emerges as a cornerstone because many think that it is critical to the effectiveness of collaboration and the acceptance of AI systems. Traditional methods of assessing trust have predominantly relied on self-reported measures, requiring participants to articulate their perceptions and attitudes through questionnaires. However, these explicit methods may not fully capture the nuanced dynamics of trust, especially in real-time and complex interaction environments. This paper introduces an innovative approach to evaluating trust in human-AI teams, pivoting from the conventional reliance on verbal or written feedback to analyzing gameplay behaviors as implicit indicators of trust levels. Utilizing the Overcooked-AI environment, our study explores how participants’ interactions with AI agents of varying performance levels can reveal underlying trust mechanisms without a single query posed to the human players. This approach not only bypasses the efficiency challenges posed by repetitive and lengthy trust assessment methods, but also provides insights comparable to them. We highlight the potential of non-verbal cues and action patterns as reliable trust indicators by comparing the predictive accuracies of questionnaire-based models with those derived from gameplay behavior analysis. Furthermore, our findings suggest that these implicit measures can be integrated into adaptive systems and algorithms for realtime trust calibration in human-agent teaming settings. This shift towards an action-oriented trust assessment challenges existing paradigms and opens new avenues for understanding and enhancing human-AI collaboration.

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.

The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.

The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.

As smartphones become increasingly intimate and continuous companions, many opportunities are arising in human behavior sensing, modeling, and coaching. This position paper explores opportunities and challenges for mobile-based deployment of behavior change interventions. We suggest the adoption and extension of reinforcement learning for addressing these challenges, and we identify several key areas of future research that, on the basis of prior results, appear ripe for extending the benefits of reinforcement learning to human behavior change. These areas include stronger grounding of states in theories of human behavior, RL agent adaptation and decomposition, cooperative reinforcement learning, and in situ evaluation.

Increasingly large document collections require improved information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of traditional supervised classifiers has degraded as the number of documents has increased. This is because along with growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.