This paper provides an overview into the use of ACT-R as a cognitive architecture in the military. ACT-R stands for Adaptive Control of Thought - Rational. It is a cognitive architecture, a framework for a human like AI program, that models the human mind. In this paper its use will be examined in the military. Through this literary survey an overview will be created of the military’s usage of ACT-R. The overview will answer the questions in which applications the military uses ACT-R and why they use ACT-R. It will bring understanding to the people of how ACT-R is used in the military. It will also give insight into where their tax money is being spent on. For the military an overview will come in handy in case ACT-R gets outdated. They will know what programs will need an update. The overview consists of three parts. A robotics operator manager, a test to determine the value of an officer managing multiple robots. The creation of intelligent tutoring systems for ship navigation and aircraft recognition. A supporting tool for analysts to help determine the value of information.

This study is a systematic literature review that was conducted to investigate and further explain how different decision making strategies are implemented through heuristics-based, utility-based approaches, or how different blends of these approaches are created to make hybrid decision making models that excel in their domain. Following the identification, screening and the analysis procedures of the systematic literature review, relevant literature works were found. After close analysis of the relevant works, it was evident that introducing a heuristic-based approach on top of a utility-based approach was beneficial for a robust decision making process, and vice versa. However, it was also identified that the trade off of hybridization comes with a cost of specialization for the niche domain the implementation is tasked to work in.

Various types of user data such as heart rate, age, eye tracking data, body mass index, conversational data and more can be used to model a user. Intelligent systems can user these models to recognize and adapt. For example, by recognizing if someone is bored or scared while playing games based on a model of their engagement and using that to adapt the difficulty of a game [1]. However, this survey will focus on modelling user decision-making and reasoning. It will thus answer the question: "How do intelligent systems acquire and use user data to model decision-making and reasoning, and how are these models applied for recognition and adaptation?" To answer these questions, a systematic literature review was conducted. Using the key concepts Intelligent Systems, Recognition, Adaptation, User Modelling, and Human Decision-Making and Reasoning, queries have been formulated for 4 different databases. Papers have been screened and extracted using the PRISMA guidelines [2], resulting in the usage of 52 articles. The results reveal that intelligent systems are dominated by recommender systems. They use user preferences to recommend items in all sorts of domains. These user preferences can be modelled using other models such as user demographics, behaviour, emotions and characteristics. Besides making the decision-making process easier and thereby improving user experience, they also deal with other challenges such as fairness, privacy, usability and prediction accuracy. While being less prevalent, other intelligent systems such as decision support systems, assistance systems and social robots sometimes serve even more important purposes and have the potential of solving diverse problems. However, their underutilization suggests an opportunity for research in this area.

Episodic memory (EM) -- the capacity to recall past experiences situated in time and context -- is a critical component of intelligent behavior. Although several cognitive architectures (CAs) have incorporated mechanisms inspired by episodic memory, implementations vary widely in structure, mechanisms, and integration with other cognitive functions. While prior work has reviewed episodic memory across a range of architectures in a high-level manner, detailed, structured comparisons among specific systems remain lacking. This study presents a focused comparative analysis of modeling episodic memory in two contrasting cognitive architectures: Soar, a symbolic, rule-based, general-purpose system, and Xapagy, a system designed specifically for narrative reasoning, relying on direct, unprocessed recordings of autobiographical events. By analyzing the representations, structures, and mechanisms of episodic memory in these two systems, this study highlights important design trade-offs and distinct assumptions about the role of episodic memory in cognition and its modeling approaches in CAs.

This paper surveys nine studies that implement aspects of moral reasoning within cognitive architectures (CAs) or CA-inspired frameworks. Its primary aim is to assess the viability of this approach for future research and to clarify the state of the domain. Two research paradigms emerge: (1) modeling human moral reasoning and (2) constructing artificial moral agents. Despite this distinction, all studies face similar challenges: fragmented reuse (each employs a different architecture), limited pre-programmed behaviors, and the absence of standardized benchmarks or metrics. Researchers remain optimistic about the explainability of their systems' behaviors and inner workings, yet often they acknowledge significant scalability and validation hurdles. Overall, CAs currently support only small-scale experiments; substantial further research – both empirical and into the theoretical basis of the field – is needed before these systems can attain real-world relevance.

Heuristic strategies are an integral part of consumer decision-making. Heuristics serve as mental shortcuts that reduce cognitive effort, simplifying consumer decisions. To go from qualitative insights into these heuristics to quantitative data, a cognitive architecture must represent these heuristic strategies to understand consumer behavior better. This study will focus on the cognitive architectures ACT-R and CLARION since there is an interesting distinction in how they structure symbolic (explicit) and subsymbolic (implicit) cognition, influencing how they represent heuristic behavior. Currently, there is no systematic overview and comparison of how ACT-R and CLARION represent heuristics relevant to consumer decision-making. This paper aims to fill this knowledge gap by performing a systematic literature review on papers that contain an ACT-R or CLARION representation of heuristics relevant to consumer decision-making. The review uses four databases for the literature search: Scopus, Web of Science, IEEE Xplore, and ACM Digital Library. In total, 58 records have been screened, and 12 records have been included in the review. The review shows that ACT-R’s strength relies on representing heuristics by sequentially executing rule-based heuristics, while CLARION focuses on representing similarity- based heuristics by using bottom-up activation from its implicit layers. The results show a pattern in which the architectural structure mainly determines which heuristic strategies have been represented.

Recent developments in Artificial Intelligence offer new possibilities for the development of systems which adapt to human motivation or emotion. These can have a variety of applications such as making therapy more accessible or boosting student motivation or engagement. In order to gain an overview these applications, of adaption strategies and inputs are used and challenges researchers are facing during development, a Systematic Literature Review was conducted. The review follows PRISMA guidelines and includes from Scopus, Web of Science and IEEE Explore. Due to time limitations, only papers published in 2024 and 2025 were considered. The final review includes 139 papers, out of which only 6 target motivation. 46 were chatbots, and 53 were recommendation systems. Researchers primarily struggled with the interpretation of complex emotions, and the need for more adaptation options. Lastly, a strong need for extensive user testing and comprehensive data privacy measures was identified.

This paper is a systematic literature review (SLR) investigating how intelligent systems leverage learning-related information to adapt their behaviour. This paper is done according to PRISMA guidelines, which ensures reproducibility. For this review, we analysed 58 papers published after 2023. The review focused on types of inputs, system objectives, and application domains. The systems in this survey adapt their behaviour based on various inputs, such as facial expression, gestures, eye gaze, and user preferences. These systems enhance the learning experience by increasing user engagement and motivation. Many of these papers target the education domain, such as STEM, but there are also papers focused on motor skills training or cognitive training. Time constraints limited the scope of the review, particularly in identifying long-term trends. But the result can be a solid base for future research into adaptive learning and training platforms.

With an increased demand for personalized systems, adaptive systems can support its users by recognizing their cognitive state, and adapt different elements to improve the user's mental state. With a literature survey, the following question has been answered: how do intelligent systems acquire and use information related to user attention? The answer was found by following the PRISMA guidelines, using the identification flowchart, to ensure reproducibility of the identification and screening of the papers, in addition to applying consistent criteria to which articles could be included in the review. This process resulted in 74 papers that fit the criteria. The results showed a large variety in the sensor input, modeling, objectives and domain, while the adaptation strategies could be summarized by five categories: UI change, feedback timing, automation level, difficulty adjustment and behavioral feedback. Combinations of categories were also present. UI changes and feedback timing were the most popular categories, especially from 2015 onward. Difficulty adjustments were surprisingly rarely utilized, especially in articles focused on education, possibly because the adaptation requires additional complexity to be added to the system. Challenges described in the literature were mainly focused on short term improvements, instead of long term issues.

In an increasingly digital world, the ability of systems to adapt to individual users has become essential. Among cognitive variables informing such systems, the human short-term memory plays a crucial role, as it is responsible for perceiving, storing and retrieving data. This study explores the modalities in which various information about memory-related processes has been used in the field of Human-Centered AI (HCAI) by analyzing input methods, objectives, application domains and nature of implemented adaptations. By conducting a systematic literature review, the paper aims to address this gap, while identifying current trends and challenges. The 45 papers included in this study revealed that increasing or decreasing complexity of content or adjusting difficulty levels based on accuracy rates or time periods can improve human performance and support learning in computer-based environments. Such personalization are especially useful in the educational and healthcare domains. These results provide a basis of design guidelines for future research of adaptive mechanisms considering memory-related information. This survey is anticipated to be a starting point for upcoming developments in this field or succeeding reviews that could incorporate a wider range of the memory scope.

Recognizing facial emotions is key for social interaction, yet the subjective nature of emotion labeling poses challenges for automatic facial affect prediction. Variability in how individuals interpret emotions leads to uncertainty in training data for machine learning models. While multiple raters and interrater agreement (IRA) measures are used to address this, the extent of their use and their impact on dataset reliability is not well understood. This systematic literature review investigates the methodologies used to measure IRA in facial affect recognition datasets. Concrete eligibility and feasibility criteria were applied, and it resulted in 47 papers being retrieved from Scopus, Web of Science, IEEExplore, and ACM Digital Library. Data on affect states, affect representation schemes (ARS), and IRA methodologies used by the datasets and their corresponding papers were extracted to provide a comprehensive overview and allow a detailed analysis. Clear correlation was not found in between ARS and IRA, but the retrieved data showed that Fleiss' kappa was the most popular methodology over time but also in the recent years.

Emotional datasets for automatic affect prediction usually employ raters to annotate emotions or verify the annotations. To ensure the reliability of these raters some use interrater agreement measures, to verify the degree to which annotators agree with each other on what they rate. This systematic review explores what kind of interrater agreement measures are used in emotional speech corpora. The affective states, the affect representation schemes, and the collection method of the datasets as well as the popularity of these measures were investigated. Scopus, IEEE Xplore, Web of Science, and ACM digital library were used to extract papers that describe the creation of datasets. 45 papers were included in the review. The review concludes that the interrater agreement measures used, are highly dependent on the collection method for speech and the affect representation schemes. It was found that there is no standardized way to measure interrater agreement. Datasets that use actors to record emulated emotions mostly use recognition rate as their interrater agreement measures. Datasets that use a dimensional representation scheme often compute the mean agreement of the raters and the standard deviation of that measure to check the interrater agreement. Datasets that do not use actors nor are dimensional use a plethora of different measures such as probabilistic computing of agreement, or majority agreement measures, but a large amount use no measures at all.

This study explores the influence of interrater agreement measures and affect representation schemes in automatic affect prediction systems using physiological signals. These systems often use supervised learning and require unambiguous and objective labeling, a challenge when multiple human annotators are involved, which can affect model performance.
The research involved the first part of a two-stage process: systematically reviewing datasets and their characteristics concerning interrater agreement on the affective interpretation of physiological signals. This stage established a reliable foundation for the second step: a future analysis of model performance reported in technical papers utilizing these datasets. The main takeaways were that the number of raters varies significantly over datasets and the complexity introduced by combining affect representation schemes can negatively affect interrater agreement.

Human-computer interaction has long been the focus of technological evolution; however, in order for this type of system to reach its peak potential, machines must recognize that humans are constantly influenced by emotions. Text affective content analysis models are one attempt to integrate human psychology into computers, trying to detect the emotion transmitted by written input. There are numerous approaches to implementing such systems, with supervised learning still popular. The challenge of creating textual affective datasets is not in the availability of records, as humanity has reached a peak in data production, especially text, but in ensuring the consistency of the annotations provided by humans when included in the process. This study conducts a systematic literature review focused on providing details of published corpora. The annotation process, as well as any trends in how it evolved, will be examined to obtain dataset particularities. The ultimate intent is to lay the groundwork for an ample study aimed at analyzing the relationship between interrater agreement levels and performance scores of models trained on these datasets. The relevant literature was extracted from 3 search engines: Scopus, IEEE Xplore, and Web of Science, with a focus on manually labeled written records that are not part of multimodal systems, resulting in an analysis of 41 datasets. According to the aggregations, when humans are recruited to perform this task, researchers are more likely to use multiple annotators and calculate the degree of agreement between them to ensure the data's reliability before using it. The conducting researchers are inclined to either train these people before the procedure or tailor the set of labels to potentially increase the uniformity of ratings. As a result, this paper highlights the variety of annotation process characteristics and points towards standardizing this task.

With the rise in the number of human-computer interactions, the need for systems that can accurately infer and respond to users' emotions becomes increasingly important. One can achieve this by examining audio-visual signals, aiming to identify the underlying emotions from an individual's gestures, auditory cues, and surroundings. Such automatic affect prediction systems depend heavily on labeled datasets. However, the subjective nature of emotion interpretation often introduces uncertainty, making it challenging to create reliable and high-quality datasets. To combat this issue, researchers have tried employing multiple raters to judge the affective state of a person, while employing interrater agreement measures to monitor uncertainty. To this moment, it is still unclear to what extend does reaching a good level of interrater agreement impact the performance of audio-visual Automatic Affect Prediction models.
As a first step towards understanding the potential influences on performance, this paper conducts a systematic literature review to investigate the objective annotation procedure in audio-visual databases. The survey extracted relevant literature from 4 scientific databases: Scopus, IEEE Xplore, Web of Science and ACM Digital Library. The results are aggregated from 55 papers and presented by following the PRISMA guidelines. They indicate that most databases use multiple annotators, a little more than half measure interrater agreement, and most train the raters to increase the uniformity of the labels.

Understanding how users retrospectively evaluate their interactions with adaptive intelligent systems is crucial to improving their behaviours during interactions. Prior work has shown the potential to predict retrospective evaluations based on different real-time aspects of conversations, such as verbal cues and non-verbal behaviours. However, the relationship between how one retrospectively evaluates and the real-time evaluations in the moment of conversations remains unclear. This study investigates the relationship between real-time evaluations of a situation, using the Situational Interdependence Scale (SIS) framework, and its retrospective evaluations. We investigate the presence of the peak-end rule and a complex relationship that could be modelled using Long Short-Term Memory (LSTM) for each SIS dimension using the PACO dataset. Due to the absence of ground truth for real-time SIS evaluations, we also present a methodologically sound technical approach to utilize a Large Language Model (LLM) to estimate values for each SIS dimension for each spoken utterance in conversations. Analysis of the experiments revealed the absence of both the peak-end rule and an LSTM-modelled relationship across all dimensions of SIS. However, both types of models at least predict better than the average of the estimated real-time evaluation. This may be largely due to the inaccuracy of the estimated real-time SIS evaluations and the limited LLM’s capability of labelling real-time SIS in conversational data. Future works may focus on improving the annotation of real-time SIS evaluations through human annotation or human-supervised few-shot learning of LLM, using other modalities in combinations with verbal content, and exploring other predictive models.

Cognitive Impairment, commonly termed as Dementia, affects a large number of older adults. People with dementia (PwD) experience cognitive decline that impacts their ability to perform daily activities and maintain social connections. The number of PwD is expected to rise, and unfortunately, there is still no cure. A number of non drug therapies such as music and art therapy are used to stimulate neurons and slow down the progress of the disease. When symptoms become severe, full-time care may be needed, requiring a move to a nursing home for formal care. However, studies show that interaction with peers is limited in a care home, leading to loneliness and reduced quality of life. Caregivers organize group activities like singing and baking to alleviate these feelings, but their busy schedule can limit the frequency of these exercises. This begs the question: Can a robot support caregivers in the provision of group activities in a care home? This would allow caregivers more time to attend to the personal needs of the residents. It not only has the potential to reduce workload of caregivers but also creates opportunities for residents to interact with each other.

In this thesis, we propose the design of a robot which moderates a sing-along activity for a group of people living in a nursing home. An activity in the realm of music therapy is chosen since it is one of the non-drug therapies which has shown to have numerous benefits like improving memory recall, eliciting emotions and aiding relaxation. The robot session is curated to give opportunities for interaction with group members and with the robot too. While the song is playing and participants are singing along with the music, the robot performs engagement detection, and encourages those who do not seem involved.

Multiple ways are proposed to detect engagement in HRI, such as emotion recognition and estimation of head pose. These approaches have limitations in the scope of this thesis, particularly because these methods are not designed for groups of older adults residing in a care home. Given these limitations and our focus on the target group, we propose a novel engagement detection methodology, which leverages the presence of a robot and assigns an active role to it. Instead of relying on conventional passive engagement detection methods, this approach uses an interactive probing technique. The robot prompts users to perform certain actions, and whether or not they respond to it is monitored using pose estimation. This is termed robot engagement. This approach is combined with activity engagement, which indicates whether the participant is singing or not. With this hybrid technique, we aim to increase the efficacy of engagement detection. If a participant is detected as disengaged, either in the activity or in the robot, we provide personalized encouragement and motivation by addressing participants by name. Along with this, the robot also compliments those who actively participate, motivating them further...

Images possess the ability to convey a wide range of emotions, and extracting affective information from images is crucial for affect prediction systems. This process can be achieved through the application of machine learning algorithms. Categorical Emotion States (CES) and Dimensional Emotion Space (DES) are two typical models used for representing emotions. Moreover, the development of a mapping schema between these representations can contribute to benefit the research in AI and psychology. Consequently, this study focuses on investigating the feasibility of translating emotions from DES to CES. To accomplish this goal, relevant databases are identified and combined as the training data, and machine learning models, namely Naive Bayes, K-nearest Neighbors, and Decision Tree are employed to perform the classification task. The results indicate the superior performance of the K-nearest Neighbors classifier, exhibiting higher mean accuracy (60%) and low standard deviation (0.004) among all implemented classifiers. Overall, the translation from DES to CES offers several advantages, including a simplified and interpretable representation of emotions, as well as the provision of a common language for discussing and expressing emotions.

Studies in Music Affect Content Analysis use varying emotion schemes to represent the states induced when listening to music. However, there are limited studies that explore the translation between these representation schemes. This paper explores the feasibility of using machine learning models to translate from a dimensional scheme of Valence, Energy and Tension, to a categorical emotion of Anger, Fear, Happy, Sad, or Tender, specifically in the context of musical stimuli. Additionally, this paper considers how the close proximity of certain emotions in the dimensional space, such as Fear and Anger, negatively influence the performance of translation models. This paper reflects on past studies and presents new results concluding feasible translations of music affect content, moreover, providing suggestions for future analysis.

Physiological signals, such as Electroencephalogram (EEG), Glavic Skin Response (GSR), or Body Temperature, are common inputs for Automatic Affect Recognition (AAR) systems. One of the crucial elements of AAR is the Affect Representation Scheme (ARS) used to define the affective states recognized by the system (e.g., happiness, sadness, fear, anger). Throughout the years many AAR reviews have been published. However, most of them do not include a detailed analysis of ARSs and the motivation behind them. This paper aims to fill this knowledge gap by performing a Systematic Literature Review (SLR) of Computer Science papers that propose a Physiological-signal-based AAR (PAAR) system. We explore how researchers discuss and choose an ARS and whether they base it on actual psychological theories. Eligible papers are retrieved from 4 databases: Web Of Science, IEEExplore, Scopus, and ACM Digital Library. Due to time limitations, the review is done rapidly and some additional search constraints are applied for feasibility. The most significant restrictions are: considering papers published since 2020 and performing experiments on specific benchmarking datasets. We take these constraints and their possible impact into consideration when interpreting the results. The presented review procedure can be stripped from the additional filters and reused for a full review. In total 115 papers are processed. The majority of papers introduce an EEG-based emotion recognition system. The analysis of the extracted information reveals that dimensional ARSs, in particular, Valence/Arousal model is the most popular. Moreover, authors often choose to reduce the dimensions to high/low values. Categorical ARSs are less frequent and usually are adopted from the dataset. Lastly, the authors do not provide extensive motivation for the choice of ARS and rarely refer to psychological theories.