Concept of a Robust & Training-free Probabilistic System for Real-time Intention Analysis in Teams

Die Arbeit beschäftigt sich mit der Analyse von Teamintentionen in Smart Environments (SE). Die fundamentale Aussage der Arbeit ist, dass die Entwicklung und Integration expliziter Modelle von Nutzeraufgaben einen wichtigen Beitrag zur Entwicklung mobiler und ubiquitärer Softwaresysteme liefern können. Die Arbeit sammelt Beschreibungen von menschlichem Verhalten sowohl in Gruppensituationen als auch Problemlösungssituationen. Sie untersucht, wie SE-Projekte die Aktivitäten eines Nutzers modellieren, und liefert ein Teamintentionsmodell zur Ableitung und Auswahl geplanten Teamaktivitäten mittels der Beobachtung mehrerer Nutzer durch verrauschte und heterogene Sensoren. Dazu wird ein auf hierarchischen dynamischen Bayes’schen Netzen basierender Ansatz gewählt

Active physical inference via reinforcement learning

When encountering unfamiliar physical objects, children andadults often perform structured interrogatory actions such asgrasping and prodding, so revealing latent physical propertiessuch as masses and textures. However, the processes drivingand supporting these curious behaviors are still largely mys-terious. In this paper, we develop and train an agent able toactively uncover latent physical properties such as the massand force of objects in a simulated physical “micro-world’.Concretely, we used a simulation-based-inference frameworkto quantify the physical information produced by observationand interaction with the evolving dynamic environment. Weused model-free reinforcement learning algorithm to train anagent to implement general strategies for revealing latent phys-ical properties. We compare the behaviors of this agent to thehuman behaviors observed in a similar task

QLens: Visual analytics of multi-step problem-solving behaviors for improving question design

With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students' problem-solving processes unfold step by step to infer whether students' problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from the online platforms, provides the opportunity to analyze problem-solving behaviors. However, it is still challenging to interpret, summarize, and compare the high dimensional problem-solving sequence data. In this paper, we present a visual analytics system, QLens, to help question designers inspect detailed problem-solving trajectories, compare different student groups, distill insights for design improvements. In particular, QLens models problem-solving behavior as a hybrid state transition graph and visualizes it through a novel glyph-embedded Sankey diagram, which reflects students' problem-solving logic, engagement, and encountered difficulties. We conduct three case studies and three expert interviews to demonstrate the usefulness of QLens on real-world datasets that consist of thousands of problem-solving traces

Amortised experimental design and parameter estimation for user models of pointing

Funding Information: This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artifcial Intelligence FCAI) and ELISE Networks of Excellence Centres (EU Horizon:2020 grant agreement 951847) and Bitville Oy. The authors want to thank the Probabilistic Machine Learning (PML) and the User Interfaces research groups at Aalto University for fruitful discussions and feedback. Publisher Copyright: © 2023 Owner/Author. | openaire: EC/H2020/951847/EU//ELISEUser models play an important role in interaction design, supporting automation of interaction design choices. In order to do so, model parameters must be estimated from user data. While very large amounts of user data are sometimes required, recent research has shown how experiments can be designed so as to gather data and infer parameters as efficiently as possible, thereby minimising the data requirement. In the current article, we investigate a variant of these methods that amortises the computational cost of designing experiments by training a policy for choosing experimental designs with simulated participants. Our solution learns which experiments provide the most useful data for parameter estimation by interacting with in-silico agents sampled from the model space thereby using synthetic data rather than vast amounts of human data. The approach is demonstrated for three progressively complex models of pointing.Peer reviewe

Automated posture analysis for detecting learner's affective state

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (leaves 87-94).As means of improving the ability of the computer to respond in a way that facilitates a productive and enjoyable learning experience, this thesis proposes a system for the automated recognition and dynamical analysis of natural occurring postures when a child is working in a learning-computer situation. Specifically, an experiment was conducted with 10 children between 8 and 11 years old to elicit natural occurring behaviors during a learning-computer task. Two studies were carried out; the first study reveals that 9 natural occurring postures are frequently repeated during the children's experiment; the second one shows that three teachers could reliably recognize 5 affective states (high interest, interest, low interest, taking a break and boredom). Hence, a static posture recognition system that distinguishes the set of 9 postures was built. This system senses the postures using two matrices of pressure sensors mounted on the seat and back of a chair. The matrices capture the pressure body distribution of a person sitting on the chair. Using Gaussian Mixtures and feed-forward Neural Network algorithms, the system classifies the postures in real time. It achieves an overall accuracy of 87.6% when it is tested with children's postures that were not included in the training set. Also, the children's posture sequences were dynamically analyzed using a Hidden Markov Model for representing each of the 5 affective states found by the teachers. As a result, only the affective states of high interest, low interest, and taking a break were recognized with an overall accuracy of 87% when tested with new postures sequences coming from children included in the training set. In contrast, when the system was tested with posture sequences coming from two subjects that were not included in the training set, it had an overall accuracy of 76%.by Selene Atenea Mota Toledo.S.M

Unnatural pedagogy : a computational analysis of children\u27s learning to learn from other people.

Infants rely on others for much of what they learn. People are a ready source of quick information, but people produce data differently than the world. Data from a person are a result of that person\u27s knowledgeability and intentions. People may produce inaccurate or misleading data. On the other hand, if a person is knowledgeable about the world and intends to teach, that person may produce data that are more useful than simply accurate data: data that are pedagogical. This idea that people have special innate methods for efficient information transfer lies at the heart of recent proposals regarding what makes humans such powerful knowledge accumulators. These innate assumptions result in developmental patterns observed in epistemic trust research. This research seeks to create a computational account of the development of these abilities. We argue that pedagogy is not innate, but rather that people learn to learn from others. We employ novel computational models to show that there is sufficient data early on from which infants may learn that people choose data pedagogically, that the development of children\u27s epistemic trust is primarily a result of their decreasing beliefs that all informants are helpful, and that innate pedagogy would not lead to more rapid learning. We connect results from the pedagogy and epistemic trust literatures across tasks and development, showing that these are different manifestations of the same underlying abilities, and show that pedagogy need not be innate to have powerful implications for learning

Towards a unified approach

"Decision-making in the presence of uncertainty is a pervasive computation. Latent variable decoding—inferring hidden causes underlying visible effects—is commonly observed in nature, and it is an unsolved challenge in modern machine learning. On many occasions, animals need to base their choices on uncertain evidence; for instance, when deciding whether to approach or avoid an obfuscated visual stimulus that could be either a prey or a predator. Yet, their strategies are, in general, poorly understood. In simple cases, these problems admit an optimal, explicit solution. However, in more complex real-life scenarios, it is difficult to determine the best possible behavior. The most common approach in modern machine learning relies on artificial neural networks—black boxes that map each input to an output. This input-output mapping depends on a large number of parameters, the weights of the synaptic connections, which are optimized during learning.(...)