Back in the 1990s we started work on pedagogical agents, a new user interface paradigm for interactive learning environments. Pedagogical agents are autonomous characters that inhabit learning environments and can engage with learners in rich, face-to-face interactions. Building on this work, in 2000 we, together with our colleague, Jeff Rickel, published an article on pedagogical agents that surveyed this new paradigm and discussed its potential. We made the case that pedagogical agents that interact with learners in natural, life-like ways can help learning environments achieve improved learning outcomes. This article has been widely cited, and was a winner of the 2017 IFAAMAS Award for Influential Papers in Autonomous Agents and Multiagent Systems (IFAAMAS, 2017). On the occasion of receiving the IFAAMAS award, and after twenty years of work on pedagogical agents, we decided to take another look at the future of the field. We’ll start by revisiting our predictions for pedagogical agents back in 2000, and examine which of those predictions panned out. Then, informed what we have learned since then, we will take another look at emerging trends and the future of pedagogical agents. Advances in natural language dialogue, affective computing, machine learning, virtual environments, and robotics are making possible even more lifelike and effective pedagogical agents, with potentially profound effects on the way people learn.

Negative: "I hate it", "It scares me" & "I am uncomfortable with it" Positive" "I am comfortable with it", "I am enthusiastic about it" & "I love it". Negative: "I hate it", "It scares me" & "I am uncomfortable with it" Positive" "I am comfortable with it", "I am enthusiastic about it" & "I love it". COMPAS software to generate several scores including predictions of "Risk of Recidivism" and "Risk of Violent Recidivism." COMPAS software to generate several scores including predictions of "Risk of Recidivism" and "Risk of Violent Recidivism." Digital twin refers to a digital replica of physical assets, processes and systems that can be used for various purposes.

R is an open source programming language and software environment for statistical computing and graphics. R was created in 1992 and is supported by the R Foundation for Statistical Computing. R is widely used among statisticians and data miners for data analysis. The popularity of R has increased in the recent years. Here's a list of the best R tutorials, books and courses to help you learn R programming language in 2018.

The series opens with "Intro to Drone Pilot Training" on July 14 at the college's Fort Omaha Campus. Students will be introduced to rules and regulations needed to fly drones and will finish the course by navigating drones through an indoor obstacle course.

Hey guys, I'm looking to make an application that uses neural text to speech for my Python program. I'm not sure what open source SOTA is like, would love to get some reference repositories to check out, especially if they have demos.

We consider the problem of detecting norm violations in open multi-agent systems (MAS). We show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations. The cost of providing the incentives is not borne by the MAS and does not come from fines charged for norm violations (fines may be impossible to levy in a system where agents are free to leave and rejoin again under a different identity). Instead, monitoring incentives come from (scrip) fees for accessing the services provided by the MAS. In some cases, perfect monitoring (and hence enforcement) can be achieved: no norms will be violated in equilibrium. In other cases, we show that, while it is impossible to achieve perfect enforcement, we can get arbitrarily close; we can make the probability of a norm violation in equilibrium arbitrarily small. We show using simulations that our theoretical results, which apply to systems with a large number of agents, hold for multi-agent systems with as few as 1000 agents--the system rapidly converges to the steady-state distribution of scrip tokens necessary to ensure monitoring and then remains close to the steady state.

Topic discovery has witnessed a significant growth as a field of data mining at large. In particular, time-evolving topic discovery, where the evolution of a topic is taken into account has been instrumental in understanding the historical context of an emerging topic in a dynamic corpus. Traditionally, time-evolving topic discovery has focused on this notion of time. However, especially in settings where content is contributed by a community or a crowd, an orthogonal notion of time is the one that pertains to the level of expertise of the content creator: the more experienced the creator, the more advanced the topic. In this paper, we propose a novel time-evolving topic discovery method which, in addition to the extracted topics, is able to identify the evolution of that topic over time, as well as the level of difficulty of that topic, as it is inferred by the level of expertise of its main contributors. Our method is based on a novel formulation of Constrained Coupled Matrix-Tensor Factorization, which adopts constraints well-motivated for, and, as we demonstrate, are essential for high-quality topic discovery. We qualitatively evaluate our approach using real data from the Physics and also Programming Stack Exchange forum, and we were able to identify topics of varying levels of difficulty which can be linked to external events, such as the announcement of gravitational waves by the LIGO lab in Physics forum. We provide a quantitative evaluation of our method by conducting a user study where experts were asked to judge the coherence and quality of the extracted topics. Finally, our proposed method has implications for automatic curriculum design using the extracted topics, where the notion of the level of difficulty is necessary for the proper modeling of prerequisites and advanced concepts.

Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically, relying on past students' data. In particular, the Hint Factory (Barnes & Stamper, 2008) recommends edits that are most likely to guide students from their current state towards a correct solution, based on what successful students in the past have done in the same situation. Still, the Hint Factory relies on student data being available for any state a student might visit while solving the task, which is not the case for some learning tasks, such as open-ended programming tasks. In this contribution we provide a mathematical framework for edit-based hint policies and, based on this theory, propose a novel hint policy to provide edit hints in vast and sparsely populated state spaces. In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states. Because the space of possible weighted averages is continuous, we call this approach the Continuous Hint Factory. In our experimental evaluation, we demonstrate that the Continuous Hint Factory can predict more accurately what capable students would do compared to existing prediction schemes on two learning tasks, especially in an open-ended programming task, and that the Continuous Hint Factory is comparable to existing hint policies at reproducing tutor hints on a simple UML diagram task.

Interpretability of the underlying AI representations is a key raison d'\^{e}tre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of learners' cognition and emotions for the purpose of supporting human learning and teaching. Over thirty years of research in ITS (also known as AI in Education) produced important work, which informs about how AI can be used in Education to best effects and, through the OLM research, what are the necessary considerations to make it interpretable and explainable for the benefit of learning. We argue that this work can provide a valuable starting point for a framework of interpretable AI, and as such is of relevance to the application of both knowledge-based and machine learning systems in other high-stakes contexts, beyond education.

Learning a new skill by observing another individual, the ability to imitate, is a key part of intelligence in human and animals. Can we enable a robot to do the same, learning to manipulate a new object by simply watching a human manipulating the object just as in the video below? The robot learns to place the peach into the red bowl after watching the human do so. Such a capability would make it dramatically easier for us to communicate new goals to robots – we could simply show robots what we want them to do, rather than teleoperating the robot or engineering a reward function (an approach that is difficult as it requires a full-fledged perception system). Many prior works have investigated how well a robot can learn from an expert of its own kind (i.e. through teleoperation or kinesthetic teaching), which is usually called imitation learning. However, imitation learning of vision-based skills usually requires a huge number of demonstrations of an expert performing a skill.