We present a Kalman smoothing framework based on modeling errors using the heavy tailed Student's t distribution, along with algorithms, convergence theory, open-source general implementation, and several important applications. The computational effort per iteration grows linearly with the length of the time series, and all smoothers allow nonlinear process and measurement models. Robust smoothers form an important subclass of smoothers within this framework. These smoothers work in situations where measurements are highly contaminated by noise or include data unexplained by the forward model. Highly robust smoothers are developed by modeling measurement errors using the Student's t distribution, and outperform the recently proposed L1-Laplace smoother in extreme situations with data containing 20% or more outliers. A second special application we consider in detail allows tracking sudden changes in the state. It is developed by modeling process noise using the Student's t distribution, and the resulting smoother can track sudden changes in the state. These features can be used separately or in tandem, and we present a general smoother algorithm and open source implementation, together with convergence analysis that covers a wide range of smoothers. A key ingredient of our approach is a technique to deal with the non-convexity of the Student's t loss function. Numerical results for linear and nonlinear models illustrate the performance of the new smoothers for robust and tracking applications, as well as for mixed problems that have both types of features.

As an alternative to variable selection or shrinkage in high dimensional regression, we propose to randomly compress the predictors prior to analysis. This dramatically reduces storage and computational bottlenecks, performing well when the predictors can be projected to a low dimensional linear subspace with minimal loss of information about the response. As opposed to existing Bayesian dimensionality reduction approaches, the exact posterior distribution conditional on the compressed data is available analytically, speeding up computation by many orders of magnitude while also bypassing robustness issues due to convergence and mixing problems with MCMC. Model averaging is used to reduce sensitivity to the random projection matrix, while accommodating uncertainty in the subspace dimension. Strong theoretical support is provided for the approach by showing near parametric convergence rates for the predictive density in the large p small n asymptotic paradigm. Practical performance relative to competitors is illustrated in simulations and real data applications.

In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce. Model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples.

Although the use of metric fluents is fundamental to many practical planning problems, the study of heuristics to support fully automated planners working with these fluents remains relatively unexplored. The most widely used heuristic is the relaxation of metric fluents into interval-valued variables --- an idea first proposed a decade ago. Other heuristics depend on domain encodings that supply additional information about fluents, such as capacity constraints or other resource-related annotations. A particular challenge to these approaches is in handling interactions between metric fluents that represent exchange, such as the transformation of quantities of raw materials into quantities of processed goods, or trading of money for materials. The usual relaxation of metric fluents is often very poor in these situations, since it does not recognise that resources, once spent, are no longer available to be spent again. We present a heuristic for numeric planning problems building on the propositional relaxed planning graph, but using a mathematical program for numeric reasoning. We define a class of producer--consumer planning problems and demonstrate how the numeric constraints in these can be modelled in a mixed integer program (MIP). This MIP is then combined with a metric Relaxed Planning Graph (RPG) heuristic to produce an integrated hybrid heuristic. The MIP tracks resource use more accurately than the usual relaxation, but relaxes the ordering of actions, while the RPG captures the causal propositional aspects of the problem. We discuss how these two components interact to produce a single unified heuristic and go on to explore how further numeric features of planning problems can be integrated into the MIP. We show that encoding a limited subset of the propositional problem to augment the MIP can yield more accurate guidance, partly by exploiting structure such as propositional landmarks and propositional resources. Our results show that the use of this heuristic enhances scalability on problems where numeric resource interaction is key in finding a solution.

This paper reports a model of a developmental agent. It is inspired by some characteristics of Piagets sensorimotor stage. During this stage essential skills for creative thinking are developed. Our computational model attempts to shed some light about how these abilities arise and, in this way, contribute to the study of the developmental side of computational creativity.

In previous work we presented a computational framework that allows a robot or agent to reason about whether it should trust an interactive partner or whether the interactive partner trusts the robot  (Wagner & Arkin, 2011). This article examines the use of this framework in a well-known situation for examining trust--the Investor-Trustee game (King-Casas, Tomlin, Anen, Camerer, Quartz, & Montague, 2005). Our experiment pits the robot against a person in this game and explores the impact of recognizing and responding to trust signals. Our results demonstrate that the recognition that a person has intentionally placed themselves at risk allows the robot to reciprocate and, by doing so, improve both individuals play in the game. This work has implications for home healthcare, search and rescue, and military applications.

Game-based learning has been proven an effective method to engage students in the class. However, it is very challenging to balance playability and learnability when only developing digital games. Some "playable" games may not carry sufficient knowledge; some "learnable" games may reduce the students' interest and curiosity. In this ongoing research, we proposed an innovative learning method, "game-initiated learning." This method consists of three main steps: game, discussion and self-directedlearning. In this model, students can experience real-world problems from the game, discuss problems they found in the game, and finally, the instructors can deliver related knowledge that is useful to solving the problems previously discussed. To validate the proposed method, we selected a topic of disaster education in Taiwan and experimentally developed a set of course materials including a digital game, animation videos and an e-book. We conducted a review meeting, inviting experts from hydraulic engineering, game development, and disaster mediation as well as schoolteachers and students. The reviewers were asked to play the games and review all course materials. From the feedbacks of the reviewers, we found game-initiated learning an educational method with great potential in providing tacit and explicit knowledge about disaster management.

The author discusses the relationship between symbolic play, abstract thinking, and divergent and associative thinking based on analogies, and finally connects symbolic play with the creative process. Play and the creative act are seen as similar by definition, since they are characterized as divergent, regulative, expressive and autotelic processes. Symbolic play is not only a product of the animistic and concrete logical way of thinking in childhood but also represents a mode of abstract thinking at the fictional symbolic level, which provides different options important for creativity development. Symbolic play is based on analogies with reality, and in this way reality is transformed in the imagination to be comprehended by the child. This transformation, which takes place in the nest of analogy at the symbolic level, is a key for creative production. Analogies in symbolic play are created through the divergent associative thinking process, also basic for any creative activity. The author has already used play as a tool to enhance creative behavior among young students in primary schools, and currently one project is being implemented in Serbia by the Institute for Educational Research with the intention of promoting initiative, cooperation and creativity by using play among other learning methods.

Online social media has become an integral part of our social lives. Online social interactions are distinct from face-to-face interactions. Different social media types have enabled novel forms of social exchanges to take place. Individuals vary greatly in their online behaviors and preferences. The current research argued for the significance of understanding individual differences in social media behaviors from brain structure variability. Using a novel approach that combined methodologies from personality and neuroscience, this research found that variations in social media behaviors and preferences were reliably reflected in brain structure. Interestingly, the general preference for an online mode of social interaction reflected decreased volumes of grey matter in regions involved in facial and speech processing. The associations between patterns of media behaviors and brain structure obtained in this research had demonstrated the feasibility of adopting a neuroscience approach to explain the complex differences in media behaviors.

This paper reports a model of a developmental agent. It is inspired by some characteristics of Piagets sensorimotor stage. During this stage essential skills for creative thinking are developed. Our computational model attempts to shed some light about how these abilities arise and, in this way, contribute to the study of the developmental side of computational creativity.