This paper describes a machine induction program (WITT) that attempts to model human categorization. Properties of categories to which human subjects are sensitive includes best or prototypical members, relative contrasts between putative categories, and polymorphy (neither necessary or sufficient features). This approach represents an alternative to usual Artificial Intelligence approaches to generalization and conceptual clustering which tend to focus on necessary and sufficient feature rules, equivalence classes, and simple search and match schemes. WITT is shown to be more consistent with human categorization while potentially including results produced by more traditional clustering schemes. Applications of this approach in the domains of expert systems and information retrieval are also discussed.

During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in which the regularizer can be expressed as the composition of a convex function $\omega$ with a linear function. This setting includes several methods such the group Lasso, the Fused Lasso, multi-task learning and many more. We present a general approach for solving regularization problems of this kind, under the assumption that the proximity operator of the function $\omega$ is available. Furthermore, we comment on the application of this approach to support vector machines, a technique pioneered by the groundbreaking work of Vladimir Vapnik.

Robust visual tracking for long video sequences is a research area that has many important applications. The main challenges include how the target image can be modeled and how this model can be updated. In this paper, we model the target using a covariance descriptor, as this descriptor is robust to problems such as pixel-pixel misalignment, pose and illumination changes, that commonly occur in visual tracking. We model the changes in the template using a generative process. We introduce a new dynamical model for the template update using a random walk on the Riemannian manifold where the covariance descriptors lie in. This is done using log-transformed space of the manifold to free the constraints imposed inherently by positive semidefinite matrices. Modeling template variations and poses kinetics together in the state space enables us to jointly quantify the uncertainties relating to the kinematic states and the template in a principled way. Finally, the sequential inference of the posterior distribution of the kinematic states and the template is done using a particle filter. Our results shows that this principled approach can be robust to changes in illumination, poses and spatial affine transformation. In the experiments, our method outperformed the current state-of-the-art algorithm - the incremental Principal Component Analysis method, particularly when a target underwent fast poses changes and also maintained a comparable performance in stable target tracking cases.

We address the problem faced by an autonomous agent that must achieve quick responses to a family of qualitatively-related tasks, such as a robot interacting with different types of human participants. We work in the setting where the tasks share a state-action space and have the same qualitative objective but differ in the dynamics and reward process. We adopt a transfer approach where the agent attempts to exploit common structure in learnt policies to accelerate learning in a new one. Our technique consists of a few key steps. First, we use a probabilistic model to describe the regions in state space which successful trajectories seem to prefer. Then, we extract policy fragments from previously-learnt policies for these regions as candidates for reuse. These fragments may be treated as options with corresponding domains and termination conditions extracted by unsupervised learning. Then, the set of reusable policies is used when learning novel tasks, and the process repeats. The utility of this method is demonstrated through experiments in the simulated soccer domain, where the variability comes from the different possible behaviours of opponent teams, and the agent needs to perform well against novel opponents.

The recently developed Efficient Lifelong Learning Algorithm (ELLA) acquires knowledge incrementally over a sequence of tasks, learning a repository of latent model components that are sparsely shared between models. ELLA shows strong performance in comparison to other multi-task learning algorithms, achieving nearly identical performance to batch multi-task learning methods while learning tasks sequentially in three orders of magnitude (over 1,000x) less time. In this paper, we evaluate several curriculum selection methods that allow ELLA to actively select the next task for learning in order to maximize performance on future learning tasks. Through experiments with three real and one synthetic data set, we demonstrate that active curriculum selection allows an agent to learn up to 50% more efficiently than when the agent has no control over the task order.

In many real-life design problems, there is a requirement to simultaneously balance multiple tasks or objectives in the system that are conflicting in nature, where minimizing one objective causes another to increase in value, thereby resulting in trade-offs between the objectives. For example, in embedded multi-core mobile devices and very large scale data centers, there is a continuous problem of simultaneously balancing interfering goals of maximal power savings and minimal performance delay with varying trade-off values for different application workloads executing on them. Typically, the optimal trade-offs for the executing workloads, lie on a difficult to determine optimal Pareto front. The nature of the problem requires learning over the lifetime of the mobile device or server with continuous evaluation and prediction of the trade-off settings on the system that balances the interfering objectives optimally. Towards this, we propose an on-line learning method, where the weights of experts for addressing the objectives are updated based on a convex combination of their relative performance in addressing all objectives simultaneously. An additional importance vector that assigns relative importance to each objective at every round is used, and is sampled from a convex cone pointed at the origin Our preliminary results show that the convex combination of the importance vector and the gradient of the potential functions of the learner's regret with respect to each objective ensure that in the next round, the drift (instantaneous regret vector), is the Pareto descent direction that enables better convergence to the optimal Pareto front.

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.

Conventional paradigms of machine learning assume all the training data are available when learning starts. However, in lifelong learning, the examples are observed sequentially as learning unfolds, and the learner should continually explore the world and reorganize and refine the internal model or knowledge of the world. This leads to a fundamental challenge: How to balance long-term and short-term goals and how to trade-off between information gain and model complexity? These questions boil down to “what objective functions can best guide a lifelong learning agent?” Here we develop a sequential Bayesian framework for lifelong learning, build a taxonomy of lifelong-learning paradigms, and examine information-theoretic objective functions for each paradigm, with an emphasis on predictive and active learning. The objective functions can provide theoretical criteria for designing algorithms and determining effective strategies for selective sampling, representation discovery, knowledge transfer, and continual update over a lifetime of experience.

The recently developed Efficient Lifelong Learning Algorithm (ELLA) acquires knowledge incrementally over a sequence of tasks, learning a repository of latent model components that are sparsely shared between models. ELLA shows strong performance in comparison to other multi-task learning algorithms, achieving nearly identical performance to batch multi-task learning methods while learning tasks sequentially in three orders of magnitude (over 1,000x) less time. In this paper, we evaluate several curriculum selection methods that allow ELLA to actively select the next task for learning in order to maximize performance on future learning tasks. Through experiments with three real and one synthetic data set, we demonstrate that active curriculum selection allows an agent to learn up to 50% more efficiently than when the agent has no control over the task order.

We address the problem faced by an autonomous agent that must achieve quick responses to a family of qualitatively-related tasks, such as a robot interacting with different types of human participants. We work in the setting where the tasks share a state-action space and have the same qualitative objective but differ in the dynamics and reward process. We adopt a transfer approach where the agent attempts to exploit common structure in learnt policies to accelerate learning in a new one. Our technique consists of a few key steps. First, we use a probabilistic model to describe the regions in state space which successful trajectories seem to prefer. Then, we extract policy fragments from previously-learnt policies for these regions as candidates for reuse. These fragments may be treated as options with corresponding domains and termination conditions extracted by unsupervised learning. Then, the set of reusable policies is used when learning novel tasks, and the process repeats. The utility of this method is demonstrated through experiments in the simulated soccer domain, where the variability comes from the different possible behaviours of opponent teams, and the agent needs to perform well against novel opponents.