Recognizing the actions, plans, and goals of a person in an unconstrained environment is a key feature that future robotic systems will need in order to achieve a natural human-machine interaction. Indeed, we humans are constantly understanding and predicting the actions and goals of others, which allows us to interact in intuitive and safe ways. While action and plan recognition are tasks that humans perform naturally and with little effort, they are still an unresolved problem from the point of view of artificial intelligence. The immense variety of possible actions and plans that may be encountered in an unconstrained environment makes current approaches be far from human-like performance. In addition, while very different types of algorithms have been proposed to tackle the problem of activity, plan, and goal (intention) recognition, these tend to focus in only one part of the problem (e.g., action recognition), and techniques that address the problem as a whole have been not so thoroughly explored. This review is meant to provide a general view of the problem of activity, plan, and goal recognition as a whole. It presents a description of the problem, both from the human perspective and from the computational perspective, and proposes a classification of the main types of approaches that have been proposed to address it (logic-based, classical machine learning, deep learning, and brain-inspired), together with a description and comparison of the classes. This general view of the problem can help on the identification of research gaps, and may also provide inspiration for the development of new approaches that address the problem in a unified way.

Unsupervised feature learning refers to the problem of learning useful feature extraction functions from unlabeled data. Despite the great success of deep learning networks in this task in recent years, both for static and for sequential data, these systems can in general still not compete with the high performance of our brain at learning to extract useful representations from its sensory input. We propose the Neocortex-Inspired Locally Recurrent Neural Network: a new neural network for unsupervised feature learning in sequential data that brings ideas from the structure and function of the neocortex to the well-established fields of machine learning and neural networks. By mimicking connection patterns in the feedforward circuits of the neocortex, our system tries to generalize some of the ideas behind the success of convolutional neural networks to types of data other than images. To evaluate the performance of our system at extracting useful features, we have trained different classifiers using those and other learnt features as input and we have compared the obtained accuracies. Our system has shown to outperform other shallow feature learning systems in this task, both in terms of the accuracies achieved and in terms of how fast the classification task is learnt. The results obtained confirm our system as a state-of-the-art shallow feature learning system for sequential data, and suggest that extending it to or integrating it into deep architectures may lead to new successful networks that are competent at dealing with complex sequential tasks.