Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents with commonsense knowledge. Such knowledge would allow agents to efficiently act in the world by pruning out implausible actions, and to perform look-ahead planning to determine how current actions might affect future world states. We design a new text-based gaming environment called TextWorld Commonsense (TWC) for training and evaluating RL agents with a specific kind of commonsense knowledge about objects, their attributes, and affordances. We also introduce several baseline RL agents which track the sequential context and dynamically retrieve the relevant commonsense knowledge from ConceptNet. We show that agents which incorporate commonsense knowledge in TWC perform better, while acting more efficiently. We conduct user-studies to estimate human performance on TWC and show that there is ample room for future improvement.

The ability to engage in goal-oriented conversations has allowed humans to gain knowledge, reduce uncertainty, and perform tasks more efficiently.Artificial agents, however, are still far behind humans in having goaldriven conversations.In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective.This task is challenging, since these questions must not only be consistent with a strategy to achieve a goal, but also consider the contextual information in the image.We propose an end-to-end goal-oriented visual dialogue system, that combines reinforcement learning with regularized information gain.Unlike previous approaches that have been proposed for the task, our work is motivated by the Rational Speech Act framework, which models the process of human inquiry to reach a goal.We test the two versions of our model on the GuessWhat?! dataset, obtaining significant results that outperform the current state-of-the-art models in the task of generating questions to find an undisclosed object in an image.

Developmental Disorders are chronic disabilities that have a severe impact on the day to day functioning of a large section of the human population. Recognizing developmental disorders from facial images is an important but a relatively unexplored challenge in the field of computer vision. This paper proposes a novel framework to detect developmental disorders from facial images. A spectrum of disorders constituting of Autism Spectrum Disorder, Cerebral Palsy, Fetal Alcohol Syndrome, Down syndrome, Intellectual disability and Progeria have been considered for recognition. The framework relies on Deep Convolutional Neural Networks (DCNN) for feature extraction. A new data-set comprising of images of subjects with these disabilities was built for testing the performance of the frame work. This model has been tested on different age groups, individual disabilities and has also been compared to a similar model that uses human intelligence to identify different developmental disorders. The results indicate that the model performs better than average human intelligence in terms of differentiating amongst different disabilities and is able to recognize subjects with these developmental disorders with an accuracy of 98.80%.