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Extracting structured knowledge from texts has traditionally been used for knowledge base generation. However, other sources of information, such as images can be leveraged into this process to build more complete and richer knowledge bases. Structured semantic representation of the content of an image and knowledge graph embeddings can provide a unique representation of semantic relationships between image entities. Linking known entities in knowledge graphs and learning open-world images using language models has attracted lots of interest over the years. In this paper, we propose a shared learning approach to learn semantic attributes of images by combining a knowledge graph embedding model with the recognized attributes of images. The proposed model premises to help us understand the semantic relationship between the entities of an image and implicitly provide a link for the extracted entities through a knowledge graph embedding model. Under the limitation of using a custom user-defined knowledge base with limited data, the proposed model presents significant accuracy and provides a new alternative to the earlier approaches. The proposed approach is a step towards bridging the gap between frameworks which learn from large amounts of data and frameworks which use a limited set of predicates to infer new knowledge.

For rapid growth in technology and automation, human tasks are being taken over by robots as robots have proven to be better with both speed and precision. One of the major and widespread usage of these robots is in the industrial businesses, where they are employed to carry massive loads in and around work areas. As these working environments might not be completely localized and could be dynamically changing, new approaches must be evaluated to guarantee a crash-free way of performing duties.This paper presents a new and efficient fusion algorithm for solving path planning problem in a custom 2D environment. This fusion algorithm integrates an improved and optimized version of both, A* algorithm and the Artificial potential field method. Firstly, an initial or preliminary path is planned in the environmental model by adopting A* algorithm. The heuristic function of this A* algorithm is optimized and improved according to the environmental model. This is followed by selecting and saving the key nodes in the initial path Lastly, on the basis of these saved key nodes, path smoothing is done by artificial potential field method. Our simulation results carried out using Python viz. libraries indicate that the new fusion algorithm is feasible and superior in smoothness performance and can satisfy as a time-efficient and cheaper alternative to conventional A* strategies of path planning.

Model-based reinforcement learning strategies are believed to exhibit more significant sample complexity than model-free strategies to control dynamical systems, such as quadcopters. This belief that Model-based strategies that involve the use of welltrained neural networks for making such high-level decisions always give better performance can be dispelled by making use of Model-free policy search methods. This paper proposes the use of a model-free random searching strategy, called Augmented Random Search (ARS), which is a better and faster approach of linear policy training for continuous control tasks like controlling a Quadcopter's flight. The method achieves state-of-the-art accuracy by eliminating the use of too much data for the training of neural networks that are present in the previous approaches to the task of Quadcopter control. The paper also highlights the performance results of the searching strategy used for this task in a strategically designed task environment with the help of simulations. Reward collection performance over 1000 episodes and agent's behavior in flight for augmented random search is compared with that of the behavior for reinforcement learning state-of-the-art algorithm, called Deep Deterministic policy gradient(DDPG) Our simulations and results manifest that a high variability in performance is observed in commonly used strategies for sample efficiency of such tasks but the built policy network of ARS-Quad can react relatively accurately to step response providing a better performing alternative to reinforcement learning strategies.

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