Dynamic difficulty adjustment (DDA) is a method of automatically modifying a game’s features, behaviors, and scenarios in real-time, depending on the player’s skill, so that the player, when the game is very simple, does not feel bored or frustrated, when it is very difficult. The intent of the DDA is to keep the player engrossed till the end and to provide him/her with a challenging experience. In traditional games, difficulty levels increase linearly or stepwise during the course of the game. The features such as frequency, starting levels, or rates can be set only at the beginning of the game by choosing a level of difficulty. This can, however, result in a negative experience for players as they try to map a predecided learning curve. DDA attempts to solve this problem by presenting a customized solution for the gamers. This paper provides a review of the current approaches to DDA.

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One of the challenging problems in mobile robotics is mapping a dynamic environment for navigating robots. In order to disambiguate multiple moving obstacles, state-of-art techniques often solve some form of dynamic SLAM (Simultaneous Localization and Mapping) problem. Unfortunately, their higher computational complexity press the need for simpler and more efficient approaches suitable for real-time embedded systems. In this paper, we present a ROS-based efficient algorithm for constructing dynamic maps, which exploits the spatial-temporal locality for detecting and tracking moving objects without relying on prior knowledge of their geometrical features. A two-prong contribution of this work is as follows: first, an efficient scheme for decoding sensory data into an estimated time-varying object boundary that ultimately decides its orientation and trajectory based on the iteratively updated robot Field of View (FoV); second, lower time-complexity of updating the dynamic environment through manipulating spatial-temporal locality available in the object motion profile. Unlike existing approaches, the snapshots of the environment remain constant in the number of moving objects. We validate the efficacy of our algorithm on both V-Rep simulations and real-life experiments with a wide array of dynamic environments. We show that the algorithm accurately detects and tracks objects with a high probability as long as sensor noise is low and the speed of moving objects remains within acceptable limits.

In software industry, the DevOps is an increasingly adopting software development paradigm. Towards the sustainable DevOps adoption, there is a need to transform the organization´s Culture, Automation, Measurement and Sharing (CAMS) aspects concerning to core theme of continues development and operations. The software organizations face several complexities while implementing the DevOps principles. The sustainable DevOps implementation assist the software organizations to develop the quality projects with good return on investment. This evidence-based study aims to explore the guidelines of sustainable DevOps implementation, reported in literature and industry practices. Using systematic literature review and questionnaire survey, we identified the 48 guidelines for sustainable DevOps implementation. We further develop a decision-making framework aiming to assist the practitioners to consider the most significant set of guidelines on priority. The results show that out of CAMS, culture is the most important principle for sustainable DevOps implementation. Moreover, (i) enterprises should focus on building a collaborative culture with shared goals, (ii) assess your organization"s readiness to utilize a microservices architecture and (iii) educate executives at your company about the benefits of DevOps to gain resource and budget support are the highest priority guidelines for sustainable DevOps implementation. We believe that this in-depth study helps the practitioners to understand the core principles and guidelines for sustainable DevOps implementation.