Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

An overarching problem with Learning-Enabled Systems (LES) is determining whether training data is sufficient to ensure the LES is resilient to environmental uncertainty and how to obtain better training data to improve the system's performance when it is not. Automated methods can ease the burden for developers by augmenting real-world data with synthetically generated data. We propose an evolution-based method to assist developers with the assessment of learning-enabled systems in environments not covered by available datasets. We have developed Enki, a tool that can generate various conditions of the environment in order to discover properties that lead to diverse and unique system behaviors. These environmental properties are then used to construct synthetic data for two purposes: (1) to assess a system's performance in an uncertain environment and (2) to improve system resilience in the presence of uncertainty. We show that our technique outperforms a random generation method when assessing the effect of multiple adverse environmental conditions on a Deep Neural Network (DNN) trained for the commonly-used CIFAR-10 benchmark.

Enhancing Learning-Enabled Software Systems to Address Environmental Uncertainty

Fully Autonomous Driving is considered as one of the difficult problems faced the automotive applications. It is forbidden due to the presence of some restricted Laws that prevent cars from being autonomous for the fear of accidents occurrence. However, researchers try to reach autonomous driving as a new area for research for the aim of having a strong push against these restricted Laws. Crash Avoidance functionality is one of the most important features in Self-Driving Cars that is partially integrated recently. We propose an end-to-end Crash Avoidance Deep IoT solution which is decomposed into two main parts: a) Detection Deep Neural Network which aims to detect accident occurrence in front of the ego-vehicle, and b) Accident Information Spreading IoT which is responsible for informing upcoming vehicles that there is an accident, then these vehicles will be able to take the reasonable actions either changing their routes, or changing their lanes avoiding crash. Due to the lack of Crash benchmarks, we build our own benchmark, depending only on a front camera, using ROS-Gazebo Simulation environment covering various crashes situations. In General, our proposed idea is the first solution that merges Deep Learning with IoT in automotive applications.

End-to-End Crash Avoidance Deep IoT-based Solution

The avoidance of multiple space debris collisions by autonomous spacecraft has garnered significant interests worldwide. Applying deep reinforcement learning (DRL) to autonomous spacecraft collision avoidance problems is still difficult because of limitations on constraint satisfaction and environment state perception, even if DRL is a suitable model-free and data-driven framework. In this research, a state-of-the-art penalized proximal policy optimization (P3O) method is applied to address the spacecraft's autonomous collision avoidance problem, which is formalized as a constrained Markov decision process (CMDP). In contrast with traditional DRL methods, P3O promises to satisfy multiple constraints in actual spaceship operations while also facilitating efficient learning in multi-dimensional, continuous state and action spaces. The scalability of the P3O algorithm is enhanced by combining the feature extraction capabilities and variable-length input capabilities of the long short-term memory (LSTM) networks, enabling the P3O to adapt to a variable number of space debris without the need for network retraining. The performance of the proposed method is compared with other five methods through simulation cases, which verifies the superior performance of the proposed method in terms of scalability, energy consumption, collision probability and constraint satisfaction. 

Autonomous spacecraft collision avoidance with a variable number of space debris based on safe reinforcement learning

Mobile robot monocular vision-based obstacle avoidance algorithm using a deep neural network

Deep learning has been rapidly growing in recent years obtaining excellent results for many computer vision applications, such as image classification and object detection. One aspect for the incre ...

Deep Learning for Autonomous Collision Avoidance

Constraint Programming can be used to solve many problems and this thesis is about getting an overview on the usage of Constraint Programming for Constraint Satisfaction Problems, both interactive ...

Usage of Constraint Programming for Nurse Rostering Problems : A literature study

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Deep Learning for Autonomous Collision Avoidance

Usage of Constraint Programming for Nurse Rostering Problems : A literature study