Search

Home automation is seen as a potential pillar of the smart city revolution that combines smart mobility, lifestyle and ecosystem governed by intelligent sensors connected to the internet. Households can save money and be more comfortable with automated appliances. The cost of electricity and user comfort are fundamentally contradictory, so they can be presented as a dynamic multi-objective optimization problem with fluctuating priorities for the customer to use various devices at different times. For this reason, this paper proposes an advanced Intelligent Home Energy Management (IHEM) approach based on reinforcement learning to achieve home demand response (DR) efficiency. The optimal formulation of the one-hour-ahead energy consumption scheduling problem is considered a Markov Decision Process (MDP) with discrete time steps. An efficient Neural Network (NN)-based approach with a Q-learning algorithm is developed to address this problem, enabling the IHEM system to achieve better cost-effective scheduling performance. The accurate data of electricity price and energy supplied by the Photovoltaic (PV) system are analyzed in sliding periods by machine learning for uncertainty prediction. Using the newly developed approach, which has the dual objective of minimizing the electricity bill, it is possible to obtain scheduling decisions for appliances and energy storage. The results show that the proposed optimization method reduces the monthly electricity costs by 20% compared to the Integer Linear Programming (ILP)-based HEMS method. • Artificial intelligence (AI) approach to monitor household energy consumption. • An efficient deep reinforcement learning algorithm to control activity recognition in smart homes. • Design and implementation of Intelligent home energy management system. • Converge promptly and considerably reduce operating expenses adopting AI-based approach. [ABSTRACT FROM AUTHOR]

The combinatorial optimization (CO) problems on the graph are the core and classic problems in artificial intelligence (AI) and operations research (OR). For example, the Vehicle Routing Problem (VRP) and Traveling Salesman Problem (TSP) are fascinating NP-hard problems and have important significance for the existing transportation system. Traditional methods such as heuristics methods, exact algorithms, and solution solvers can already find approximate solutions on small-scale graphs. However, they are helpless for large-scale graphs and other problems with similar structures. Moreover, traditional methods often require artificially designed heuristic functions to aid decision-making. In recent years, more and more work has focused on applying deep learning and reinforcement learning (RL) to learn heuristics, which allows us to learn the internal structure of the graph end-to-end and find the optimal path under the guidance of heuristic rules. However, most of these still need manual assistance, and the RL method used has the problems of low sampling efficiency and small searchable space. This paper proposes a novel framework (called OmegaZero) based on Alphago Zero, which does not prescribe expert experience or label data but is trained through self-play. We divide the learning into two stages: in the first stage, we employ graph attention network (GAT) and GRU to learn node representations and memory history trajectories. In the second stage, we employ Monte Carlo tree search (MCTS) and deep RL to search for the solution space and train the model. [ABSTRACT FROM AUTHOR]

Artificial intelligence is steadily increasing its impact on everyday life. Therefore, the societal issues of artificial intelligence have become an important concern in the AI research. The presence of data that reflects human biases towards historically discriminated groups defined by sensitive features such as race and gender, results in machine learning models which discriminate against these groups. In order to tackle the impact of bias in data, researchers developed a variety of specialized machine learning algorithms which are able to satisfy different fairness constraints imposed on the model. Group fairness constraints do not fit standard machine learning formulations easily due to their non-differentiable nature. In this paper we developed a technique for learning a fair classifier by Monte Carlo policy gradient method which naturally deals with such non-differentiable constraints. Our methodology focuses on direct optimization of both group fairness metric and predictive performance of the model. In addition, we propose two different variance reduction techniques of gradient estimation. We compare our models to seven other related and state-of-the-art models and demonstrate that they are able to achieve better trade-off between accuracy and unfairness. To the best of our knowledge, this is the first fair classification algorithm which solves the issue of non-differentiable constraints by reinforcement learning techniques. [ABSTRACT FROM AUTHOR]

The Deep Reinforcement Learning (DRL) algorithm, Proximal Policy Optimization (PPO2), is deployed on a custom spacecraft (S/C) build and loss model to determine if an Artificial Intelligence (AI) can learn to monitor satellite constellation health and determine an optimal replacement strategy. A custom environment is created to simulate how S/C are built, launched, generate revenue, and finally decay. The reinforcement learning agent successfully learned an optimal policy for two models: a Simplified Model where the financial cost of actions is ignored; and an Advanced Model where the financial cost of actions is a major element. In both models the AI monitors the constellations and takes multiple strategic and tactical actions to replace satellites to maintain constellation performance. The Simplified Model showed that the PPO2 algorithm was able to converge on an optimal solution after ∼ 200,000 simulations. The Advanced Model was much more difficult for the AI to learn, and thus, the performance drops during the early episodes, but eventually converges to an optimal policy at ∼ 25,000,000 simulations. With the Advanced Model, the AI is taking actions that are successfully providing strategies for constellation management and satellite replacements which include these actions' financial implications. Thus, the methods in this paper provide initial research developments towards a real-world tool and an AI application that can aid various Aerospace businesses in managing Low Earth Orbit (LEO) constellations. This type of AI application may become imperative for deploying and maintaining small satellite mega-constellations. [ABSTRACT FROM AUTHOR]