High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. Mining high utility itemsets in a database with $n$ items generally results in a huge search space, composed of $2^{n}$ itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance AlgorithM for Mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.

Healthcare networking and computing infrastructure is rapidly changing from closed environments to open environments that incorporate ubiquitous medical devices and novel application scenarios. For example, home-based healthcare involves the collection and analysis of data from pervasive sensors (often in real time) to guide therapy or to perform medical interventions. In this article, we address the challenges of data interoperability and regulatory compliance when designing and deploying healthcare applications in a heterogeneous home-edge-cloud environment. We propose the ChainSDI framework that leverages the blockchain technique along with abundant edge computing resources to manage secure data sharing and computing on sensitive patient data. We implement programmable ChainSDI application programming interfaces to facilitate the specification of home-based healthcare services running on a software-defined infrastructure (SDI). This article is a collaboration between computer scientists, medical researchers, healthcare IT professionals as well as healthcare providers with the goal of increasing the availability of SDI infrastructures while meeting the performance and regulatory requirements of healthcare applications. Our prototype demonstrates the feasibility of the framework and serves as a testbed for novel experimental studies in the context of emerging healthcare applications.

Reinforcement Learning (RL) agents often encounter the bottleneck of the performance when the dilemma of exploration and exploitation arises. In this study, an adaptive exploration strategy with multi-attribute decision-making is proposed to address the trade-off problem between exploration and exploitation. Firstly, the proposed method decomposes a complex task into several sub-tasks and trains each sub-task using the same training method individually. Then, the proposed method uses a multi-attribute decision-making method to develop an action policy integrating the training results of these trained sub-tasks. There are practical advantages to improve learning performance by allowing multiple learners to learn in parallel. An adaptive exploration strategy determines the probability of exploration depending on the information entropy instead of the suffocating work of empirical tuning. Finally, transfer learning extends the applicability of the proposed method. The experiment of the robot migration, the robot confrontation, and the real wheeled mobile robot are used to demonstrate the availability and practicability of the proposed method.

In this paper, deep reinforcement learning (DRL) and knowledge transfer are used to achieve the effective control of the learning agent for the confrontation in the multi-agent systems. Firstly, a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm with parameter sharing is proposed to achieve confrontation decision-making of multi-agent. In the process of training, the information of other agents is introduced to the critic network to improve the strategy of confrontation. The parameter sharing mechanism can reduce the loss of experience storage. In the DDPG algorithm, we use four neural networks to generate real-time action and Q-value function respectively and use a momentum mechanism to optimize the training process to accelerate the convergence rate for the neural network. Secondly, this paper introduces an auxiliary controller using a policy-based reinforcement learning (RL) method to achieve the assistant decision-making for the game agent. In addition, an effective reward function is used to help agents balance losses of enemies and our side. Furthermore, this paper also uses the knowledge transfer method to extend the learning model to more complex scenes and improve the generalization of the proposed confrontation model. Two confrontation decision-making experiments are designed to verify the effectiveness of the proposed method. In a small-scale task scenario, the trained agent can successfully learn to fight with the competitors and achieve a good winning rate. For large-scale confrontation scenarios, the knowledge transfer method can gradually improve the decision-making level of the learning agent.

Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Q-learning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.