A Multi-Agent Extension of a Hierarchical Task Network Planning Formalism

A Hybrid Blockchain Model for Trusted Data of Supply Chain Finance

Supply chain finance is an effective way to solve the problem of capital turnover of construction enterprises and stabilize economic growth under epidemic situation. Blockchain technology can solve the problems in the current supply chain finance business, such as incorrect information between banks and enterprises, lack of visibility in transaction process, and possible joint fraud in the core enterprise model. However, it still has problems such as inconvenient information verification, data fraud, and difficulty to achieve a balance between efficiency, security and cost. This paper presents a hybrid chain model combining PANDA (a consensus algorithm based on public chain) and X-Alliance (a consensus algorithm based on alliance chain). Such proposed hybrid chain model can process the transaction of each account in parallel, asynchronize from other unrelated accounts in the network, provide more reliable data storage and authority management, and ensure the ownership of change tracking data, which has higher performance and lower protection cost while ensuring data security and privacy security. According to the data of network crawling, the experimental results show that the throughput can reach 1200tps when four nodes are deployed. The model serves for the supply chain financial data management of engineering projects, making trade authenticity review, risk assessment and credit transmission of core enterprises more efficient. On this basis, each participant can carry out analysis and give early warning of capital flow, timely analyze and calculate the authenticity of transaction background. The proposed method provides a reference for the normal operation of the project fund under the COVID-19 epidemic.

Robot Assistance in Dynamic Smart Environments-A Hierarchical Continual Planning in the Now Framework

By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner increases the computational load, and thus can delay the execution of a task. Moreover, tasks may be frequently replanned due to the unanticipated actions of humans. Our framework aims to mitigate these inadequacies. In this paper, we propose a continual planning framework, which incorporates the sensing and actuation capabilities of IoT devices into a robot’s state estimation, task planing and task execution. The robot’s onboard task planner queries a cloud-based framework for actuators, capable of the actions the robot cannot execute. Once generated, the plan is sent to the cloud back-end, which will inform the robot if any IoT device reports a state change affecting its plan. Moreover, a Hierarchical Continual Planning in the Now approach was developed in which tasks are split-up into subtasks. To delay the planning of actions that will not be promptly executed, and thus to reduce the frequency of replanning, the first subtask is planned and executed before the subsequent subtask is. Only information relevant to the current (sub)task is provided to the task planner. We apply our framework to a smart home and office scenario in which the robot is tasked with carrying out a human’s requests. A prototype implementation in a smart home, and simulator-based evaluation results, are presented to demonstrate the effectiveness of our framework.