Unmanned ship navigates on the water in an autonomous or semiautonomous way, which can be widely used in maritime transportation, intelligence collection, maritime training and testing, reconnaissance, and evidence collection. In this paper, we use deep reinforcement learning to solve the optimization problem in the path planning and management of unmanned ships. Specifically, we take the waiting time (phase and duration) at the corner of the path as the optimization goal to minimize the total travel time of unmanned ships passing through the path. We propose a new reward function, which considers the environment and control delay of unmanned ships at the same time, which can reduce the coordination time between unmanned ships at the same time. In the simulation experiment, through the quantitative and qualitative results of deep reinforcement learning of unmanned ship navigation and path angle waiting, the effectiveness of our solution is verified.

Interactive genetic algorithms have been used in a wide variety of applications and extensively developed to facilitate the personalization and customization of products for users. However, the ambiguity effect or cognitive ambiguity of users during the product customization process will affect the effects of the final customized product. Here, we first deconstructed the ambiguity effect into cognitive ambiguity during early decision-making and that during the decision-making process. A spatial mapping strategy that involves "text-image-symbol" and a clustering strategy were then proposed to mitigate cognitive ambiguity in the two different stages, respectively. Then, a specific application example-"Chinese vase design" was studied. Based on the proposed strategy and interactive genetic algorithm, a prototype of computer-aided design system for product modeling was developed by MATLAB software. Then, 10 users were invited to experiment at three different cases (not using the proposed strategy, using spatial mapping strategy, using spatial mapping strategy and scheme clustering strategy). The experiment results have verified that the spatial mapping strategy was efficient to solve cognitive ambiguity during early decision-making, and clustering strategy was efficient to solve cognitive ambiguity during the decision-making process.

In recent years, intelligent design technology that is based on interactive evolutionary algorithms, namely interactive evolutionary design (IED) systems, has received extensive attention in the computer science, design, and other related literature. However, due to the complexity of design problems and the limitation of human cognitive ability, IED faces several challenges in actual design applications. With the aim to address these problems in the IED, this paper deconstructs the IED of the product styling from the perspective of the cognitive association of the users, and proposes a corresponding cognitive intervention method that is based on the association of information. We built databases of the perceptual evaluation results of typical cases and coded profiles of the typical cases, combined with the corresponding interaction process, to improve the efficiency of creating associations between dissimilar information in the early stages of evolution. Besides, in order to simplify the process of creating associations between similar information, this paper proposes a clustering model of similar information based on explicit and implicit distances. The proposed method is then applied to the evolutionary design of an SUV. The experimental results show that the proposed method reduces the initial and total evaluation time. Therefore, the proposed method improves users’ ability to understand the complex design tasks of IED for product styling, optimizing the interactive evaluation process by guiding designers to efficiently create the cognitive association of information, and increases the effectiveness of adopting IED to solve actual design problems about product styling.

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