10 results about "Curiosity" patented technology

The invention discloses a family-school interaction foreign language teaching system and method based on multi-modal artificial intelligence, and the system comprises the steps: constructing a student personalized interest label library, a curiosity engine library, and a dynamic memory curve and emotion curve model, and fusing the technologies of natural language processing (NLP), computer vision (CV), etc. And a closed-loop teaching system of interest-driven content generation, curiosity-guided learning, memory strategy intelligent adaptation, emotion real-time adjustment and family-school cooperative intervention is formed. Based on a multi-interest fusion generation algorithm of a deep neural network and a generative adversarial network, personalized texts are dynamically constructed, and new grammar and new words are led out through a curiosity engine; experimental data shows that the memory retention rate is increased by 47%, the home-school collaborative foreign language learning efficiency is increased by 2.3 times, the problems of disjunction of interest, low memory efficiency, missing of forgetting curves and emotion management and insufficient home-school collaboration in traditional foreign language teaching are solved, and data privacy security is guaranteed through a Federated Learning framework.

The present invention relates to the fields of artificial intelligence and operations research optimization technology, and discloses an operations research optimization algorithm development system under intelligent gaming, including: a multi-level reward shaping module, which guides multi-agents to efficiently explore and learn in a sparse reward environment; a pheromone indirect communication network module, which realizes low-overhead collaborative optimization; an agent role adaptive differentiation module, which forms a self-organization structure with coordinated complementarity; a curiosity-driven exploration processing module, which guides multi-agents to efficiently explore and develop in a complex gaming space; a Nash equilibrium and multi-agent value decomposition module, which constructs an operations research optimization framework that can coordinate the collaborative and competitive relationships of multi-agents in an intelligent gaming environment; the system has efficient exploration capabilities in a sparse reward environment through a multi-level reward shaping system and a curiosity-driven exploration process.

The invention discloses an AUV trajectory tracking deep reinforcement learning method based on an improved curiosity mechanism. On the basis of an internal curiosity mechanism, a self-adaptive internal reward coefficient mechanism is provided, and an improved internal curiosity module IICM is constructed, so that the AUV can dynamically adjust the exploration capability of the AUV according to the actual tracking effect. Meanwhile, the IICM is combined on the SAC algorithm framework, an SAC + IICM algorithm is provided, the exploration behavior of the AUV is stimulated through a self-adaptive internal reward mechanism, and the understanding depth of the AUV on the environment is improved. Besides, in order to improve the tracking effect and the training efficiency, a composite reward function fusing factors such as path errors, speed changes and yaw angle errors is designed, and a state and action space highly matched with a tracking task is constructed. The method has the advantages of being high in autonomy, good in adaptability, high in convergence speed, high in control precision, high in robustness and the like, and is suitable for an AUV autonomous operation scene in a complex marine environment.

A curiosity-driven Android app automatic testing method includes the following steps:1) a left part represents a pre-processing assembly which achieves abstraction of an Android app state based on an Android interface structure; 2) a right part is curiosity-driven reinforcement learning module which maintains a historical access state set and optimizes an exploration policy constantly to guide a test to find more new states under the guidance of a reward function based on a curiosity thought; and 3) a middle part represents an advanced guidance module of a deterministic finite automaton (DFA), where the DFA is constructed during operation to record all access states and frequencies thereof; and if the new states are not explored within a given time budget, an AndroidExplore selects the most curious state as a starting point of a next exploration according to global information of the DFA.

The utility model discloses an electromagnetic suspension scientific teaching aid structure, which belongs to the technical field of scientific teaching aids and comprises a fixing plate, a power supply is arranged on the fixing plate, a coil support bottom plate and a coil support top plate are arranged on one side of the fixing plate, and a central limiting pipe is arranged between the coil support bottom plate and the coil support top plate. A center limiting pipe is arranged on the fixing plate, a coil roller is arranged on the periphery of one end of the center limiting pipe in a matched mode, an inductance coil is wound on the outer circle of the coil roller, a magnetic ring is arranged on one side of the inductance coil and is hollow, the magnetic ring and the periphery of the center limiting pipe are in limiting sliding design, and a display screen is installed on the fixing plate in an embedded mode. The power supply is electrically connected with the display screen and the coil part. According to the electromagnetic suspension scientific teaching aid structure, electromagnetic suspension is visually embodied in an interesting mode, curiosity and exploratory property of children can be aroused, and learning interest is improved.

The present invention discloses a personalized recommendation method, system, computer device and storage medium based on curiosity. The method includes: obtaining a data set and constructing a user-item interaction matrix; inputting the user and item numbers in the user-item interaction matrix into the individual preference module and the social consensus module in the curiosity model respectively, and outputting an individual preference score and a social consensus score respectively; using the user-item interaction matrix to train the individual preference module and the social consensus module respectively, constructing a curiosity curve according to the scores output by the two modules after preliminary training, and then calculating a curiosity score and using it as a guiding weight to continue training the above two modules; using the two trained modules to predict the user-item interaction data in the real scenario to obtain a final recommendation list. Through the method provided by the present invention, the "information island" problem is effectively alleviated, and diverse recommendations based on the exploration of user curiosity are realized, which has good practical application value.

The purpose of the present invention is to provide a composition for suppressing a decline in curiosity or improving curiosity. The present invention relates to a composition for suppressing a decline in curiosity or improving curiosity, the composition containing L-ergothioneine or a salt thereof.

Provided is a method for exploration based on curiosity and prioritization of experience data in multi-agent reinforcement learning, the method including the steps of: calculating a similarity between a policy of a first agent and a policy of a second agent and computing a final reward using the similarity; and performing clustering on a replay buffer using a result of calculating the similarity between the policy of the first agent and the policy of the second agent and performing sampling on data in the cluster.

The system according to this embodiment aims to leverage children's natural curiosity and promote their active participation. [Solution] The system according to the embodiment comprises an analysis unit, a prompt creation unit, and a motivation unit. The analysis unit analyzes the child's browsing history and interests. The prompt creation unit creates personalized prompts based on the analysis results obtained by the analysis unit. The motivation unit encourages the child's behavior based on the prompts created by the prompt creation unit.

An object of the present application is to provide a composition for inhibiting a decrease in curiosity or improving curiosity. The present application relates to a composition for inhibiting a decrease in curiosity or improving curiosity, characterized by containing L-ergothioneine or a salt thereof.