191 results about "Action selection" patented technology

A machine learning based electronic mail system. A classifier and action selection module analyzes the incoming message and classifies the messages with associated confidence levels, which may include analyzing the electronic message by tokenization of the text, morphological analysis of the text, and other well known processes. The classifier and action selection module then determines the appropriate action or actions to effect on the message.

In a robot device, an action selecting / control system includes a plurality of elementary action modules each of which outputs an action when selected. An activation level calculation unit calculates an activation level AL of each elementary action on the basis of information from an internal-state manager and external-stimulus recognition unit and with reference to a data base. An action selector selects an elementary action whose activation level AL is highest as an action to be implemented. Each action is associated with a predetermined internal state and external stimulus. The activation level calculation unit calculates an activation level AL of each action on the basis of a predicted satisfaction level variation based on the level of an instinct for an action corresponding to an input internal state and a predicted internal-state variation predictable based on an input external stimulus.

A system for monitoring and predicting the condition of an electronic system comprises a component model, an inference engine based on the component model, and an action selection component that selects an action based on an output of the inference engine.

In order to promote efficient learning of relationships inherent in a system or setup S described by system-state and context parameters, the next action to take, affecting the setup, is determined based on the knowledge gain expected to result from this action. Knowledge-gain is assessed “locally” by comparing the value of a knowledge-indicator parameter after the action with the value of this indicator on one or more previous occasions when the system-state / context parameter(s) and action variable(s)=had similar values to the current ones. Preferably the “level of knowledge” is assessed based on the accuracy of predictions made by a prediction module. This technique can be applied to train a prediction machine by causing it to participate in the selection of a sequence of actions. This technique can also be applied for managing development of a self-developing device or system, the self-developing device or system performing a sequence of actions selected according to the action-selection technique.

The invention discloses a medical interrogation dialogue system and a reinforcement learning method applied to the medical interrogation dialogue system, and relates to the technical field of medicalinformation. The system comprises a natural language understanding module used for classifying the intentions of users and filling slot values to form structured semantic frames; a dialogue managementmodule used for interacting with a user through a robot agent, inputting a dialogue state, performing action decision on the semantic frame through a decision network, and outputting final system action selection; a user simulator used for carrying out natural language interaction with the dialogue management module and outputting user action selection; a natural language generation module used for receiving system action selection and user action selection, enabling the user to check the selection through generating sentences similar to a human language by using a template-based method. According to the invention, the medical knowledge information between diseases and symptoms is introduced as a guide, and the inquiry historical experience is enriched through continuous interaction witha simulated patient. The reasonability of inquiry symptoms and the accuracy of disease diagnosis are improved, and the diagnosis result is higher in credibility.

The invention discloses an unmanned vehicle urban intersection left-turn decision-making method based on conflict resolution. The method comprises the steps of track prediction for straight vehicles at an intersection, decision-making process selection of a behavior decision-making module corresponding to different scenes, and vehicle control parameter selection corresponding to an action selection module. According to the invention, the decision-making framework of the left turn of the unmanned vehicle at the intersection is divided into environment assessment, behavior decision-making and action selection; prediction of intersection straight driving motion tracks is realized by using a Gaussian process regression model, decision-making processes under different left-turn scenes are formulated, and an unmanned vehicle driving action selection method considering multiple factors is provided; and the decision-making process of the left turn of the unmanned vehicle at the intersection isstructured and clarified, so that the reasonability and the adaptability of the decision-making model are improved.

A system for monitoring and predicting the condition of an electronic system comprises a component model, an inference engine based on the component model, and an action selection component that selects an action based on an output of the inference engine.

An action is performed in a spoken dialog system in response to a user's spoken utterance. A policy which maps belief states of user intent to actions is retrieved or created. A belief state is determined based on the spoken utterance, and an action is selected based on the determined belief state and the policy. The action is performed, and in one embodiment, involves requesting clarification of the spoken utterance from the user. Creating a policy may involve simulating user inputs and spoken dialog system interactions, and modifying policy parameters iteratively until a policy threshold is satisfied. In one embodiment, a belief state is determined by converting the spoken utterance into text, assigning the text to one or more dialog slots associated with nodes in a probabilistic ontology tree (POT), and determining a joint probability based on probability distribution tables in the POT and on the dialog slot assignments.

The invention discloses an intelligent vehicle speed decision-making method based on a deep reinforcement learning method. The method comprises the steps of constructing a state space S, an action space A and an instant rewarding space R of a Markov decision-making model of an intelligent vehicle passing through an intersection; initializing a neural network, and constructing an experience pool; performing action selection by adopting an epsilon-greed algorithm, and filling the experience into the experience pool constructed in the step 2; randomly selecting a part of experience from the experience pool, and training a neural network by adopting a stochastic gradient descent method; completing the speed decision of the intelligent vehicle at the current moment according to the latest neural network, adding the experience to an experience pool, randomly selecting a part of experience, and carrying out the training of a new round of neural network. The invention further discloses a simulation method of the intelligent vehicle speed decision-making method based on deep reinforcement learning. The method is advantaged in that simulation experiments are carried out based on a deep reinforcement learning simulation system established by a matlab automatic driving toolbox.

The invention discloses a wireless network distributed autonomous resource allocation method based on stateless Q learning. First, the number of channels and the transmitted power are taken as a set of actions, and a set of actions is randomly selected to calculate the actual network throughput; then the ratio of the actual network throughput and the theoretical throughput is taken as a reward after the action selection, and an action value function is updated according to the reward; and finally, the iterative adjustment of the actions can find the maximum solution of a cumulative reward value function, and the corresponding action thereof can reach the optimum performance of the wireless network. The method provided by the invention allows each node to autonomously perform channel allocation and transmitted power control to maximize the network throughput under the condition that prior information such as resource configuration of other nodes in the network is unknown.

A method for computer-supported control and / or regulation of a technical system is provided. In the method a reinforcing learning method and an artificial neuronal network are used. In a preferred embodiment, parallel feed-forward networks are connected together such that the global architecture meets an optimal criterion. The network thus approximates the observed benefits as predictor for the expected benefits. In this manner, actual observations are used in an optimal manner to determine a quality function. The quality function obtained intrinsically from the network provides the optimal action selection rule for the given control problem. The method may be applied to any technical system for regulation or control. A preferred field of application is the regulation or control of turbines, in particular a gas turbine.

The invention discloses an Enshinstein chess game algorithm based on reinforcement learning. BP neural network is applied to a value evaluation method of a chessboard and an action selection strategyof a Monte Carlo tree search algorithm. The characteristics of the chessboard are learned and the network parameters are gradually adjusted by means of the self-chess-playing learning rule of the reinforcement learning method, so that the value evaluation of the BP neural network on the chessboard and the strategy calculation of the chess playing action are gradually accurate, and the performanceof the whole game algorithm is gradually improved. According to the invention, the two BP neural networks are respectively used as a value estimation function and a behavior strategy function of the Einstein chess. The reinforcement learning algorithm serves as an evolution mechanism for adjusting BP neural network parameters, the defect that the level of an existing Enshinstein chess training setis limited by the human level is overcome, and the upper limit of the game level of the Enshinstein chess is improved.

The present invention is directed to a system and method that facilitates the more fully informed and efficient trading of items of value, including securities. According to the present invention, certain embodiments permit a customer to determine the merits of and to execute a trade from a single screen. One embodiment of the present invention provides a single option chain trading screen enabling a customer to view a matrix of all available options for a given security, including the various strike prices, expiration dates, and whether they are calls or puts. Another embodiment provides a customer with a single option chain trading screen allowing a customer to “hover” at or near various icons to obtain supplemental information without leaving the trading screen, and use a triple-action selection component to ultimately execute a trade.