Systems for creating a reverse-hypothesized network, said system comprising: data inputter for inputting input data, said data residing on nodes; context determination mechanism for identifying a context for each node; node identifier for identifying a node of disagreement, per nodes' set; stimulus inputter for adding stimulus data to said formed nodes' set to identify changes in nodes' parameters and network linkages in order to differentiate said forward hypothesis nodes and corresponding forward hypothesized nodes' set from said reverse hypothesis nodes and corresponding reverse hypothesized nodes' set, thereby providing inputs for obtaining an uncertainty index; uncertainty determination mechanism; freedom index determination mechanism; creativity index determination mechanism; and output mechanism for providing an output which is a vectored reverse-hypothesized node, output being a function of creativity index, creativity index being a function of uncertainty index and freedom index.

Systems and methods are provided for self-learning natural language predictive searching including receiving a first input, the first input being related to the desired outcome; retrieving a first information related to the first input; determining a first output based on at least the first input and the first information; outputting the first output; receiving a second input based on the outputted first output in response to the first output being different from the desired outcome, the second input being related to the desired outcome; retrieving, by the processor, a second information related to the second input; determining a second output based on at least the second input, the second information, the first input and the first information; and outputting the second output.

A system is disclosed for anticipating and rendering unnecessary human action in a smart home or other connected environment. Sensor data and data from smart appliances may be used to determine and predict human user behavior to a degree that allows an automated system to act upon the appliances or other devices even before the human user is able to act, allowing the human user's past actions to program the automated system without conscious effort by the human user to define the conditions under which an action should be taken.

A method for building behavior scheme for an agent is provided. The method includes: receiving data relating to at least one agent, the data including at least one environmental parameter of the at least one agent and a plurality of actions which can be performed by the at least one agent; defining at least one desire to be achieved by the at least one agent; and constructing at least one behavior scheme configured to achieve the desire, the behavior scheme includes at least one action. The execution of the action depends on the environmental parameter.

A system is disclosed for anticipating and rendering unnecessary human action in a smart home or other connected environment. Sensor data and data from smart appliances may be used to determine and predict human user behavior to a degree that allows an automated system to act upon the appliances or other devices even before the human user is able to act, allowing the human user's past actions to program the automated system without conscious effort by the human user to define the conditions under which an action should be taken.

A terminal positioning method and a network device, where the network device obtains radio signal sampling information of a first terminal at a current moment. The first terminal is any terminal in a target region, and the target region is a preset geographic region. The network device obtains position information of the first terminal at the current moment by prediction based on the radio signal sampling information of the first terminal at the current moment and a predictive model of the target region. The predictive model is obtained by extensive data training in the target region, has relatively strong error tolerance and error-correction capabilities, and can accurately reflect a relationship between radio signal sampling information and position information of a terminal. Terminal positioning accuracy is effectively improved.

A system for event prediction using schema networks includes a first antecedent entity state that represents a first entity at a first time; a first consequent entity state that represents the first entity at a second time; a second antecedent entity state that represents a second entity at the first time; and a first schema factor that couples the first and second antecedent entity states to the first consequent entity state; wherein the first schema factor is configured to predict the first consequent entity state from the first and second antecedent entity states.

An Automatic Dependent Surveillance Broadcast (ADS-B) system may include a plurality of ADS-B terrestrial stations, with each ADS-B terrestrial station comprising an antenna and wireless circuitry associated therewith defining a station gain pattern. The system may further include a controller including a variational autoencoder (VAE) configured to compress station pattern data from the plurality of ADS-B terrestrial stations, create a normal distribution of the compressed data in a latent space of the VAE, and decompress the compressed station pattern data from the latent space. The controller may also include a processor coupled to the VAE and configured to process the decompressed station pattern data using a probabilistic model selected from among different probabilistic models based upon a game theoretic reward matrix, determine an anomaly from the processed decompressed station pattern data, and generate an alert (e.g., a station specific alert) based upon the determined anomaly.

An apparatus, a program-stored storage medium and a method with an inference engine can execute inference using a minimum ruleset in various applications. The apparatus includes: a machine learning engine being a classifying-type engine configured to include adapted-to-category learning models each generated by using each adapted-to-category set of teacher data, the adapted-to-category set being obtained by classifying teacher data for each category, and to use the learning models to output a category data corresponding to the inputted object data; a ruleset selector configured to select, from rulesets each prepared for each category and stored in a rule base, a ruleset corresponding to the category data outputted from the machine learning engine; and a rule engine configured to execute inference to the inputted object data by using the ruleset selected by the ruleset selector, and to output the inference result.

Methods and systems for transcribing a media file using reinforcement learning are provided. In one aspect, the method includes: identifying a low confidence of accuracy portion from a transcription result of the media file; constructing a phoneme sequence that includes an audio segment corresponding to the identified low confidence of accuracy portion, based on at least on a reward function; creating a new audio waveform based at least on the constructed phoneme sequence; and generating a new transcription using a transcription engine based on the new audio waveform.