Abstract: A machine learning system can include a symbolic memory network and a secondary memory network. The machine learning system can retrieve a trace from a secondary memory network and reprocess the trace. The machine learning system can generate an analog by reprocessing the trace using at least one of input data obtained from an environment, or at least one analog obtained from the symbolic memory network. The machine learning system can determine that the generated analog satisfies a storage condition for the symbolic memory network. The machine learning system can provide instructions to take an action in the environment based on a subsequent retrieval of the generated analog from the symbolic memory network.

Abstract: A machine learning system can include a symbolic representation generator and an inference component. The symbolic representation can generate a symbolic memory network using input obtained from an environment. The inference component can generate a causal model by experimenting upon components, operations, and parameters of the symbolic representation generator. The causal model can influence the generation of the symbolic memory network. In particular, the machine learning system can identify an analog in the symbolic memory network as being entangled, identify a disentangled version of the identified entangled analog in the symbolic memory network, and, in response to the identification of the disentangled version of the identified entangled analog, release the identified entangled analog from the symbolic memory network and provide instructions to take an action in an environment based on the disentangled version of the identified entangled analog.

Abstract: A symbolic machine learning system can include a symbolic representation generator, an inference component, and an interface component. The symbolic representation generator can repeatedly receive inputs associated with observed objects and computes a symbolic memory network representative of associations involving the observed objects. The inference component can repeatedly perturb components, operations, or parameters of the symbolic representation generator and computes a causal model of effects of the perturbations. The interface component can output signals generated based on the causal model and concerning the associations involving the observed objects.

Abstract: A machine learning system including a symbolic representation generator can generate a first relationship analog through non-interactive learning. The symbolic representation generator can generate a second relationship analog through interactive experimentation using the first relationship analog. The interactive experimentation can include accumulating analogs during an experimental unit and specifying a goal. The interactive experimentation can include retrieving the first relationship analog based on the specified goal and the accumulated analogs. The interactive experimentation can include generating the second relationship analog using at least one of relational learning or comparison-based refinement, where the generation can use the first relationship analog and the accumulated analogs.

Abstract: A machine learning system can determine symbolic representation generator parameters. The machine learning system can select and configure an experimental design. The machine learning system can assign symbolic representation experimental conditions (SRECs) to a policy according to the experimental design. During an experimental unit, the machine learning system can match SRECs assigned to the policy to analogs in a symbolic representation generated by the symbolic representation generator using input obtained from an environment according to the symbolic representation generator parameters. The machine learning system can provide instructions to take actions in the environment based on the matched SRECs and to update the symbolic representation generator parameters based on a result of the experimental unit.

Abstract: A method includes calculating model probabilities and a ranked list of locations and receiving a selection of a plurality of the ranked locations. The method also includes receiving actual item count data from at least a subset of the selected locations and comparing the actual item count data to a corresponding inventory record. The method further includes outputting a discrepancy indication based upon a discrepancy between the actual item count data and the corresponding inventory record.

Abstract: A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.

Abstract: A method includes detecting, via object detection hardware, a portion of at least one of a plurality of objects, receiving object attributes for the at least one of a plurality of objects, providing at least one contact area, based upon the object attributes, on each of a plurality of object representations corresponding to each of the at least one of a plurality of objects, providing a surface representation, displaying, via display hardware, the plurality of object representations each residing upon the surface representation, and displaying at least one support area on the surface representation corresponding to the at least one contact area associated with the plurality of object representations.

Abstract: An example method includes selecting treatment assignments for operation of a system in an environment based on a causal model mapped to a probability distribution over possible treatment assignments, where the causal model measures causal relationships between the treatment assignments and a performance metric associated with the environment. The method further includes mapping causal effects between the treatment assignments and measures of uncertainty associated with the environment in the causal model to corresponding probabilities using probability matching. The method further includes determining the performance metric using the treatment assignments and corresponding dependent variables obtained from the environment in response to the treatment assignments. The method further includes adjusting, based on the performance metric, the causal model, and re-computing the adjusted causal model by computing overall causal effects based on the measures of uncertainty around the overall causal effects.

Abstract: A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a process of polishing semiconductor wafers. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of input settings for polishing a semiconductor wafer, based on a causal model that measures current causal relationships between input settings and a quality of semiconductor wafers; ii) receiving a measure of the quality of the semiconductor wafer polished with the configuration of input settings; and iii) adjusting, based on the measure of the quality of the semiconductor wafer polished with the configuration of input settings, the causal model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting control settings for the environment based on (i) a causal model that identifies causal relationships between possible settings for controllable elements in the environment and environment responses that reflect a performance of the control system in controlling the environment and (ii) current values of a set of internal parameters; and during the repeatedly selecting: monitoring environment responses to the selected control settings; determining, based on the environment responses, an indication that one or more properties of the environment have changed; and in response, modifying the current values of one or more of the internal parameters.

Abstract: A method of performing a process using a plurality of control signals and resulting in a plurality of measurable outcomes is described. The method includes optimizing the plurality of control signals by at least: receiving a plurality of process constraints; receiving, for each measurable outcome, an optimum range; receiving, for each control signal, a plurality of potential optimum values; iteratively performing the process, where for each process iteration, the value of each control signal is selected from among the plurality of potential optimum values received for the control signal; for each process iteration, measuring each outcome in the plurality of measurable outcomes; and generating confidence intervals for the control signals to determine a causal relationship between the control signals and the measurable outcomes. The method includes performing the process using at least the control signals determined by the causal relationship to causally affect at least one of the measurable outcomes.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments.

Abstract: A method of performing a process using a plurality of control signals and resulting in a plurality of measurable outcomes is described. The method includes optimizing the plurality of control signals by at least: receiving a plurality of process constraints; receiving, for each measurable outcome, an optimum range; receiving, for each control signal, a plurality of potential optimum values; iteratively performing the process, where for each process iteration, the value of each control signal is selected from among the plurality of potential optimum values received for the control signal; for each process iteration, measuring each outcome in the plurality of measurable outcomes; and generating confidence intervals for the control signals to determine a causal relationship between the control signals and the measurable outcomes. The method includes performing the process using at least the control signals determined by the causal relationship to causally affect at least one of the measurable outcomes.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a process of polishing semiconductor wafers. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of input settings for polishing a semiconductor wafer, based on a causal model that measures current causal relationships between input settings and a quality of semiconductor wafers; ii) receiving a measure of the quality of the semiconductor wafer polished with the configuration of input settings; and iii) adjusting, based on the measure of the quality of the semiconductor wafer polished with the configuration of input settings, the causal model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a manufacturing process. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of control settings for a manufacturing process, based on a causal model that measures causal relationships between control settings and a measure of a success of the manufacturing process; ii) determining the measure of the success of the manufacturing process using the configuration of control settings; and iii) adjusting, based on the measure of the success of the manufacturing process using the configuration of control settings, the causal model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes identifying a procedural instance; determining a temporal extent for the procedural instance based on temporal extent parameters for the one or more entities in the procedural instance; selecting control settings for the procedural instance; monitoring environment responses to the control settings that are received for the one or more entities; determining which of the environment responses to attribute to the procedural instance in a causal model; and adjusting, based at least in part on the environment responses that are attributed to the procedural instance, the temporal extent parameters for the one or more entities.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting, by a control system for the environment, control settings for the environment based on internal parameters of the control system, wherein: at least some of the control settings for the environment are selected based on a causal model, and the internal parameters include a first set of internal parameters that define a number of previously received performance metric values that are used to generate the causal model for a particular controllable element; obtaining, for each selected control setting, a performance metric value; determining that generating the causal model for the at particular controllable element would result in higher system performance; and adjusting, based on the determining, the first set of internal parameters.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting control settings for the environment based on (i) a causal model that identifies causal relationships between possible settings for controllable elements in the environment and environment responses that reflect a performance of the control system in controlling the environment and (ii) current values of a set of internal parameters; and during the repeatedly selecting: monitoring environment responses to the selected control settings; determining, based on the environment responses, an indication that one or more properties of the environment have changed; and in response, modifying the current values of one or more of the internal parameters.