A computer-implemented method comprising: receiving, by one or more configured computing systems, a set of binary rules that model operation of a specified type of physical system having multiple sensors and one or more controls and one or more outputs, wherein each sensor measures at least one type of state information related to the operation of the specified type of physical system, wherein each control is manipulatable to modify at least one output of the specified type of physical system, and wherein the binary rules specify conditions involving state information to be evaluated to reach true or false values for resulting control actions that manipulate the one or more controls; obtaining, by the one or more configured computing systems, data that describes actual operation of a target physical system of the specified type over a period of time, the data including, for each of multiple times during the period of time, information about values of the sensors at the time for the target physical system and of one or more control actions performed to manipulate the controls for the target physical system for the time and of the outputs from the target physical system resulting from the one or more control actions for the time; generating, by the one or more configured computing systems and from the obtained data, a data model representing the actual operation of the target physical system; automatically learning, by the one or more configured computing systems and from the generated data model, one or more soft rules that further model the actual operation of the target physical system, wherein each soft rule specifies one or more additional conditions to be evaluated to reach one of multiple possible values, including one or more values other than true and false and further having an associated confidence in a particular value with an associated confidence level, and wherein the learning includes: querying the data model to obtain results from the actual operation of the target physical system in specified situations in which one or more sensors of the target physical system have each of multiple values; determining differences between the results obtained from the querying of the data model and expected results of the specified situations from the set of binary rules; and constructing the one or more soft rules to represent the determined differences; and providing, by the one or more configured computing systems, information about the learned one or more soft rules, and using the learned one or more soft rules to improve further control of the target physical system. receiving, by one or more configured computing systems, a set of binary rules that model operation of a specified type of physical system having multiple sensors and one or more controls and one or more outputs, wherein each sensor measures at least one type of state information related to the operation of the specified type of physical system, wherein each control is manipulatable to modify at least one output of the specified type of physical system, and wherein the binary rules specify conditions involving state information to be evaluated to reach true or false values for resulting control actions that manipulate the one or more controls; obtaining, by the one or more configured computing systems, data that describes actual operation of a target physical system of the specified type over a period of time, the data including, for each of multiple times during the period of time, information about values of the sensors at the time for the target physical system and of one or more control actions performed to manipulate the controls for the target physical system for the time and of the outputs from the target physical system resulting from the one or more control actions for the time; generating, by the one or more configured computing systems and from the obtained data, a data model representing the actual operation of the target physical system; automatically learning, by the one or more configured computing systems and from the generated data model, one or more soft rules that further model the actual operation of the target physical system, wherein each soft rule specifies one or more additional conditions to be evaluated to reach one of multiple possible values, including one or more values other than true and false and further having an associated confidence in a particular value with an associated confidence level, and wherein the learning includes: querying the data model to obtain results from the actual operation of the target physical system in specified situations in which one or more sensors of the target physical system have each of multiple values; determining differences between the results obtained from the querying of the data model and expected results of the specified situations from the set of binary rules; and constructing the one or more soft rules to represent the determined differences; and querying the data model to obtain results from the actual operation of the target physical system in specified situations in which one or more sensors of the target physical system have each of multiple values; determining differences between the results obtained from the querying of the data model and expected results of the specified situations from the set of binary rules; and constructing the one or more soft rules to represent the determined differences; and providing, by the one or more configured computing systems, information about the learned one or more soft rules, and using the learned one or more soft rules to improve further control of the target physical system.