A non-transitory computer readable medium storing instructions executable by a processor, the computer readable medium comprising:
- instructions executable with the processor to:
- receive a selection by a user that comprises a cluster of various objects that are selected purposefully or randomly among a plurality of informatical and/or physical objects in an environment including an instruction set developed by the user or a 3rd party user with respect to the selected cluster in order to process the cluster, wherein each of said objects is either a singular elementary object or a cluster of interrelated elementary objects presented on or connected to a data network as a resource of said data network;
- analyze, classify, and cluster singular elementary objects of the selected cluster to identify sub-clusters of interrelated elementary objects in rank order (including each singular elementary object as a zeroth-order cluster), to identify each of the objects according to distinctive attributes, functions, and interrelationship;
- generate, for each of the identified objects, an analogue reference, each of the analogue references uniquely identifying a corresponding one of the identified objects according to the extracted attributes, functions, and interrelationship, wherein generating the analogue reference comprises generating an analogue reference for each identified sub-cluster identified in rank order;
- analyze, based on the analogue references generated, the instruction set with respect to the selected cluster to extract the context of the process, thereby conceptualizing the process, and identifying the context ′CB of the prospective super-object;
- construct, based on the context ′Cp identified and the analogue references generated, a model of the selected cluster that includes any essential content and generate, according to that model, the root-object ′OB of the prospective super-object;
- determine, based on the root-object ′OB generated and the context ′CB identified, the lower critical boundary ′LBD, the upper critical boundary ′UBD, and the optimal similarity threshold ′STOP of the prospective super-object, wherein a lower critical boundary LBD is defined as a similarity rate, measured in the context CB with respect to the root-object OB, of the least similar object that is adequate for the realization of the respective process among all adequate objects in the universe to which the object belongs, and an upper critical boundary UBD is defined as a similarity rate, measured in the context CB with respect to the root-object OB, of the most similar object that is inadequate for the realization of the respective process among all inadequate objects in the universe to which the object belongs, and an optimal similarity threshold STOP is defined as a similarity rate, measured in the context CB with respect to the root-object OB, that provides (i) the highest probability of success of encountering an adequate object among all objects that satisfy the condition of association, while also providing (ii) the widest coverage for the set of adequate objects, wherein [0, LBD) defines a domain in which all objects are inadequate, and (UBD, 1] defines a domain in which all objects are adequate, and (LBD, UBD) defines a critical domain in which an encountered object is either adequate or inadequate for realization of the respective process, wherein, within the respective universe, LBD, UBD, and STOP are dependent variables on the root-object OB and the context CB, such that for each (OB, CB) pair there exists a particular critical domain bounded by inherently existing particular critical values, including a value providing the optimal similarity threshold STOP, wherein determining the lower critical boundary ′LBD, the upper critical boundary ′UBD, and the optimal similarity threshold ′STOP further comprising:
- (i) selecting a subset from the set of all singular elementary objects and/or all clusters of interrelated elementary objects existing in the respective universe;
- (ii) determining, for each of a plurality of objects (TOn), a corresponding similarity rate represented by STOn~′OB/′CB, measured in the comparison process between the object and the root-object ′OB in the context ′CB, represented by TOn~′OB/′CB, and an adequacy or inadequacy determination indicative of whether the instruction set successfully realizes the respective process;
- (iii) training at least one machine learning model using the similarity rates and the adequacy or inadequacy determinations;
- and
- (iv) using said machine learning model to determine at least one of:
- the lower critical boundary ′LBD, the upper critical boundary ′UBD, or the optimal similarity threshold ′STOP of a prospective super-object;
- decide on the similarity threshold ′ST, in accordance with the operational needs, based on the critical domain bounded by the lower critical boundary ′LBD, and the upper critical boundary ′UBD, and the optimal similarity threshold ′STOP determined, wherein the probability of success in arbitrary encounters that satisfy the condition of association increases as ′ST approaches ′UBD, and setting ′ST larger than ′UBD guarantees success for all matches, and the coverage for the set of adequate objects increases as ′ST approaches ′LBD and reaches its maxima at ′LBD, such that setting ′ST smaller than ′LBD does not further increase the coverage, while the optimal similarity threshold ′STOP provides the optimal efficiency for the OB−CB pair by providing (i) the highest probability of success to encounter an adequate object among all objects that satisfy the condition of association and (ii) the widest coverage for the set of adequate objects;
- identify, based on the context ′CB, root-object ′OB, and similarity threshold ′ST, the association rule of the prospective super-object as ′ST≤STOn~′OB/′CB≤1, wherein TOn is a transforming object, postulated to transform unpredictably between manifestations, appearances, or observations, thereby representing dynamic uncertainty and vagueness induced by such transformations, and wherein the index n denotes an nth individual object identified among encountered objects in the environment;
- embed, with the prospective super-object, the context ′CB, the root-object ′OB, and the similarity threshold ′ST, and store the prospective super-object, including any other essential content, to generate the corresponding super-object;
- and set the association rule ′ST≤STOn~′OB/′CB≤1 for establishing association between the super-object and an encountered transforming object TOn;
- revisit the environment;
- analyze, classify, and cluster informatical and/or physical elementary objects that exist in an environment to identify each of the finitely many objects according to distinctive attributes, functions, and interrelationship, wherein each of said objects is either a singular elementary object or a cluster of interrelated elementary objects presented on or connected to a data network as a resource of said data network;
- generate, for each of the identified objects (TOn), an analogue reference, each of the analogue references uniquely identifying a corresponding one of the identified objects according to the extracted attributes, functions, and interrelationship, wherein each of said identified objects is treated as a transforming-object, even if the identified object is not a transforming-object;
- compare each of the identified transforming objects (TOn) with a stored super-object by comparing (i) an analogue reference generated for TOn with (ii) an analogue reference of the root-object (O_B) in context (C_B), to determine the similarity rate STOn~OB/CB, and evaluating whether STOn~OB/CB satisfies the association rule ST≤STOn~OB/CB≤1, wherein OB is the root-object, CB is the context, and ST is the similarity threshold stored with the super-object;
- establish an association with an analogous transforming object (TOn) that fulfils the corresponding condition;
- compare the associated transforming object (TOn) with the root-object (OB) of the super-object to identify the difference in the context of the execution of the instruction set;
- and
- adapt, based on the difference identified, the instruction set for the corresponding analogous transforming object (TOn) and execute the instruction set.