Methods and systems for object-aware fuzzy processing based on analogies

1 . A method comprising:

receiving 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 3 rd 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;

analyzing, classifying, and clustering 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 zero th -order cluster), to identify each of the objects according to distinctive attributes, functions, and interrelationship;

generating, 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;

analyzing, 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 ′C B of the prospective super-object;

constructing, based on the context ′C B identified and the analogue references generated, a model of the selected cluster that includes any essential content and generating, according to that model, the root-object ′O B of the prospective super-object;

determining, based on the root-object ′O B generated and the context ′C B identified, the lower critical boundary ′L BD , the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP of the prospective super-object, wherein a lower critical boundary L BD is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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 U BD is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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 ST OP is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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, L BD ) defines a domain in which all objects are inadequate, and (U BD , 1] defines a domain in which all objects are adequate, and (L BD , U BD ) 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, L BD , U BD , and ST OP are dependent variables on the root-object O B and the context C B , such that for each (O B , C B ) pair there exists a particular critical domain bounded by inherently existing particular critical values, including a value providing the optimal similarity threshold ST OP , wherein determining the lower critical boundary ′L BD , the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP 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 (TO n ), a corresponding similarity rate represented by S TO n ~′O B /′C B , measured in the comparison process between the object and the root-object ′O B in the context ′C B , represented by TO n ~′O B /′C B , 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 ′L BD , the upper critical boundary ′U BD , or the optimal similarity threshold ′ST OP of a prospective super-object;

deciding on the similarity threshold ′ST, in accordance with the operational needs, based on the critical domain bounded by the lower critical boundary ′L BD , and the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP determined, wherein the probability of success in arbitrary encounters that satisfy the condition of association increases as ′ST approaches ′U BD , and setting ′ST larger than ′U BD guarantees success for all matches, and the coverage for the set of adequate objects increases as ′ST approaches ′L BD and reaches its maxima at ′L BD , such that setting ′ST smaller than ′L BD does not further increase the coverage, while the optimal similarity threshold ′ST OP provides the optimal efficiency for the O B −C B 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;

identifying, based on the context ′C B , root-object ′O B , and similarity threshold ′ST, the association rule of the prospective super-object as ′ST≤S TO n ~′O B /′C B ≤1, wherein TO n 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;

embedding, with the prospective super-object, the context ′C B , the root-object ′O B , and the similarity threshold ′ST, and storing the prospective super-object, including any other essential content, to generate the corresponding super-object; and setting the association rule ′ST≤S TO n ~′O B /′C B ≤1 for establishing association between the super-object and an encountered transforming object TO n ;

revisiting the environment;

analyzing, classifying, and clustering 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;

generating, for each of the identified objects (TO n ), 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;

comparing each of the identified transforming objects (TO n ) with a stored super-object by comparing (i) an analogue reference generated for TO n with (ii) an analogue reference of the root-object (O_B) in context (C_B), to determine the similarity rate S TO n ~O B /C B , and evaluating whether S TO n ~O B /C B satisfies the association rule ST≤S TO n ~O B /C B ≤1, wherein O B is the root-object, C B is the context, and ST is the similarity threshold stored with the super-object;

establishing an association with an analogous transforming object (TO n ) that fulfils the corresponding condition;

comparing the associated transforming object (TO n ) with the root-object (O B ) of the super-object to identify the difference in the context of the execution of the instruction set; and

adapting, based on the difference identified, the instruction set for the corresponding analogous transforming object (TO n ) and executing the instruction set.

2 . The method of claim 1 , further comprising controlling the similarity rate (S TO n ~O B /C B ) of the associated transforming object (TO n ) relative to the root-object O B , in the context C B , for a setpoint r(t), wherein controlling comprises:

(i) identifying the similarity rate of TO n|Cy k-1 for the k th cycle, wherein TO n is an informatical or physical object that is selected to be manipulated in order to control its similarity relative to the root-object O B in the context of C B for a setpoint r(t) while k is the index of the cycle, Cy k is the k th cycle, and TO n|Cy k-1 is the derivative of the TO n generated in the (k−1) th cycle, while k≥1 and TO n|Cy 0 =TO n ;

(ii) identifying the error e| Cy k according to the setpoint r(t) for the k th cycle;

(iii) identifying the control input u| Cy k according to the error e| Cy k for the k th cycle;

(iv) manipulating TO n|Cy k-1 in order to increase or decrease the similarity rate (S TO n ~O B /C B ) relative to O B in the context of C B according to the control input u| Cy k and generating TO n|Cy k as the k th derivative of TO n for the k th cycle; and

(v) re-executing the whole process for the (k+1) th cycle, wherein said controlling maintains the similarity rate of the associated object relative to the root-object O B in the context C B at the setpoint r(t), via manipulating the associated object, as the associated object alters.

3 . The method of claim 2 , wherein identifying the control input u| Cy k further comprises identifying, by a proportional—integral—derivative controller, a proportional output P, an integral output I, and a differential output D based on the error e| Cy k , such that u| Cy k =P+I+D, wherein P=K p e| Cy k for k≥1, I=K i ((e| Cy k −e| Cy k-1 )/2+e| Cy k-1 )Δt, k>1, e| Cy k ≥e| Cy k-k , I=K i ((e| Cy k-1 −e| Cy k )Δt, k>1, e| Cy k-1 >e| Cy k , and D=K d (e| Cy k −e| Cy k-1 )/Δt for k>1, wherein the proportional, integral, and derivative control parameters (K p , K i , K d ) are preset individually according to characteristics of the associated object and tuned during the controlling, and wherein the setpoint r(t) is a function of time.

4 . The method of claim 2 , wherein manipulating TO n|Cy k-1 further comprises performing at least one additive or subtractive modification to at least one singular elementary object and/or at least one cluster of interrelated elementary objects of TO n|Cy k-1 , and wherein manipulating further comprises estimating an effect of the at least one additive or subtractive modification on the similarity rate S TO n ~O B /C B using predictive modelling and/or a deep learning model.

5 . The method of claim 1 , further comprising:

storing data/information with respect to the association, adaptation, and execution processes of each of the corresponding super-objects; and

optimizing further the similarity threshold (ST) of each of the super-objects as corresponding data/information accumulates.

6 . The method of claim 5 , wherein optimizing further the similarity threshold of each of the super-objects further comprises adjusting the similarity threshold within a critical domain bounded by a lower critical boundary L BD and an upper critical boundary U BD corresponding to a root-object O B and a context C B of the respective super-object, and wherein optimizing further comprises updating the similarity threshold, as the stored data/information accumulates, to increase a probability of success of encountering an adequate object among objects that satisfy a condition of association while maintaining coverage for a set of adequate objects.

7 . The method of claim 1 , further comprising executing the corresponding instruction set of a super-object partially if executing the instruction set fully is not possible, or alternatively bringing the respective process to a partially executable level and staying on hold without executing the instruction set until a missing sub-object/component is identified and compensated.

8 . The method of claim 7 , further comprising:

identifying a missing sub-object/component that is the cause of a partial execution with respect to an associated super-object;

identifying a partial association rule ST′≤S TO n ~O B′ /C B′ ≤1 with respect to the missing sub-object/component;

searching in alternative environments to identify and associate with an analogous object that fulfils the condition;

re-adapting the instruction set in accordance with the analogous object that replaces the missing sub-object/component; and

executing the instruction set fully.

9 . The method of claim 8 , wherein compensating for the missing sub-object/component further comprises dynamically creating an object possessing properties of the missing sub-object/component and/or creating a composite object from a plurality of objects collected from a plurality of environments, and further modifying the created object or the composite object according to execution needs, and wherein, if executing the instruction set fully remains not possible, the method further comprises re-executing the identifying, searching, associating, and re-adapting steps until executing the instruction set fully becomes possible.

10 . The method of claim 7 , wherein executing the instruction set partially further comprises identifying sub-processes associated with sub-clusters of the selected cluster, determining a priority order of the sub-processes and/or assigning weight coefficients to elementary objects or sub-clusters according to essentiality, and partially executing the instruction set according to the priority order and/or the weight coefficients.

11 . The method of claim 1 , wherein prior to evaluating whether each identified transforming object (TO n ) satisfies the association rule ST≤S TO n ~O B /C B ≤1, the method further comprises preliminarily identifying one or more objects that are irrelevant enough to be excluded from the association-rule evaluation.

12 . The method of claim 1 , wherein selecting a subset from the set of all singular elementary objects and/or all clusters of interrelated elementary objects existing in the respective universe further comprising inter-manipulating the singular elementary objects and/or clusters of interrelated elementary objects belonging to the selected subset, and identifying each possible object in the subset based on all possible outcomes of permutations and/or combinations of interactions between the existing singular elementary objects and/or clusters of interrelated elementary objects of the subset, wherein the respective universe is one in which any interaction is precisely observable and perceivable, and wherein, when the selected subset is finite, the all possible outcomes comprise a finite set of outcomes, and wherein inter-manipulating comprises manipulating the objects of the subset with respect to each other through permutations and/or combinations of interactions that occur between the objects of the subset, such that each possible object is represented by an outcome of at least one of said permutations and/or combinations of interactions.

13 . A system comprising:

one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system 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 3 rd 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 ′C B of the prospective super-object;

construct, based on the context ′C B 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 ′O B of the prospective super-object;

determine, based on the root-object ′O B generated and the context ′C B identified, the lower critical boundary ′L BD , the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP of the prospective super-object, wherein a lower critical boundary L BD is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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 U BD is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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 ST OP is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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, L BD ) defines a domain in which all objects are inadequate, and (U BD , 1] defines a domain in which all objects are adequate, and (L BD , U BD ) 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, L BD , U BD , and ST OP are dependent variables on the root-object O B and the context C B , such that for each (O B , C B ) pair there exists a particular critical domain bounded by inherently existing particular critical values, including a value providing the optimal similarity threshold ST OP , wherein determining the lower critical boundary ′L BD , the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP 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 (TO n ), a corresponding similarity rate represented by S TO n ~′O B /′C B , measured in the comparison process between the object and the root-object ′O B in the context ′C B , represented by TO n ~′O B /′C B , 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 ′L BD , the upper critical boundary ′U BD , or the optimal similarity threshold ′ST OP 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 ′L BD , and the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP determined, wherein the probability of success in arbitrary encounters that satisfy the condition of association increases as ′ST approaches ′U BD , and setting ′ST larger than ′U BD guarantees success for all matches, and the coverage for the set of adequate objects increases as ′ST approaches ′L BD and reaches its maxima at ′L BD , such that setting ′ST smaller than ′L BD does not further increase the coverage, while the optimal similarity threshold ′ST OP provides the optimal efficiency for the O B −C B 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 ′C B , root-object ′O B , and similarity threshold ′ST, the association rule of the prospective super-object as ′ST≤S TO n ~′O B /′C B ≤1, wherein TO n 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 ′C B , the root-object ′O B , 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≤S TO n ~′O B /′C B ≤1 for establishing association between the super-object and an encountered transforming object TO n ;

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 (TO n ), 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 (TO n ) with a stored super-object by comparing (i) an analogue reference generated for TO n with (ii) an analogue reference of the root-object (O_B) in context (C_B), to determine the similarity rate S TO n ~O B /C B , and evaluating whether S TO n ~O B /C B satisfies the association rule ST≤S TO n ~O B /C B ≤1, wherein O B is the root-object, C B is the context, and ST is the similarity threshold stored with the super-object;

establish an association with an analogous transforming object (TO n ) that fulfils the corresponding condition;

compare the associated transforming object (TO n ) with the root-object (O B ) 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 (TO n ) and execute the instruction set.

14 . The system of claim 13 , wherein the instructions further cause the system to control the similarity rate (S TO n ~O B /C B ) of the associated transforming object (TO n ) relative to the root-object O B , in the context C B , for a setpoint r(t), wherein controlling comprises:

(i) identifying the similarity rate of TO n|Cy k-1 for the k th cycle, wherein TO n is an informatical or physical object that is selected to be manipulated in order to control its similarity relative to the root-object O B in the context of C B for a setpoint r(t) while k is the index of the cycle, Cy k is the k th cycle, and TO n|Cy k-1 is the derivative of the TO n generated in the (k−1) th cycle, while k≥1 and TO n|Cy 0 =TO n ;

(ii) identifying the error e| Cy k according to the setpoint r(t) for the k th cycle;

(iii) identifying the control input u| Cy k according to the error e| Cy k for the k th cycle;

(iv) manipulating TO n|Cy k-1 in order to increase or decrease the similarity rate (S TO n ~O B /C B ) relative to O B in the context of C B according to the control input u| Cy k and generating TO n|Cy k as the k th derivative of TO n for the k th cycle; and

(v) re-executing the whole process for the (k+1) th cycle, wherein said controlling maintains the similarity rate of the associated object relative to the root-object O B in the context C B at the setpoint r(t), via manipulating the associated object, as the associated object alters.

15 . The system of claim 14 , wherein the instructions further cause the system to identify the control input u| Cy k , wherein identifying the control input u| Cy k further comprises identifying, by a proportional-integral-derivative controller, a proportional output P, an integral output I, and a differential output D based on the error e| Cy k , such that u| Cy k =P+I+D, wherein P=K p e| Cy k for k≥1, I=K i ((e| Cy k −e| Cy k-1 )/2+e| Cy k-1 )Δt, k>1, e| Cy k ≥e| Cy k-1 ; I=K i ((e| Cy k-1 −e| Cy k )/2+e| Cy k )Δt, k>1, e| Cy k-1 >e| Cy k , and D=K d (e| Cy k −e| Cy k-1 )/Δt for k>1, wherein the proportional, integral, and derivative control parameters (K p , K i , K d ) are preset individually according to characteristics of the associated object and tuned during the controlling, and wherein the setpoint r(t) is a function of time.

16 . The system of claim 14 , wherein the instructions further cause the system to manipulate TO n|Cy k-1 , wherein manipulating TO n|Cy k-1 further comprises performing at least one additive or subtractive modification to at least one singular elementary object and/or at least one cluster of interrelated elementary objects of TO n|Cy k-1 , and wherein manipulating further comprises estimating an effect of the at least one additive or subtractive modification on the similarity rate S TO n ~O B /C B using predictive modelling and/or a deep learning model.

17 . The system of claim 13 , wherein the instructions further cause the system to:

store data/information with respect to the association, adaptation, and execution processes of each of the corresponding super-objects; and

optimize further the similarity threshold (ST) of each of the super-objects as corresponding data/information accumulates.

18 . The system of claim 17 , wherein the instructions further cause the system to optimize further the similarity threshold of each of the super-objects, wherein optimizing further comprises adjusting the similarity threshold within a critical domain bounded by a lower critical boundary L BD and an upper critical boundary U BD corresponding to a root-object O B and a context C B of the respective super-object, and wherein optimizing further comprises updating the similarity threshold, as the stored data/information accumulates, to increase a probability of success of encountering an adequate object among objects that satisfy a condition of association while maintaining coverage for a set of adequate objects.

19 . The system of claim 13 , wherein the instructions further cause the system to execute the corresponding instruction set of a super-object partially if executing the instruction set fully is not possible, or alternatively bringing the respective process to a partially executable level and staying on hold without executing the instruction set until a missing sub-object/component is identified and compensated.

20 . The system of claim 19 , wherein the instructions further cause the system to:

identify a missing sub-object/component that is the cause of a partial execution with respect to an associated super-object;

identify a partial association rule ST′≤S TO n ~O B′ /C B′ ≤1 with respect to the missing sub-object/component;

search in alternative environments to identify and associate with an analogous object that fulfils the condition;

re-adapt the instruction set in accordance with the analogous object that replaces the missing sub-object/component; and

execute the instruction set fully.

21 . The system of claim 20 , wherein the instructions further cause the system to compensate for the missing sub-object/component, wherein compensating for the missing sub-object/component further comprises dynamically creating an object possessing properties of the missing sub-object/component and/or creating a composite object from a plurality of objects collected from a plurality of environments, and further modifying the created object or the composite object according to execution needs, and wherein, if executing the instruction set fully remains not possible, the instructions further cause the system to re-execute the identifying, searching, associating, and re-adapting steps until executing the instruction set fully becomes possible.

22 . 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 ′C B 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 ′O B of the prospective super-object;

determine, based on the root-object ′O B generated and the context ′C B identified, the lower critical boundary ′L BD , the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP of the prospective super-object, wherein a lower critical boundary L BD is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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 U BD is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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 ST OP is defined as a similarity rate, measured in the context C B with respect to the root-object O B , 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, L BD ) defines a domain in which all objects are inadequate, and (U BD , 1] defines a domain in which all objects are adequate, and (L BD , U BD ) 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, L BD , U BD , and ST OP are dependent variables on the root-object O B and the context C B , such that for each (O B , C B ) pair there exists a particular critical domain bounded by inherently existing particular critical values, including a value providing the optimal similarity threshold ST OP , wherein determining the lower critical boundary ′L BD , the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP 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 (TO n ), a corresponding similarity rate represented by S TO n ~′O B /′C B , measured in the comparison process between the object and the root-object ′O B in the context ′C B , represented by TO n ~′O B /′C B , 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 ′L BD , the upper critical boundary ′U BD , or the optimal similarity threshold ′ST OP 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 ′L BD , and the upper critical boundary ′U BD , and the optimal similarity threshold ′ST OP determined, wherein the probability of success in arbitrary encounters that satisfy the condition of association increases as ′ST approaches ′U BD , and setting ′ST larger than ′U BD guarantees success for all matches, and the coverage for the set of adequate objects increases as ′ST approaches ′L BD and reaches its maxima at ′L BD , such that setting ′ST smaller than ′L BD does not further increase the coverage, while the optimal similarity threshold ′ST OP provides the optimal efficiency for the O B −C B 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 ′C B , root-object ′O B , and similarity threshold ′ST, the association rule of the prospective super-object as ′ST≤S TO n ~′O B /′C B ≤1, wherein TO n 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 ′C B , the root-object ′O B , 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≤S TO n ~′O B /′C B ≤1 for establishing association between the super-object and an encountered transforming object TO n ;

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 (TO n ), 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 (TO n ) with a stored super-object by comparing (i) an analogue reference generated for TO n with (ii) an analogue reference of the root-object (O_B) in context (C_B), to determine the similarity rate S TO n ~O B /C B , and evaluating whether S TO n ~O B /C B satisfies the association rule ST≤S TO n ~O B /C B ≤1, wherein O B is the root-object, C B is the context, and ST is the similarity threshold stored with the super-object;

establish an association with an analogous transforming object (TO n ) that fulfils the corresponding condition;

compare the associated transforming object (TO n ) with the root-object (O B ) 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 (TO n ) and execute the instruction set.

23 . The non-transitory computer readable medium of claim 22 , wherein the computer readable medium further comprises instructions executable with the processor to control the similarity rate (S TO n ~O B /C B ) of the associated transforming object (TO n ) relative to the root-object O B , in the context C B , for a setpoint r(t), wherein controlling comprises:

(i) identifying the similarity rate of TO n|Cy k-1 for the k th cycle, wherein TO n is an informatical or physical object that is selected to be manipulated in order to control its similarity relative to the root-object O B in the context of C B for a setpoint r(t) while k is the index of the cycle, Cy k is the k th cycle, and TO n|Cy k-1 is the derivative of the TO n generated in the (k−1) th cycle, while k≥1 and TO n|Cy 0 =TO n ;

(ii) identifying the error e| Cy k according to the setpoint r(t) for the k th cycle;

(iii) identifying the control input u| Cy k according to the error e| Cy k for the k th cycle;

(iv) manipulating TO n|Cy k-1 in order to increase or decrease the similarity rate (S TO n ~O B /C B ) relative to O B in the context of C B according to the control input u| Cy k and generating TO n|Cy k as the k th derivative of TO n for the k th cycle; and

(v) re-executing the whole process for the (k+1) th cycle, wherein said controlling maintains the similarity rate of the associated object relative to the root-object O B in the context C B at the setpoint r(t), via manipulating the associated object, as the associated object alters.

24 . The non-transitory computer readable medium of claim 23 , wherein the computer readable medium further comprises instructions executable with the processor to identify the control input u| Cy k wherein identifying the control input u| Cy k further comprises identifying, by a proportional—integral—derivative controller, a proportional output P, an integral output I, and a differential output D based on the error e| Cy k , such that u| Cy k =P+I+D, wherein P=K p e| Cy k for k≥1, I=K i ((e| Cy k −e| Cy k-1 )/2+e| Cy k-1 )Δt, k>1, e| Cy k-1 , I=K i (e| Cy k-1 −e| Cy k )/2+e| Cy k )Δt, k>1, e| Cy k-1 >e| Cy k ; and D=K d (e| Cy k −e| Cy k-1 )/Δt for k>1, wherein the proportional, integral, and derivative control parameters (K p , K i , K d ) are preset individually according to characteristics of the associated object and tuned during the controlling, and wherein the setpoint r(t) is a function of time.

25 . The non-transitory computer readable medium of claim 23 , wherein the computer readable medium further comprises instructions executable with the processor to manipulate TO n|Cy k-1 wherein manipulating TO n|Cy k-1 further comprises performing at least one additive or subtractive modification to at least one singular elementary object and/or at least one cluster of interrelated elementary objects of TO n|Cy k-1 , and wherein manipulating further comprises estimating an effect of the at least one additive or subtractive modification on the similarity rate S TO n ~O B /C B using predictive modelling and/or a deep learning model.

26 . The non-transitory computer readable medium of claim 22 , wherein the computer readable medium further comprises instructions executable with the processor to:

store data/information with respect to the association, adaptation, and execution processes of each of the corresponding super-objects; and

optimize further the similarity threshold (ST) of each of the super-objects as corresponding data/information accumulates.

27 . The non-transitory computer readable medium of claim 26 , wherein the computer readable medium further comprises instructions executable with the processor to optimize further the similarity threshold of each of the super-objects, wherein optimizing further comprises adjusting the similarity threshold within a critical domain bounded by a lower critical boundary L BD and an upper critical boundary U BD corresponding to a root-object O B and a context C B of the respective super-object, and wherein optimizing further comprises updating the similarity threshold, as the stored data/information accumulates, to increase a probability of success of encountering an adequate object among objects that satisfy a condition of association while maintaining coverage for a set of adequate objects.

28 . The non-transitory computer readable medium of claim 22 , wherein the computer readable medium further comprises instructions executable with the processor to execute the corresponding instruction set of a super-object partially if executing the instruction set fully is not possible, or alternatively bringing the respective process to a partially executable level and staying on hold without executing the instruction set until a missing sub-object/component is identified and compensated.

29 . The non-transitory computer readable medium of claim 28 , wherein the computer readable medium further comprises instructions executable with the processor to:

identify a missing sub-object/component that is the cause of a partial execution with respect to an associated super-object;

identify a partial association rule ST′≤S TO n ~O B′ /C B′ ≤1 with respect to the missing sub-object/component;

search in alternative environments to identify and associate with an analogous object that fulfils the condition;

re-adapt the instruction set in accordance with the analogous object that replaces the missing sub-object/component; and

execute the instruction set fully.

30 . The non-transitory computer readable medium of claim 29 , wherein the computer readable medium further comprises instructions executable with the processor to compensate for the missing sub-object/component, wherein compensating for the missing sub-object/component further comprises dynamically creating an object possessing properties of the missing sub-object/component and/or creating a composite object from a plurality of objects collected from a plurality of environments, and further modifying the created object or the composite object according to execution needs, and wherein, if executing the instruction set fully remains not possible, the computer readable medium further comprises instructions executable with the processor to re-execute the identifying, searching, associating, and re-adapting steps until executing the instruction set fully becomes possible.