Interoperable composite data units for use in distributed computing execution environments
Disclosed implementations provide executable models, such as artificial intelligence models that can be owned, traded, and used in various execution environments. By coupling a model with a strictly defined interface definition, the model can be executed in various execution environments that support the interface. Coupling the model with a non-fungible cryptographic token allows the model and other components to be owned and traded as a unit. The tradeable composite units have utility across multiple supported execution environments, such as video game environments, chat bot environments and financial trading environments. Additionally, the interface allows for the creation of pipelines and systems from multiple complementary composite units.
1. Non-transitory computer readable media for providing a character entity for an execution environment of a computing system, the computer readable media including software modules recorded thereon, the modules comprising:
a model module including an Artificial Intelligence (AI) learning model having an artificial neural network that, when executed within the execution environment, implements the character entity, wherein the character entity is one of a Player Entity or a Non-Player Character (NPC);
an interface definition module including a pointer to an interface definition associated with the Al learning model, that is used by the execution environment to communicate with the Al learning model;
an input value matrix that contains a set of values that are mapped to input variables of the Al learning model to thereby have an effect on activity of the character entity within the execution environment; and
an activity training module to be updated by a machine learning model trainer to thereby train the Al learning model based on activity of the character entity in the execution environment.
2. The non-transitory computer readable media of claim 1 , wherein the execution environment is a game environment.
3. The non-transitory computer readable media of claim 1 , wherein the interface definition specifies a finite set of inputs and outputs for the computation model.
4. The non-transitory computer readable media of claim 1 , wherein the values represent ability attributes of the character entity within the execution environment.
5. The non-transitory computer readable media of claim 4 , wherein the values are changed as a result of activity of the character entity within the environment.
6. The non-transitory computer readable media of claim 1 , further comprising a unique identifier associated with the AI learning model.
7. The non-transitory computer readable media of claim 1 , wherein the unique identifier is a Non-Fungible Token (NFT) stored on a ledger of a decentralized computing environment.
8. The non-transitory computer readable media of claim 1 , wherein the AI Learning model is network addressable.
9. The non-transitory computer readable media of claim 8 , wherein the AI learning model is content addressed.
10. The non-transitory computer readable media of claim 1 , further comprising at least one form that includes environment specific attributes of the character entity.
11. The non-transitory computer readable media of claim 1 , wherein the activity training module is in an open format built to represent machine learning models.
12. The non-transitory computer readable media of claim 1 , wherein the input value matrix comprises multiple sets of variables, each set of variables being related to a respective category.
13. A method for providing a composite data structure corresponding to a character entity that is to be implemented in an execution environment of a computing system, the composite data structure including software modules recorded on non-transient media, the method comprising:
providing a model module including an Artificial Intelligence (AI) learning model having an artificial neural network that, when executed within the execution environment, implements the character entity;
providing an interface definition module including a pointer to an interface definition associated with the Al learning model, that is used by the execution environment to communicate with the Al learning model;
providing an input value matrix that contains a set of values that are mapped to input variables of the Al learning model to thereby have an effect on activity of the character entity within the execution environment; and
providing an activity training module to be updated by a machine learning model trainer to thereby train the Al learning model based on activity of the character entity in the execution environment.
14. The method of claim 13 , wherein the execution environment is a game environment.
15. The method of claim 14 , wherein the character entity is a Non-Player Character (NPC).
16. The method of claim 14 , wherein the character entity is a player in the game environment.
17. The method of claim 13 , wherein the interface definition specifies a finite set of inputs and outputs for the computation model.
18. The method of claim 13 , wherein the values represent ability attributes of the character entity within the execution environment.
19. The method of claim 18 , wherein the values are changed as a result of activity of the character entity within the environment.
20. The method of claim 13 , wherein the method is provided as a service to the execution environment.
21. The method of claim 13 , further comprising providing a unique identifier associated with the AI learning model.
22. The method of claim 13 , wherein the unique identifier is a Non-Fungible Token (NFT) stored on a ledger of a decentralized computing environment.
23. The method of claim 13 , wherein the AI Learning model is network addressable.
24. The method of claim 23 , wherein the AI learning model is content addressed.
25. The method of claim 13 , further comprising at least one form that includes environment specific attributes of the character entity.
26. The method of claim 13 , wherein the activity training module is in an open format built to represent machine learning models.
27. The method of claim 13 , wherein the input value matrix comprises multiple sets of variables, each set of variables being related to a respective category.