Systems and methods for forward market purchase of machine resources

1. A transaction-enabling system, comprising:

a fleet of machines, each one of the fleet of machines having a resource requirement comprising a plurality of machine-related resources the plurality of machine-related resources comprising at least two of: a compute resource, a spectrum resource, or a network bandwidth resource, the resource requirement including a first resource among the plurality of machine-related resources; and

a controller, comprising:

an intelligent agent circuit structured to aggregate data corresponding to the plurality of machine-related resources from a plurality of data sources comprising at least an external data source, the external data source comprising at least a behavioral data source;

an expert system circuit structured to configure a purchase of at least one of the plurality of machine-related resources, the configuring the purchase comprising:

maintaining a training data set comprising feedback data indicating outcomes of previous purchases and at least one of: facility parameters, yield, profitability, optimization of resources, satisfaction of users, or satisfaction of operators;

training an artificial intelligence system based on the training data set, the training the artificial intelligence system comprising updating the training data set with the feedback data and iteratively self-adjusting forecasts for a forward market price of the at least one of the plurality of machine-related resources based on the updated training data set that includes the feedback data, the forward market price being for at least one of:

the first resource; and

a second resource, among the at least one of the plurality of machine-related resources, that can be substituted for the first resource; and

determining a substitution cost of the second resource; and

a machine resource acquisition circuit structured to:

determine a machine-related resource acquisition value;

automatically solicit the configured purchase of the at least one of the plurality of machine-related resources in a forward market for at least one of the first resource or the second resource in response to the determined substitution cost of the second resource; and

configure the purchase in response to the machine-related resource acquisition value.

2. The system of claim 1 , wherein:

the expert system circuit is further configured to identify a timing of the configured purchase; and

the timing is based at least in part on the aggregated data.

3. The system of claim 2 , wherein the machine resource acquisition circuit is further structured to automatically solicit the configured purchase in response to the identified timing.

4. The system of claim 1 , wherein the intelligent agent circuit comprises a system comprising at least one of: a simple reflex system, a model-based reflex system, a goal-based system, a utility-based system, a learning system, an embodied system, a fuzzy system, or a data mining system.

5. The system of claim 1 , wherein the external data source further comprises at least one of: a market condition data source, an agent data source, or a historical outcome data source.

6. The system of claim 1 , wherein the determination of the machine-related resource acquisition value is based in part on at least one of: an expected cost range, a cost parameter of a machine resource, an effectiveness parameter of a machine resource, or a future predicted cost of one of the machine-related resource.

7. The system of claim 1 , wherein the expert system circuit is further structured to determine the machine-related resource acquisition value in response to a comparison of a first cost of the machine-related resource on a spot market of the machine-related resource with a cost parameter of the machine-related resource.

8. The system of claim 1 , wherein the expert system circuit is further structured to improve a future purchase configuration or timing identification based on a data set comprising outcomes resulting from purchases made under historical input conditions.

9. The system of claim 1 , wherein the external data source comprises at least one of: a bot, a crawler, or a dialog manager.

10. A method, comprising:

interpreting, by a controller, a resource requirement for a fleet of machines, each machine of the fleet of machines requiring a plurality of machine-related resources, the plurality of machine-related resources comprising at least two of: a compute resource, a spectrum resource, or a network bandwidth resource, the resource requirement including a first resource among the plurality of machine-related resources;

aggregating, by the controller, data from a plurality of data sources comprising at least an external data source, the aggregated data comprising data related to at least one of the plurality of machine-related resources, the external data source comprising at least a behavioral data source;

operating, by the controller, an intelligent agent to configure a purchase of the at least one of the plurality of machine-related resources in response to the resource requirement and the aggregated data, the configuring the purchase comprising:

maintaining a training data set comprising feedback data indicating outcomes of previous purchases;

training an artificial intelligence system based on the training data set, the training the artificial intelligence system comprising updating the training data set with the feedback data and iteratively self-adjusting forecasts for a forward market price of the at least one of the plurality of machine-related resources based on the updated training data set that includes the feedback data, the forward market price being for at least one of:

the first resource; and

a second resource, among the at least one of the plurality of machine-related resources, that can be substituted for the first resource; and

determining a substitution cost of the second resource;

determining a machine-related resource acquisition value;

soliciting, by the controller, the configured purchase of the at least one of the plurality of machine-related resources on a forward market for the at least one of the first resource or the second resource, such that a selection of at least one of the first resource or the second resource is based on a comparison of a cost of the first resource and the determined substitution cost of the second resource; and

configuring the purchase further in response to the machine-related resource acquisition value.

11. The method of claim 10 , further comprising identifying a timing of the configured purchase.

12. The method of claim 11 , wherein soliciting the configured purchase comprises soliciting in response to the identified timing.

13. The method of claim 12 , wherein identifying the timing of the configured purchase comprises determining a supply of and a demand for the machine-related resource, based at least in part on the aggregated data.

14. The method of claim 10 , wherein determining the machine-related resource acquisition value comprises comparing a first cost of the machine-related resource on a spot market for the resource with a cost parameter of the machine-related resource.

15. The method of claim 10 , further comprising performing a machine-related resource transaction in response to the machine-related resource acquisition value.

16. The method of claim 15 , wherein performing the machine-related resource transaction comprises an operation comprising at least one of: purchasing the machine-related resource, selling the machine-related resource, making an offer to sell the machine-related resource, or making an offer to purchase the machine-related resource.

17. The method of claim 10 , wherein determining the machine-related resource acquisition value is based in part on at least one of: an expected cost range, a cost parameter of a machine-related resource, an effectiveness parameter of a machine-related resource, or a future predicted cost of at least one of the plurality of machine-related resources.

18. The method of claim 10 , further comprising improving the configuring the purchase based on a data set comprising outcomes resulting from purchases made under historical input conditions.