Systems and methods for forward market purchase of machine resources
Systems and methods for automatically soliciting the purchase of a first or second machine-related resource in a forward market, wherein the first resource and the second resource are distinct instances of the same type of resource, are described. A sample system may include a fleet of machines, each having a resource requirement comprising at least two of: a compute resource, a spectrum resource, or a network bandwidth resource. The system may include an circuits to aggregate data corresponding to the machine-related resources from at least a behavioral data source, to determine a substitution cost of a second resource; to determine a machine-related resource acquisition value; and to automatically solicit a purchase, in a forward market, of one of the first resource or the second resource in response to the determined substitution cost of the second resource.
1. A transaction-enabling system, comprising:
an interface configured to communicate with 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 in communication with the fleet of machines via the interface, the 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 respective outcomes of previous purchases and, for each respective outcome, at least one of: a facility parameter, a yield parameter, a profit parameter, a resource optimization parameter, a user satisfaction parameter, or an operator satisfaction parameter;
training an artificial intelligence system based on the training data set to self-adjust forecasts for a forward market price of the at least one of the plurality of machine-related resources based on the 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,
wherein the first resource and the second resource are distinct instances of the same type of 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; and
automatically solicit the configured purchase of the at least one of the first resource or the second resource in a forward market for the at least one of the first resource or the second resource based on the machine-related resource acquisition value and the substitution cost of the second resource,
wherein the expert system circuit is further structured to update the training data set with additional feedback data corresponding to the configured purchase and reinforce the artificial intelligence system based on the additional feedback data.
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 based on the identified timing.
4. The system of claim 1 , wherein the expert system circuit is further configured to determine at least a portion of the substitution cost of the second resource as an operational change cost for at least one of the fleet of machines.
5. 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.
6. 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.
7. 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.
8. The system of claim 1 , wherein the machine resource acquisition 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.
9. 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.
10. The system of claim 1 , wherein:
the external data source comprises at least one of a bot, a crawler, or a dialog manager.
11. 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 respective outcomes of previous purchases and, for each respective outcome, at least one of a facility parameter, a resource optimizing parameter, a user satisfaction parameter, or an operator satisfaction parameter;
training an artificial intelligence system based on the training data set to self-adjust forecasts for a forward market price of the at least one of the plurality of machine-related resources based on the 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, wherein the first resource and the second resource are distinct instances of the same type of resource;
determining a substitution cost of the second resource; and
determining a machine-related resource acquisition value;
soliciting, by the controller, the configured purchase 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 on the machine-related resource acquisition value;
updating the training data set with additional feedback data corresponding to the configured purchase and reinforced the artificial intelligence system based on the additional feedback data; and
reinforcing the artificial intelligence system based on the feedback data.
12. The method of claim 11 , further comprising identifying a timing of the configured purchase.
13. The method of claim 12 , wherein soliciting the configured purchase comprises soliciting based on the identified timing.
14. The method of claim 13 , 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.
15. The method of claim 11 , 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.
16. The method of claim 11 , further comprising performing a machine-related resource transaction in response to the machine-related resource acquisition value.
17. The method of claim 16 , 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.
18. The method of claim 11 , 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.
19. The method of claim 11 , further comprising improving the configuring the purchase based on a data set comprising outcomes resulting from purchases made under historical input conditions.
20. The method of claim 11 , further comprising determining at least a portion of the substitution cost of the second resource as an operational change cost for at least one of the fleet of machines.