Agent engine

• US 20040098358A1
• Filed: 12/24/2002
• Published: 05/20/2004
• Est. Priority Date: 11/13/2002
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Roediger, Karl Christian
• Granted Patent: US 7,711,670 B2
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06N 5/00 Computing arrangements using knowledge-based models

Abstract

An agent engine includes a definition process, the definition process operable to define a data set associated with an objective, a library storing a set of components, the components comprising at least one of a pre-programmed application, object, algorithm, function, and data set definition, and an agent generator process, the agent generator process operable to define at least one agent that includes at least one component from the library, the at least one generated agent defined to perform a function related to the objective.

Claims

1. An agent engine comprising:

a definition process, the definition process operable to define a data set associated with an objective;

a library storing a set of components, the components comprising at least one of a pre-programmed application, object, algorithm, function, and data set definition; and

an agent generator process, the agent generator process operable to define at least one agent that includes at least one component from the library, the at least one generated agent defined to perform a function related to the objective.

2. The agent engine of claim 1, further comprising:

a data feed from a first generated agent to a second generated agent, the data feed operable to pass data produced by the first agent to the second agent, the passed data usable by the second agent to apply a learning algorithm to a one of a rule and a data definition related to the objective.

5. The agent engine of claim 1, further comprises:

a generated agent having at least one of a filter application, a data aggregator, an association application, an analyzer, a modeler, and an actuator.

6. The agent engine of claim 1, further comprises:

a memory system, the memory system operable to store results produced from the generated agent.

7. The agent engine of claim 1, further comprises:

a data feed process operable to define a key performance indicator having a value or range of values.

8. The agent engine of claim 7, wherein the key performance indicator define a data set for a generated agent, the data set associated with the objective.

9. The agent engine of claim 2, further comprising:

a feedback loop to determine from a data feed whether the generated agent may be modified to improve performance.

10. The agent engine of claim 1, wherein at least two agents are generated to perform the business objective.

11. The agent engine of claim 1, further comprising:

a data correlation component to determine a one of a modification and a reinforcement to at least one of a rule, an action, and a generated agent.

12. The agent engine of claim 1, wherein at least one generated agent includes executable code to modify at least one of a rule and a key performance indicator.

13. The agent engine of claim 12, wherein the modification to the generated agent is based in part on a comparison of results produced during different cycles of execution of the generated agent.

14. The agent engine of claim 1, further comprises:

a fuzzy logic algorithm to evaluate a degree of completion of the generated agent.

15. The agent engine of claim 14, wherein evaluating a degree of completion comprises at least one of comparing different data sets, defining an absolute truth operation, defining a relative truth operation, and evolving a generated agent.

3. The agent engine of method 1, wherein the objective is a business objective.

4. The agent engine of claim 3, wherein the objective is an objective related to a business entity.

16. An agent engine architecture system comprising:

a plurality of levels, at least one level having an associated generated agent,

at least one of the plurality of levels operable to define an objective,

at least one key performance indicator (KPI) associated with the objective, and rules and parameters associated with the KPI;

at least one of the plurality of levels operable to filter, analyze, or act based upon a defined data set;

at least one of the plurality of levels operable to evaluate a result from another level and determine a degree to which the result satisfies the objective;

at least one of the plurality of levels operable to modify at least one of the KPI, and the generated agent; and

at least one of the plurality of levels operable to learn and determine a modification to at least one of a rule and a generated agent.

17. The system of claim 16, wherein at least one of the plurality of levels comprises a learning system for determining whether modifications to the at least one rule and agent are successful and for modifying the system based on an output of the generated agent.

19. The system of claim 16, further comprising a definition wizard, the definition wizard operable to generate at least one of a data set and a preprogrammed component for the generated agent.

18. The system of clam 16, further comprising a library of preprogrammed components, the components including at least one of an objective, a KPI, an algorithm, a function and an object.

20. A method of analyzing data comprising:

defining an objective;

defining at least one KPI associated with the objective;

linking at least one of a rule, algorithm or function with the associated KPI;

defining data to be analyzed;

evaluating the data against the objective;

determining the degree to which at least one of the KPI, rule, algorithm and function satisfy the objective;

modifying at least one of the associated KPI, rule, algorithm and function.

21. The method of claim 20, further comprises:

analyzing an effect of the modification; and

further modifying at least one of the associated KPI, rule, algorithm and function.

22. The method of claim 20, further comprising:

evaluating the data against the objective iteratively.

23. The method of claim 22, wherein evaluating the data against the objective iteratively continues until a result considered optimal is reached.

24. An article comprising a storage medium having stored thereon instructions that when executed by a machine results in the following:

define an objective;

define at least one KPI associated with the objective;

link at least one of a rule, algorithm or function with the associated KPI;

define data to be analyzed;

evaluate the data against the objective;

determine the degree to which at least one of the KPI, rule, algorithm and function satisfy the objective;

modify at least one of the associated KPI, rule, algorithm and function.

25. The article of claim 24, further comprises instructions that when executed by a machine results in the following:

analyze an effect of the modification; and

further modify at least one of the associated KPI, rule, algorithm and function.

26. The article of claim 24, further comprises instructions that when executed by a machine results in the following:

evaluate the data against the objective iteratively.

27. An agent engine system comprising:

a plurality of levels, at least one level having an associated generated agent,

a first of the plurality of levels operable to define an objective,

a second of the plurality of levels operable to execute at least one of a key performance indicator (KPI) associated with the objective, a rule, and an algorithm associated with the objective,

a third of the plurality of levels operable to receive an output from at least one of the plurality of levels and determine a one of a modification and a reinforcement of the rule and the algorithm.

28. The system of claim 27, wherein the second of the plurality of levels is further operable to filter, analyze, or act based upon a defined data set.

29. The system of claim 27, wherein the third of the plurality of levels is further operable to determine a degree to which the output satisfies the objective, and operable to modify at least one of the KPI, and the generated agent.

30. The system of claim 29, wherein the third of the plurality of levels is further operable to determine whether a modification to the one of the rule and the algorithm is successful.

32. The system of claim 27, wherein the first of the plurality of levels further comprises a definition wizard, the definition wizard operable to generate at least one of a data set and a preprogrammed component for the generated agent.

31. The system of clam 27, further comprising a library of preprogrammed components, the components including at least one of an objective, a KPI, an algorithm, a function and an object.