Interruptibility/priority control scheme for artificial intelligence software shell

• US 5,402,526 A
• Filed: 01/05/1993
• Issued: 03/28/1995
• Est. Priority Date: 01/05/1993
• Status: Expired due to Fees

• Current Assignee: Mitsubishi Denki Kabushiki Kaisha (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Denki Kabushiki Kaisha (Mitsubishi Electric Corporation)
• Inventors: Naitoh, Hatsuhiko, Schultz, Brian A., Lowenfeld, Simon, Thompson, Robert W. Jr., Bauman, Douglas A.
• Primary Examiner(s): Downs, Robert W.
• Application Number:
  US08/000,873
• Time in Patent Office: 812 Days
• Field of Search: 395/53, 395/77, 395/68, 395/50, 395/61
• US Class Current: 706/49
• CPC Class Codes: G06N 5/043 Distributed expert systems; Blackboards

Abstract

An artificial intelligence software shell for plant operation simulation includes a blackboard module including a database having objects representing plant elements and concepts. A rule-based knowledge source module and a case-based knowledge source module, in communication with the blackboard module, operate on specific predefined blackboard objects. An input data module, in communication with the blackboard module, enables a user to input data to the shell. A control module, in communication with the knowledge source modules and the input data module, receives all input data and controls operation of the knowledge source modules in accordance with a predetermined knowledge source interruptibility/priority scheme. The control module including an event detector module having a hash table defined by a chaining algorithm.

Claims

1. An artificial intelligence software shell for plant operation simulation comprising:

a blackboard module including a database having objects representing plant elements and concepts;

at least one knowledge source module including an artificial intelligence operation scheme, in communication with the blackboard module, operating on specific predefined blackboard objects;

an input data module, in communication with the blackboard module and the at least one knowledge source module, enabling data to be inputted to the shell; and

a control module, in communication with the input data module and the at least one knowledge source module, receiving all input data and controlling operation of the at least one knowledge source in accordance with a predetermined knowledge source interruptibility/priority scheme;

wherein the control module includes an event detector module, in communication with the input data module, receiving all input data and determining when the at least one knowledge source module should execute, the event detector module having a hash table defined by a chaining algorithm.

2. A software shell as claimed in claim 1 wherein the control module includes:

an activation/agenda manager module, in communication with the at least one knowledge source module and the event detector module, executing the at least one knowledge source module in accordance with the predetermined knowledge source interruptibility/priority scheme.

3. A software shell as claimed in claim 2 wherein the at least one knowledge source module includes:

a rule-based knowledge source module having a forward-chaining belief propagation scheme including rules in if-then-else form with associated levels of belief; and

a case-based knowledge source module having a data comparison scheme including predefined patterns and conditions, whereupon execution of the case-based knowledge source, the conditions are inferred to be true if a certain level of closeness is found between received data and the patterns.

4. A software shell as claimed in claim 3 wherein the at least one knowledge source module includes a plurality of knowledge source modules having varying levels of priority.

5. A software shell as claimed in claim 3 wherein each of said modules includes a software process.

6. A software shell as claimed in claim 3 wherein the blackboard module database includes a hierarchy of objects having vertical hierarchical connections and horizontal links.

7. A software shell as claimed in claim 6 wherein each blackboard object has attributes and each attribute has at least one value, the database including a hash table and plurality of pointers for linking the objects and corresponding attributes and values.

8. A software shell as claimed in claim 7 wherein the event detector module further includes:

the hash table having entries for each input data source;

a data point structure including entries for objects and corresponding attributes and values, the entries of the hash table pointing to entries in the data point structure;

an indirect expression list pointed to by the data point structure; and

an expression list, pointed to by the indirect expression list including entries for knowledge source previous and updated values.

9. A software shell as claimed in claim 8 wherein the event detector module further includes a message evaluator, receiving messages related to input data, checking the messages for syntax errors and implementing the hash table algorithm if no syntax error is found.

10. A software shell as claimed in claim 9 wherein the activation/agenda manager module further includes:

a FIFO queue for activation of event driven knowledge sources;

a time-based queue for periodic knowledge sources; and

a knowledge source/precondition expression structure for checking knowledge source execution preconditions.

11. A software shell as claimed in claim 10 wherein the activation/agenda manager module further includes:

an array having objects and corresponding attributes and values; and

an expression list including each knowledge source pointed to by the array.

12. A software shell as claimed in claim 6 wherein the vertical hierarchical connections include IS-A and PART-OF hierarchical connections.

13. A software shell as claimed in claim 2 wherein the activation/agenda manager module includes a mechanism for interrupting execution of a knowledge source and executing a higher priority knowledge source in accordance with the knowledge source interruptibility/priority scheme.

14. A software shell as claimed in claim 13 wherein the activation/agenda manager module includes a mechanism for determining whether an interrupted knowledge source should be rescheduled for execution.

15. A software shell as claimed in claim 2 wherein the event detector module includes a data structure implementing an algorithm such that when data is received, only a knowledge source which operates on the received data is checked for execution availability.

16. A software shell as claimed in claim 1 wherein the at least one knowledge source module includes:

a rule-based knowledge source module having a forward-chaining belief propagation scheme including rules in if-then-else form with associated levels of belief; and

a case-based knowledge source module having a data comparison scheme including predefined patterns and conditions, whereupon execution of the case-based knowledge source, the conditions are inferred to be true if a certain level of closeness is found between received data and the patterns.

17. A software shell as claimed in claim 16 further including:

a blackboard structure description file including object names and attribute/value pairs for each object;

a rule-based description file including rules forming the rule-based knowledge source and a description of execution preconditions for the rule-based knowledge source; and

a case-based description file including cases forming the case-based knowledge source and a description of execution preconditions for the case-based knowledge source;

wherein the blackboard structure description file, rule-based description file, case-based description file, and input data file are user created.

18. A software shell as claimed in claim 17 further including:

a diagnostics file, created by the shell for each input data file cycle completed, including diagnoses made by the rule-based knowledge source and case matches found by the case-based knowledge source; and

a blackboard status file, created by the shell, including blackboard information as modified by any knowledge sources after completion of each input data file cycle.

19. A software shell as claimed in claim 1 wherein the blackboard module database includes a hierarchy of objects having vertical hierarchical connections and horizontal links.

20. A software shell as claimed in claim 19 wherein each blackboard object has attributes and each attribute has at least one value, the database including a hash table and plurality of pointers for linking the objects and corresponding attributes and values.

21. A software shell as claimed in claim 19 wherein the vertical hierarchical connections include IS-A and PART-OF hierarchical connections.

22. An artificial intelligence software shell for plant operation simulation comprising:

means for storing objects representing plant elements and concepts;

at least one knowledge source module including and artificial intelligence operation scheme, in communication with the means for storing, operating on specific predefined objects;

means, in communication the means for storing and the at least one knowledge source module, for enabling data to be inputted to the shell; and

means, in communication the means for enabling and the at least one knowledge source module, for receiving all input data and controlling operation of the at least one knowledge source module in accordance with a predetermined knowledge source interruptibility/priority scheme;

wherein the means for receiving and controlling includes;

means, in communication with the means for enabling, for determining when the at least one knowledge source module should execute, the means for determining including a hash table defined by a chaining algorithm.

23. A software shell as claimed in claim 22 wherein the means for receiving and controlling includes:

means, in communication with the at least one knowledge source module and the means for determining, for executing the at least one knowledge source module in accordance with the predetermined knowledge source interruptibility/priority scheme.

24. A software shell as claimed in claim 23 wherein the at least one knowledge source module includes:

a rule-based knowledge source module having a forward-chaining belief propagation scheme including rules in if-then-else form with associated levels of belief; and

a case-based knowledge source module having a data comparison scheme including predefined patterns and conditions, whereupon execution of the case-based knowledge source, the conditions are inferred to be true if a certain level of closeness is found between received data and the patterns.

25. A software shell as claimed in claim 24 wherein the means for storing includes a database having a hierarchy of objects having vertical hierarchical connections and horizontal links.

26. A software shell as claimed in claim 25 wherein each object has attributes and each attribute has at least one value, the database including a hash table and a plurality of pointers for linking the objects and corresponding attributes and values.

27. A software shell as claimed in claim 26 wherein the means for executing includes means for interrupting execution of a lower priority knowledge source and executing a higher priority knowledge source in accordance with the knowledge source interruptibility/priority scheme.

28. A software shell as claimed in claim 27 wherein the means for determining includes means for receiving input data, determining when knowledge source execution preconditions are met, and checking only a knowledge source whose execution preconditions are met for execution availability.

29. A software shell as claimed in claim 28 further including:

a blackboard structure description file including object names and attribute/value pairs for each object;

a rule-based description file including rules forming the rule-based knowledge source and a description of execution preconditions for the rule-based knowledge source; and

a case-based description file including cases forming the case-based knowledge source and a description of execution preconditions for the case-based knowledge source;

wherein the blackboard structure description file, rule-based description file, case-based description file, and input data file are user created.

30. A software shell as claimed in claim 29 further including:

a diagnostics file, created by the shell for each input data file cycle completed, including diagnoses made by the rule-based knowledge source and case matches found by the case-based knowledge source; and

a blackboard status file, created by the shell, including blackboard information as modified by any knowledge sources after completion of each input data file cycle.

31. A software shell as claimed in claim 30 further including stream sockets for intercommunication, the sockets communicating messages in the form of a request-response pair.

32. A method of simulating plant operation with an artificial intelligence software shell comprising the steps of:

storing, in a database, objects representing plant elements and concepts;

reading input data from an input data source;

determining, from the input data read, when artificial intelligence knowledge sources should execute in accordance with a predetermined knowledge source priority scheme; and

executing knowledge sources, in accordance with the determination, on specific predefined objects;

wherein the step of determining includes the step of implementing a hash table chaining algorithm.

33. A method as claimed in claim 32 wherein the step of executing knowledge sources includes the step of updating object values.

34. A method as claimed in claim 33 wherein the step of executing knowledge sources includes the steps of:

executing rule-based knowledge sources having a forward-chaining belief propagation scheme including rules in if-then-else form with associated levels of belief;

and executing case-based knowledge sources having a data comparison scheme including predefined patterns and conditions.

35. A method as claimed in claim 34 wherein the step of executing case-based knowledge sources includes the step of inferring to be true the conditions if a certain level of closeness is found between received data and the patterns.

36. A method as claimed in claim 35 wherein the step of storing includes the step of storing objects, attributes and values, each object having an attribute and each attribute having at least one value.

37. A method as claimed in claim 36 wherein the step of executing knowledge sources includes the step of interrupting execution of a lower priority knowledge source and executing a higher priority knowledge source in accordance with a knowledge source interruptibility/priority scheme.

38. A method as claimed in claim 37 wherein the step of executing knowledge sources further includes the step of, after the step of interrupting, determining whether an interrupted knowledge source should be rescheduled for execution.

39. A method as claimed in claim 38 wherein the step of executing knowledge sources further includes the step of, after determining whether an interrupted knowledge source should be rescheduled for execution, rescheduling that knowledge source in accordance with the determination.

40. A method as claimed in claim 33 wherein the step of determining includes the steps of determining when knowledge source execution preconditions are met and checking only a knowledge source whose execution preconditions are met for execution availability.

41. A method as claimed in claim 39 wherein the step of determining includes the steps of determining when knowledge source execution preconditions are met and checking only a knowledge source whose execution preconditions are met for execution availability.

42. A method as claimed in claim 40 further including the steps of:

generating a blackboard structure description file including object names and attribute/value pairs for each object;

generating a rule-based description file including rules forming the rule-based knowledge source and execution preconditions;

generating a case-based description file including cases and a description of execution preconditions; and

generating an input data file including a linked list with sets of data and corresponding time tags simulating input data for running diagnostic cycles.

43. A method as claimed in claim 42 further including the steps of:

generating a diagnostics file including diagnoses made by the rule-based knowledge sources and case matches found by the case-based knowledge sources; and

generating a blackboard status file including blackboard information as modified by any knowledge sources during execution.

44. A method as claimed in claim 33 further including the steps of communicating messages through sockets between modules in the form of request-response pairs.

45. A method as claimed in claim 33 wherein the step of executing knowledge sources further includes the step of executing model-based knowledge sources having interconnected modules with simulation relationships and inference relationships.

46. A control module for an artificial intelligence software shell for plant operation simulation, the shell including a blackboard module database having objects representing plant elements and concepts, and at least one knowledge source module including an artificial intelligence operation scheme for execution on the blackboard objects, the control module comprising:

an event detector module, in communication with the blackboard module, receiving all input data and determining when the at least one knowledge source module should execute; and

an activation/agenda manager module, in communication with the at least one knowledge source module and the event detector module, executing the at least one knowledge source module in accordance with a predetermined knowledge source interruptibility/priority scheme;

wherein the event detector module includes a hash table defined by a chaining algorithm.

47. An artificial intelligence software shell for plant operation simulation comprising:

a blackboard module including a database having objects representing plant elements and concepts;

at least one knowledge source module including an artificial intelligence operation scheme, in communication with the blackboard module, operating on specific predefined blackboard objects;

an input data module, in communication with the blackboard module and the at least one knowledge source module, enabling data to be inputted to the shell; and

a control module, in communication with the input data module and the at least one knowledge source module, receiving all input data and controlling operation of the at least one knowledge source in accordance with a predetermined knowledge source interruptibility/priority scheme, the control module includes a hash table defined by a chaining algorithm.

48. An event detector module for an artificial intelligence software shell for plant operation simulation, the shell including a blackboard module database having objects representing plant elements and concepts, and at least one knowledge source module including an artificial intelligence operation scheme for execution on the blackboard objects, the event detector module comprising:

a communication module receiving input data; and

a determination module, including a hash table defined by a chaining algorithm, for determining when the at least one knowledge source module should execute.