Translation of artificial intelligence representations

• US 10,572,968 B2
• Filed: 03/07/2019
• Issued: 02/25/2020
• Est. Priority Date: 01/27/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Riabov, Anton V., Sohrabi Araghi, Shirin, Udrea, Octavian
• Primary Examiner(s): Mazumder, Tapas
• Application Number:
  US16/295,731
• Publication Number:
  US 20190206021A1
• Time in Patent Office: 355 Days
• CPC Class Codes: G06F 16/532 Query formulation, e.g. graphical querying
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06T 1/20 Processor architectures; Processor configuration, e.g. pipelining

Abstract

Techniques for translating graphical representations of domain knowledge are provided. In one example, a computer-implemented method comprises receiving, by a device operatively coupled to a processor, a graphical representation of domain knowledge. The graphical representation comprises information indicative of a central concept and at least one chain of events associated with the central concept. The computer-implemented method further comprises translating, by the device, the graphical representation into an artificial intelligence planning problem. The artificial intelligence planning problem is expressed in an artificial intelligence description language. The translating comprises parsing the graphical representation into groupings of terms. A first grouping of terms of the grouping of terms comprises an event from the at least one chain of events and a second grouping of terms of the grouping of terms comprises the information indicative of the central concept. The computer-implemented method also comprises validating, by the device, the artificial intelligence planning problem.

Claims

1. A computer-implemented method, comprising:

receiving, by a device operatively coupled to a processor, a graphical representation of domain knowledge, wherein the graphical representation comprises information indicative of a central concept and at least one chain of events associated with the central concept, and wherein the graphical representation further comprises one or more probabilities of an event of the at least one chain of events; and

translating, by the device, the graphical representation into an artificial intelligence planning problem, wherein the translating comprises associating one or more weights with one or more predicates and one or more actions.

2. The computer-implemented method of claim 1, wherein the translating further comprises:

identifying one or more constants based on a grouping of terms;

identifying the one or more predicates and the one or more actions based on the one or more constants; and

encoding an initial state, the one or more constants, the one or more predicates, and the one or more actions into an artificial intelligence description language.

3. The computer-implemented method of claim 1, wherein the graphical representation further comprises the one or more weights associated with an event of the at least one chain of events.

4. The computer-implemented method of claim 1, wherein the artificial intelligence planning problem is expressed in an artificial intelligence description language.

5. The computer-implemented method of claim 1, wherein domain knowledge of the graphical representation of domain knowledge is changed, and the computer-implemented method further comprises propagating the changed graphical representation of domain knowledge.

6. The computer-implemented method of claim 5, wherein the propagating comprises encoding changes to an initial state, changes to the one or more constants, changes to the one or more predicates, and changes to the one or more actions into the artificial intelligence description language.

7. The computer-implemented method of claim 1, further comprising:

receiving, by the device, a second graphical representation; and

updating, by the device, an initial state of the domain knowledge based on the second graphical representation.

8. The computer-implemented method of claim 1, wherein the artificial intelligence planning problem comprises one or more observations from the graphical representation, and wherein the one or more observations are one or more unreliable observations.

9. The computer-implemented method of claim 1, wherein the artificial intelligence planning problem projects a future state.

10. The computer-implemented method of claim 1, wherein an action of the one or more predicates and the one or more actions is associated with an action cost.

11. A computer program product facilitating translating a graphical representation of domain knowledge, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing unit to cause the processing unit to:

receive a graphical representation of domain knowledge, wherein the graphical representation comprises information indicative of a central concept and at least one chain of events associated with the central concept, and wherein the graphical representation further comprises one or more probabilities of an event of the at least one chain of events; and

translate the graphical representation into an artificial intelligence planning problem, wherein the translating comprises associating one or more weights with one or more predicates and one or more actions.

12. The computer program product of claim 11, wherein the translation further comprises:

identification of one or more constants based on a grouping of terms;

identification of the one or more predicates and the one or more actions based on the one or more constants; and

an encoding of an initial state, the one or more constants, the one or more predicates, and the one or more actions into an artificial intelligence description language.

13. The computer program product of claim 11, wherein the graphical representation further comprises the one or more weights associated with an event of the at least one chain of events.

14. The computer program product of claim 11, wherein the artificial intelligence planning problem is expressed in an artificial intelligence description language.

15. The computer program product of claim 11, wherein domain knowledge of the graphical representation of domain knowledge is changed, and the program instructions are further executable by the processing unit to cause the processing unit to propagate the changed graphical representation of domain knowledge.

16. The computer program product of claim 15, wherein the propagation comprises an encoding of changes to an initial state, changes to the one or more constants, changes to the one or more predicates, and changes to the one or more actions into the artificial intelligence description language.

17. The computer program product of claim 11, wherein the program instructions are further executable by the processing unit to cause the processing unit to

receive a second graphical representation;

and

update an initial state of the domain knowledge based on the second graphical representation.

18. A system, comprising:

a memory that stores computer executable components; and

a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise;

a translation component that;

receives a graphical representation of domain knowledge, wherein the graphical representation comprises information indicative of a central concept and at least one chain of events associated with the central concept, and wherein the graphical representation further comprises one or more probabilities of an event of the at least one chain of events; and

translates the graphical representation into an artificial intelligence planning problem, wherein the translating comprises associating one or more weights with one or more predicates and one or more actions.

19. The system of claim 18, wherein domain knowledge of the graphical representation of domain knowledge is changed, and the system also propagates the changed graphical representation of domain knowledge.

20. The system of claim 19, wherein the propagation comprises an encoding of changes to an initial state, changes to the one or more constants, changes to the one or more predicates, and changes to the one or more actions into the artificial intelligence description language.