DISTRIBUTED ACTIVITY CONTROL SYSTEMS AND METHODS

• US 20180365566A1
• Filed: 05/14/2018
• Published: 12/20/2018
• Est. Priority Date: 09/18/2014
• Status: Active Grant

• Current Assignee: Robert D. Pedersen
• Original Assignee: Robert D. Pedersen
• Inventors: Pedersen, Robert D.
• Granted Patent: US 10,565,505 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 9/4843 by program, e.g. task dispatcher, supervisor, operating system
• CPC Class Codes: G06F 9/5027 the resource being a machine, e.g. CPUs, Servers, Terminals
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

A dynamic, distributed directed activity network comprising a directed activity control program specifying tasks to be executed including required individual task inputs and outputs, the required order of task execution, and permitted parallelism in task execution; a plurality of task execution agents, individual of said agents having a set of dynamically changing agent attributes and capable of executing different required tasks in said activity control; a plurality of task execution controllers, each controller associated with one or more of the task execution agents with access to dynamically changing agent attributes; a directed activity controller for communicating with said task execution controllers for directing execution of said activity control program; a communications network capable of supporting communication between said directed activity controller and task execution controllers; and wherein said directed activity controller and task execution controllers communicate via said communication network to execute said directed activity control program using selected task execution agents.

Claims

1. A method for operation of one or more specifically programmed directed activity control computer machines comprising a combination of the following steps:

a step of electronically storing in memory of said one or more specifically programmed directed activity control computer machine one or more directed activity control programs;

wherein said directed activity control programs define one or more of the following;

i) required individual directed activity task inputs and outputs;

ii) the required precedence order of specific task execution; and

iii) permitted parallelism in task execution;

a step of electronically storing in memory of said one or more specifically programmed directed activity control computer machines history files of potentially dynamically changing task execution attributes of distributed task execution agents accessible to execute selected tasks,

a step of electronically storing in memory of said one or more specifically programmed directed activity control computer machines one or more artificial intelligence expert system defined propositional logic program statements;

wherein said artificial intelligence expert system defined propositional logic program statements define one or more of the following;

i) potentially dynamically changing task execution attributes; and

ii) task execution attribute relationships for distributed resources potentially available to execute said directed activity tasks;

a step of electronically executing by the one or more specifically programmed directed activity control computer machines said one or more directed activity control programs, wherein said one or more specifically programmed directed activity control computer machines performs at least the following;

a step of electronically transmitting messages to at least one contained distributed task execution agent;

wherein said electronically transmitted messages provide instructions to direct execution of said tasks according to requirements of said directed activity control programs; and

a step of electronically receiving messages by said one or more specifically programmed directed activity control computer machines;

wherein said electronically received messages provide sensor derived information about task execution status and potentially dynamically changing contained network distributed task execution agent attributes;

a step of updating potentially dynamically changing task execution attributes stored in memory based on information in received messages,

a step of electronically analyzing said electronically received messages and dynamically updated attributes using one or more of said electronic programmable specialized directed activity control computer machines with artificial intelligence expert system decision making electronic capability and

wherein the step of analyzing said electronically received messages includes;

a step of generating expert systems comparisons with said one or more electronic programmable directed activity control computer machines of said artificial intelligence expert system defined propositional logic program statements and said electronically received message attribute analysis;

a step of analyzing said artificial intelligence expert systems comparisons to derive task dispatch indices indicating the relative desirability of using particular task execution agents among multiple potential distributed task execution agents for particular task executions; and

a step of selecting a particular contained task execution agent for executing a particular task based on comparisons of said artificial intelligence expert system analysis derived task dispatch indices wherein said artificial intelligence expert system selection is further comprises a statistical analysis of a history data base of sample times required to execute a particular task with a particular task execution agent; and

a step of transmitting by the one or more electronic programmable directed activity control computer machines a control message to execute the analyzed task using a selected contained task execution agent, and

whereby said operation of one or more specifically programmed directed activity control computer machines comprising the above combination of steps includes the following;

task resource dispatch decisions responsive to sensor derived dynamically changing task execution agents attributes based on said received messages, selection of one resource from among multiple task execution agents for execution of a particular task based on dynamically changing task execution agent attributes in said received messages analysis using artificial intelligence expert systems operations, and

resource utilization efficiency achieved from the use of artificial intelligence expert systems decision making in the execution of a directed activity control program based on sensor derived information.

2. The method of claim 1 wherein the time required to execute a particular task with a particular task execution agent may vary depending upon the utilization of the resource.

3. The method of claim 1 wherein the time required to execute a particular task with a particular task execution agent may vary depending upon the speed of the resource.

4. The method of claim 1 wherein the time required to execute a particular task with a particular task execution agent may vary depending upon maintenance issues with the resource.

5. The method of claim 1 wherein said distributed task execution agents comprise factory machinery

6. The method of claim 1 wherein said distributed task execution agents comprise automation technology.

7. The method of claim 1 wherein task execution distributed task execution agents dynamically variable attributes comprise capability, capacity, speed, availability, utilization, location, quantity or cost.

8. The method of claim 1 wherein task execution distributed task execution agent dynamically variable attributes comprise capability, availability, and location.

9. The method of claim 1 wherein task execution distributed task execution agent dynamically variable attributes comprise speed and availability.

10. The method of claim 1 wherein task execution distributed task execution agent dynamically variable attributes comprise availability and cost.

11. The method of claim 1 wherein the task execution agents may comprise computers, storage units, transportation equipment, manufacturing equipment, factories, personnel or communications equipment.

12. The method of claim 1 wherein the task execution agents comprise transportation equipment and communications equipment.

13. The method of claim 1 wherein the task execution agents comprise computers and manufacturing equipment.

15. The method of claim 1 wherein the task execution agents comprise personnel and communications equipment.

16. The method of claim 1 wherein said artificial intelligence task dispatch indices values comprises multiple ranges of values.

17. The method of claim 16 wherein said artificial intelligence task dispatch indices ranges comprise very low, low, medium, high and very high values.

18. The method of claim 16 wherein the said artificial intelligence task dispatch indices comprise values for two variable task execution attributes.

19. The method of claim 16 wherein the said artificial intelligence task dispatch indices comprise values for more than two variable task execution attributes.

20. The method of claim 17 wherein the very low, low, medium, high and very high ranges may overlap.

21. The method of claim 20 wherein said artificial intelligence comprises fuzzy logic analysis.

22. The method of claim 1 wherein operation of one or more specifically programmed directed activity control computer machines further comprises digital computer processing with mathematical optimization tools to derive task dispatch indices indicating the relative desirability of using particular task execution agents among multiple potential distributed task execution agents for particular task executions.

23. The method of claim 22 wherein the stored dynamically variable attribute information comprises a dynamic model representation of the status of distributed task execution agents.

24. The method of claim 23 wherein the stored dynamic model task execution resource attributes may indicate that particular task execution agents are unavailable to be used.

25. The method of claim 1 wherein the steps of electronically transmitting messages to at least one contained distributed task execution agent and electronically receiving messages by said one or more specifically programmed directed activity control computer machines comprise communication with cloud computing resources via the World-Wide-Web Internet.

26. The method of claim 1 wherein the statistical analysis of the history data base of sample times required to execute a particular task with a particular task execution agent further comprises computation of the average time to execute said task.

27. The method of claim 1 wherein the statistical analysis of the history data base of sample times required to execute a particular task with a particular task execution agent further comprises computation of the variance of the time to execute said task.

28. The method of claim 1 wherein the statistical analysis of the history data base of sample times required to execute a particular task with a particular task execution agent further comprises computation of the probability of waiting longer than a specified time to gain access to a specific task execution agent.

30. The method of claim 26 wherein the statistical analysis of the history data base of sample times required to execute a particular task with a particular task execution agent further comprises adding a multiple of the average waiting time corresponding to the number of waiting tasks to obtain a more accurate estimate of the total required time for completion of a particular task by a particular task execution agent.

31. A method for operation of one or more specifically programmed artificial intelligence expert system control computer machines for control of execution interrelated dependent tasks in a distributed network of task execution agents and controllers comprising a combination of the following steps:

a step of electronically storing in memory of said one or more specifically programmed artificial intelligence expert system control computer machine one or more artificial intelligence expert system control programs defining;

i) required individual task inputs and outputs for successive tasks;

ii) the required precedence order of specific task execution; and

iii) permitted parallelism in task execution;

a step of electronically storing in memory of said one or more specifically programmed artificial intelligence expert system control computer machines expert defined propositional logic statements comprising program statements defining conditional event dependent task execution relationships;

a step of electronically storing in memory of said one or more specifically programmed artificial intelligence expert system control computer machines history files of potentially dynamically changing task execution agent attributes of distributed task execution agents accessible to execute selected tasks,

a step of electronically executing by the one or more specifically programmed artificial intelligence expert system control computer machines said one or more artificial intelligence expert system programs;

a step of electronically transmitting messages to at least one contained distributed task execution agent wherein said electronically transmitted messages provide instructions to direct execution of said tasks according to artificial intelligence expert system control programs;

a step of electronically receiving messages by said one or more specifically programmed artificial intelligence expert system control computer machines providing task execution status, sensor derived information and potentially dynamically changing contained network distributed task execution agent attributes;

a step of updating potentially dynamically changing task execution attributes stored in memory attribute history files based on information in received messages,

a step of electronically analyzing electronically received messages and dynamically updated task execution agent attributes and deriving task dispatch indices indicating the relative desirability of using particular task execution agents among multiple potential distributed task execution agents for particular task executions using one or more of said electronic programmable specialized artificial intelligence expert system computer machines

a step of selecting a particular contained task execution agent for executing a particular task based on derived task dispatch indices wherein said artificial intelligence expert system selection further comprises a statistical analysis of an attribute history database of sample times required to execute a particular task with a particular task execution agent;

a step of transmitting by the one or more electronic programmable artificial intelligence control computer machines a control message to execute the analyzed task using a selected contained task execution agent,

whereby improved resource utilization efficiency is achieved from the use of artificial intelligence expert systems decision making in the execution of artificial intelligence expert system control program based on sensor derived information.