Massively Distributed Problem Solving Agent

• US 20140101079A1
• Filed: 10/10/2012
• Published: 04/10/2014
• Est. Priority Date: 10/10/2012
• Status: Abandoned Application

• Current Assignee: Steven Vaughn Deal
• Original Assignee: Steven Vaughn Deal
• Inventors: Deal, Steven Vaughn
• Application Number:
  US13/649,105
• Publication Number:
  US 20140101079A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 5/045 Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence

Abstract

A knowledge processing system that guides massive numbers of human agents and artificial agents to define, explore, and develop solutions for complex, problematic situations, that facilitates a seven-step process that proceeds from instance initiation, problem definition, problem exploration, approach selection, solution selection, to a time-sequenced action plan, and enables agents to subsequently modify process outputs and the action plan; a hybrid facilitation system in which machine and human agents jointly select the content and order of process steps and the system prompts that guide the problem-solving process; and an information-overload mitigation system which reduces the amount of information agents are required to process by means of natural language processing and graphical processing algorithms that characterize agent inputs and background materials, and identify, tag, sequester, and eliminate identical content and direct useful content to agent-defined points of application in the process.

Claims

1. A knowledge processing system that enables massive numbers of human agents and artificial intelligence agents to define, explore, and develop a solution for a problematic situation, where massive is bounded by the number of agents on the internet worldwide, comprising:

an electromagnetic communications network;

client devices that enable human and artificial intelligence agents to supply inputs to the knowledge processing system and receive outputs from the knowledge processing system over said network;

said client devices configurable to provide private workspaces for agents from which other agents are precluded and public workspaces in which agents jointly work;

an information storage and access system for managing knowledge processing system outputs and agent inputs;

a facilitation system configured to guide agents through the exercise of problem-solving steps;

said steps are problem definition, identification of the topical information domains of which the problematic situation is comprised, exploration of the problematic situation, definition of solution approaches, selection of a solution approach from those defined by agents, description of the solution approaches, and time-sequenced action plan creation;

said facilitation system presents to agents for selection as solution approaches technological, investment, engineering, data mining, experiment, information campaigning, organizational change, regulatory change, research, additional expertise, statutory change, education, marketing, quality improvement, and visioning approaches;

said problem-solving exercise is comprised of parent forums in which the problematic situation is considered as a whole and child forums in which the problematic situation is considered in parts as described by agent-identified, topical-information domains;

said facilitation system decomposes the problematic-situation exploration step and the solution-definition step into child forums, one child forum for each topical-information domain of which the problematic situation is comprised;

said facilitation system guides agents in the synthesis and relation of problematic-situation exploration child forum findings into a holistic representation of the problematic situation;

said facilitation system guides agents in the synthesis and relation of solution-defining, child-forum findings into an integrated solution comprised of solutions that address sub problems of the problematic situation that are peculiar to topical-information domains;

said facilitation system automatically assigns agents to participate in child forums based on self-identified expertise and interests elicited from agents by the system;

said facilitation system transmits input prompts to agents;

some of said input prompts direct agents to provide a description of the problematic situation;

some of said input prompts direct agents to provide a description the temporal and financial constraints and goals of the problem-solving situation;

some of said input prompts direct agents to select questions from a list of questions maintained in said data storage system;

some of said input prompts direct agents to submit answers to questions;

some of said input prompts direct agents to clarify answers in a dialogue format;

some of said input prompts direct agents to select answers from among those submitted by agents;

some of said prompts direct agents to preferentially rank answers;

said data storage system configured to associate agent responses with system-generated prompts;

said data storage system configured to store agent responses to prompts;

said storage and access system aggregating system outputs and agent inputs so they can be reused in whole or in part by other implementations of the system for other problematic situations;

said facilitation system enforces agent-defined, time constraints of the problem-solving exercise.

2. The system in claim 1, wherein knowledge processing tasks are knowledge generation, knowledge clarification, knowledge selection, knowledge structuring, knowledge refinement, information collection, dialoguing, polling, analogy development, mental simulation exercises, and storytelling.

3. The system in claim 1, wherein agents are prompted to perform cognitive simulations of outcomes achieved using an approach and engage in dialogues to probe the strengths and weakness of the approach in resolving the problematic situation;

4. The system in claim 1, wherein agents contribute background or reference materials in the form of textual, audio or visual materials that are related to the problematic situation to the data storage and access system both as a precursor to a step in the problem-solving exercise and in the course of exchanging ideas in dialogue with other agents.

5. The system in claim 1, wherein the exercise of problem-solving steps includes a feedback step in which agents are prompted to revisit one or more of the preceding problem-solving steps in order to modify the outputs in response to a change in the problematic situation.

6. The system in claim 1, wherein agents receive rewards commensurate with the quality and quantity of their contributions to the problem-solving exercise and are assessed penalties for contributions that detract from the problem-solving exercise, wherein penalties can extend to an agent suspension from further participation, requiring the agent to undergo a reinstatement process to resume participation.

7. The system in claim 6, wherein the knowledge processing system is configured as a multi-level game with the problem-solving steps serving as game levels and the rewards and penalties are tallied by the facilitation system and presented in the form of game scores wherein agents receive scores for excellence in the present exercise, for excellence in all exercises of the system in which they have participated, and for their expertise in a particular domain.

8. The system in claim 7, wherein agent scores are made available to employers as part of an employment process external to but interfacing with the present invention in which the agent scores are used as indicators of agent skills, knowledge, expertise and qualification.

9. A hybrid facilitation system in which human agents and artificial intelligence agents collaboratively facilitate the actions of a knowledge processing system, comprising:

a knowledge processing system in which agent-submitted questions are stored by the system;

said knowledge processing system employs agent-submitted questions to guide a problem solving exercise;

said knowledge processing system incorporates agent-submitted questions into the knowledge processing system baseline for use in future problem solving instances;

the order of knowledge processing steps is agent-defined by means of a polling process, to fit the temporal and financial constraints of a problem-solving activity;

knowledge processing tasks comprised of knowledge generation, knowledge clarification, knowledge selection, knowledge structuring, knowledge refinement, information collection, dialoguing, polling, analogy development, mental simulation exercises, and storytelling tasks are presented to agents as process building blocks;

said building blocks and the order in which the building blocks are used are selected by agents through a polling process.

10. The system in claim 9, wherein the system semantically critiques agent-generated questions and provides an assessment of sufficiency and effectiveness, and provides guidance on improving agent-generated questions when the system determines a superior version can be achieved.

11. The system in claim 9, wherein the system prompts agents to submit answers to questions and the knowledge processing system determines sequential dependencies between and among the agent-submitted answers and presents the dependencies in a graphical, hierarchical display.

12. The system in claim 11, wherein the system prompts agents to review and revise the sequential dependencies determined by the knowledge processing system by presenting pairwise comparisons of agent answers and prompting agents to determine relationships between answers by means of participant polling.

13. The system in claim 9, wherein the system guides agents to decompose the problematic situation into constituent parts, the system assigns agents the constituent parts in which they will participate, and agents self-select and de-select the constituent parts of the problematic situation in which they will participate.

14. An information-overload mitigation system which reduces the information processed by human agents and artificial intelligence agents interacting with and within a network-based, internet-scale problem solving system to a level which requisite parsimony constraints are satisfied without sacrificing the requisite variety necessary for complex-problem solution, comprising:

an information storage and retrieval system in which agent inputs are managed;

natural language processing algorithms;

said natural language processing algorithms extract key words, proper names, phases, activities, shapes, symbols, meaning, intent, context and affective content attributes from agent inputs;

said natural language processing algorithms performing pairwise, semantic comparisons between textual and audio agent inputs and identifying, tagging, sequestering, and eliminating inputs with identical content from agent inputs to the problem solving system;

said natural language processing continually summarizing the aggregate content of agent inputs and presenting the summary to agents;

graphics processing algorithms;

said graphics processing algorithms performing pairwise comparisons between graphical and video agent inputs and identifying, tagging, sequestering, and eliminating inputs with identical content from agent inputs to the problem solving system.

15. The system in claim 14, wherein the natural language processing algorithms are trained on agent-supplied background materials maintained in said information storage and access system.

16. The system in claim 14, wherein knowledge stored in the information management system is tagged with metadata terms that are displayed so that agents can select from said terms in order to request tagged knowledge with the selected metadata be incorporated into the problem-solving process at agent-selected points of application.

17. The system in claim 16, wherein agents contribute rankings of the usefulness of individual items in said information management system to the metadata associated with the individual items for the purpose of focusing agent attention on the most useful data items.

18. The system in claim 14, wherein the system uses content attributes extracted by natural processing algorithms to determine points of application within the said problem solving system for a stored information item and directs the information to that point of application by reference or by full display of the information item at the point of application.

19. The system in claim 14, wherein metadata identified by said natural language processing algorithms and common features identified by said graphical processing algorithms are used as search terms in semantic searches of content available over said network for the purpose of referencing material related to the problem solving processing or for retrieving the material for storage in said information management system.

20. The system in claim 19, wherein the system creates a network display of material identified by means of semantic search that depicts network relationships by location between the search-discovered information items.