International Cognitive Computing
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Lakeside, AZ 85929
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CAMBO Video Clips.
VIDEO CLIP SEGWAY.
- CAMBO:
Computer Aided Management By Objective. The name CAMBO was derived from the philosophy and tools of the MBO methodology.
The essence of MBO is participative goal setting, choosing course of actions and decision making. An important part of the MBO is the measurement and the comparison of the employee’s actual performance with the standards set. Ideally, when employees themselves have been involved with the goal setting and the choosing the course of action to be followed by them, they are more likely to fulfill their responsibilities.
- CAMBO:
- CAMBO HAL9000.
The CAMBO logo, HAL9000, a theatrical comparison.
Spacecraft Voyager, DEVISER.
1987-1990 Continuing development of CAMBO Multi-EXPERT system generator, engine design and knowledge normalization: methodology, techniques and tools. Jet Propulsion Labs, aerospace div., Spacecraft Voyager, on board EXPERT system, DEVISER. A mission autonomous control multi-expert system.
- Business communication between disciplines.
Business communication, the foundation to
a successful business enterprise.
Different business disciplines, all communicating in “English Grammatical Sentences”, (Sentence=Rule) having disparate meaning to similar words and focused upon processing a customer’s order.
- CAMBO STORYBOARDS.
An introduction to knowledge engineering storyboards and their role in
knowledge normalization.
Explicit Knowledge/Data Representation.
•To support storyboard generation, explicitly represent
–User description
–Task description (especially information requirements)
–Context of use, use case scenario
–Purpose of system, role of its support in mission
•To support evaluation and refinement
–Generic evaluation objectives and responses
–Specific evaluation objectives and responses
–Design refinement decisions, iterations and results
•To support subsequent analysis and design.
•To support design knowledge normalization structure.
All of the above – formatted for CAMBO: LIPS1 5GL.
Integration with Design Knowledge Capture.
•Context: Storyboarding tool has one role in overall knowledge management
•Schedule brief knowledge capture sessions for major milestones of analysis and development activities
–Reminder to explicitly address design knowledge
–Focus with “What have we learned about design in the context of this software application?”
–Bulk of the data has been captured automatically, so Knowledge Engineering sessions can be brief.
•Ensure that designers see benefits to themselves
–How can we do our jobs better on this type of design in the future?
–If new requirements arise from future use, what do we need to know about the current design decisions to support a good re-design?
–If we must redesign because of changing requirements, what design rationale will help us to make good decisions in the future?
•Export design data to specialized knowledge management tools
–Retain the repository of design knowledge
–Assist future users in finding relevant design knowledge
•Future design projects
•Redesigns for current application when new requirements arise.
- CAMBO: SYBASE / POWERBUILDER.
A review of a CAMBO: SYBASE / POWERBUILDER application.
- CAMBO ORACLE Large Scale Application.
A review of CAMBO's integration with other large scale application software products.
- CAMBO Stanford University Hospital, Patient's Bed.
Designing the next generation computer sourced patient's hospital bed, is it a cash register?
Computer-based order entry is a powerful tool for enhancing patient care. A computer-based order entry (COE): 1) can be successfully deployed and adopted in resource-poor settings, 2) can be built, deployed and sustained at relatively low cost and with local resources, and 3) has a greater potential to improve patient care.
- Dr. Dick Sears, CALMA Company.
The CAMBO Inference Engine Design review.
In computer science, and specifically the branches of knowledge engineering and artificial intelligence, an inference engine is a computer program that tries to derive answers from a knowledge base. It is the "brain" that expert systems use to reason about the information in the knowledge base for the ultimate purpose of formulating new conclusions. Inference engines are considered to be a special case of reasoning engines, which can use more general methods of reasoning.
- CAMBO JPL-Voyager Spacecraft.
- Deviser -An AI planner for spacecraft operations.
Jet Propulsion Laboratory
The 'Deviser III' automated spacecraft mission planner prototype can plan complete Voyager spacecraft operational sequences consisting of over 100 data capture goals, working 10-50 times faster than a human analyst. The planner generates a loop-free network of actions and events resembling a program evaluation and review technique chart. Actions, events, and inferences are the building blocks from which plans are assembled. It is noted that, for a very large spacecraft mission plan, the backtracking through remembered decision trees entailed by this artificial intelligence system can consume megabite quantities of computer memory.
Author(s): Dr. Vere, S. A., NASA Center: Jet Propulsion Laboratory
Publication Year: 1985
- JGC Corporation, Japan.
JGC purchase of an early version of CAMBO's methodology for knowledge normalization.
Process is everything for JGC Corporation, one of the world's largest engineering designers and builders of industrial plants and refineries. It provides planning, engineering, procurement, operation, and construction services for hydrocarbon, petrochemical, and chemical plants; pharmaceutical plants; research laboratories and medical facilities; and storage terminals and pipelines. JGC also builds radioactive waste and nuclear energy facilities and communications facilities. The company is a leading builder of gas processing and LNG (liquefied natural gas) plants worldwide. Founded in 1928 as Japan Gasoline Company, JGC has worked on more than 20,000 projects in about 70 countries.
- Brain Mapping.
Developing relational associations with business activities.
Brain mapping is a set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the (human or non-human) brain resulting in maps.
- The HAL9000 Interview.
To replace HAL9000 would require 100's of skilled Human Subject Matter Experts, codifying their knowledge into a single multi-Expert System.
Heuristic Life Cycle of a multi-Expert System..
We can divide the heuristic life cycle under the four prime domains of knowledge as governed by a process management system (BCL - Business Conduct Logic), also divided under the four prime domains of knowledge.
The Four Prime Domains of Knowledge, as described in the methodology for multi-expert system generation, are Accept, Plan, Develop and Install.
Each prime domain represents a unique perspective for identifying and classifying knowledge. The design and development of the methodology divides human activity in business, science, engineering, et al, into four action categories. People accept work to do, then plan how to best perform the work, develop or perform the work and then install the results or hand them off to another person’s accept activity. This division of knowledge, the beginning of performing knowledge normalization, examines its relational characteristics and codifies it into a relational knowledge base/warehouse.
The four Prime domains of knowledge for the heuristic life cycle are:
1. Accept (Knowledge Determination): Here Subject Matter Experts (SMEs) teach the multi-expert system the knowledge (memory) they have learned from their individual subjects (or discipline). Current memory theories often state that the act of remembering turns a stored memory into something malleable that must be re-encoded. In addition, re-encoding occurs during the experience in which a person places their understanding of the world around them into practice. The actions required to perform an enterprise decision making activity define and relate the knowledge necessary to support the rule set containing the knowledge elements (English grammatical sentences) of the decision making activity.
2. Plan (Knowledge Engineering): Knowledge engineers construct models of enterprise language relationships to normalize knowledge at the enterprises operational level.
3. Develop (Knowledge Codification): Knowledge engineers, using the 5th generation programming language LIPS1, codify normalized knowledge into a relational knowledge base/warehouse.
4. Install (Knowledge Application): End users of the expert system investigate knowledge through an interactive access system. The final rule validation process occurs when the user applies stored knowledge in a real time activity. Here the user examines the relevance of the stored knowledge against current conditions and determines if changes are required to update the stored knowledge.
One can view the Heuristic Life Cycle as an engine, with its component parts working in synergy to determine, engineer, codify and apply knowledge as a teaching and learning system. It is possible to formalize the methodology to perform the normalization of knowledge under the control of the methodology for Business Conduct Logic.
Knowledge Normalization = Natural Language Processing
The following four prime domains contain the processes for normalizing knowledge. To understand the process of normalizing knowledge, remember that the methodology defines a single knowledge element as a single English grammatical sentence.
The purpose of normalization is to arrange millions of English grammatical sentences into conversational English. The methodology produces storyboards that in turn give a breakdown structure to the language used to explain how an enterprise operates. This language breakdown structure produces titles for rule sets containing up to a system limitation of 99 English grammatical sentences. Each rule set is associated with other rule sets pre-determined to represent the enterprise operational expectations.
The process of knowledge engineering produces storyboards that graphically depict the enterprise operational procedures, and define the language used to define how employees described their roles and goals and contribution to the enterprise. Different enterprise operations utilize the appropriate English language for a particular discipline of the enterprise.
For instance, Stanford University Hospital uses a different language than Bank of America, to describe the manner in which they conduct business. The differences are grammatical in terms of differentiating a transaction profile. Whether the enterprise is processing a transaction to perform open-heart surgery upon a patient or processing a credit card exchange of funds for a customer, the common denominator is language. Each enterprise expresses the manner in which they conduct business according to the language utilized in their industry or discipline. While this may appear as a chasm of knowledge association, the methodology accommodates the relationships between industries and disciplines.
Whenever the process identifies a dependency between any enterprise related activities, the knowledge engineering storyboard models identify a language relationship. This ability builds a bridge between differing enterprise related disciplines and activities. The process examines the language thread of commonality in the knowledge engineering storyboards that connect differing enterprise related activities. Language relationships are dynamic and the methodology for real time project management updates to the enterprise operation, because new knowledge on how an activity is to be handled may influence project management planning.
Human Experts.
The knowledge normalization process takes place simultaneously with the knowledge acquisition process. The knowledge engineer uses the information from the knowledge acquisition sessions to develop a good model (storyboard) of the expertise rules that the SME uses to solve problems and manage their enterprise responsibilities.
The most important branch of knowledge acquisition is knowledge elicitation. What the expert knows, and how they use their knowledge is a crucial component, however, obtaining knowledge from a human expert is difficult. Expert systems have not seen more widespread use due to the knowledge elicitation bottleneck. Expert knowledge includes domain-related facts and principles; modes of reasoning; reasoning strategies; explanations and justifications.
The knowledge elicitation (and analysis) task involves finding at least one expert in the domain who:
l Is willing to provide knowledge;
l Has the time to present knowledge;
l Is capable of imparting knowledge.
The task requires repeated interviews with the expert(s), plus task analysis, concept models, etc.
One major obstacle to knowledge elicitation: experts cannot easily describe all they know regarding their domain expertise. They do not necessarily have extensive insight into the methods they use to solve problems. Their knowledge is embedded within their mind (similar to a compiled computer program - fast and efficient, but unreadable).
Knowledge engineering models of a storyboard function to satisfy the requirements of the “Business Conduct Logic” (BCL) methodology and bridge the knowledge elicitation bottleneck.
Knowledge Engineering Titles and Responsibilities
- l Business Alignment: The knowledge manager deals with the enterprise’s knowledge requirements and the direction and detail of corporate knowledge assimilation into a multi-expert system.
l Path Finder: Manages enterprise modeling to determine rule set titles.
l Designer: Assimilates web site and K/D base management.
l Bridge Builder: Integrates all software, network and hardware configuration. - Each knowledge engineering team member also interviews SMEs and records the EGS that describe the rules for processing a particular: business, scientific, engineering, medical, government, or any other knowledge source describing how the organization conducts business.
Job Descriptions:
- l The Business Alignment knowledge engineer and the Path Finder knowledge engineer identify and title a grouping of English Grammatical Sentences as a rule set.
l The Path Finder publishes the knowledge domain profile for a new knowledge rule set.
l The Designer incorporates the new knowledge rule set into the enterprise knowledge base/warehouse.
l The Bridge Builder designs and coordinates integration of software and hardware components. - The process results in a continuing flow of knowledge from human sources to a computer system.
Carl Sagan.
The Pale Blue Dot is a photograph of planet Earth taken in 1990 by Voyager 1 from a record distance, showing it against the vastness of space. By request of Carl Sagan, NASA commanded the Voyager 1 spacecraft, having completed its primary mission and now leaving the solar system, to turn its camera around and to take a photograph of Earth. It was subsequently used by Sagan as the title of his 1994 book of the same name.
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