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Introduction, to, patent pending, CAMBO a multi-Expert System ...

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Introduction, to, patent pending, CAMBO a multi-Expert System ... Introduction, to, patent pending, CAMBO a multi-Expert System ... Document Transcript

  • Introduction, to, patent pending, CAMBO a multi-Expert System Generator and a new paradigm, knowledge normalization.<br />" Any entity that can be described in " English Grammatical Sentences" can be extracted and software (Rule processing) managed through a Normalized Knowledge Base." Nicholas Zendelbach.<br />Unlike Single EXPERT system generators (AION, Eclipse, XpertRule, RuleBook..) CAMBO’s kernel logic; ‘Knowledge Normalization Methodology’, is based upon a methodology that closely observes the rules of medical science in identifying, applying and developing the science of machine intelligence.<br /> Abstract of the Disclosure. <br />The invention's name is CAMBO an acronym for Computer Aided Management By Objective. The title is a " multi-EXPERT System Generator" , and the vision an “artificial intelligent bridge” between technology and the ability to automate the instruments of the MBO methodology, namely: Charters, Organization Charts, Operational Plans, Project Management, Performance Planning and others all containing the knowledge, expressed in 'English Grammatical Sentences', upon which an enterprise conducts business. It would require the design of a unique combination of advanced methodology and technology capabilities built upon and work in concert with current state of the art, 'Data Normalized', Relational Data Base structure. The “AI Bridge” would include an advanced methodology for Normalizing Knowledge, a unique definition for a unit or element of knowledge, an advanced structure for a Spatial Relational Knowledge Base and a 5th generation programming language to support a Natural Language Processing interface.<br />The value in this invention is four fold.<br />Employee Communication.<br />•· First, the ability to: Identify, Capture, Codify and Relate 'knowledge', in a Natural Language opens the door for one person to access the combined talents, skills and experiential knowledge of many human experts.<br />•· Second, planning: project management, scheduling resources, organizing resources and directing resources will be predicated upon knowledge relationships in concert with data relationships. The kernel logic here is simply that when a computer executes knowledge, in the form of rule sets, the results are presented as data, which in turn gives direction to those rule sets requiring change. <br />•· Third, the development of systems based upon the new foundation of 'Relational Knowledge' in a Natural Language interface will reach beyond the business enterprise arena to all forms of human endeavor. An example is medical research, for which growth and direction is guided by the results produced from experiments. The ability to: Identify, Capture, Codify and relate these results will exponentially increase the ability to discover, translate and apply treatment modalities.<br />     In the field of engineering (IEEE) the knowledge of senior engineers can be passed onto junior engineers, as it would apply with all fields of human endeavor, knowledge need not be forgotten or lost. <br />Fourth, the installation of knowledge based systems in differing human endeavors will have the effect of unifying human experiential knowledge and providing the ability to cross reference a more cognitive and meaningful view of the subject matter.<br />Heuristic Life Cycle of a multi-Expert System.. The heuristic life cycle is divided under the four prime domains of knowledge as governed by the CAMBO process management system, BCL (Business Conduct Logic), A derivative of CAMBOs kernel logic knowledge domains: Philosophy, Science, Engineering and Art.<br />     The Four Prime Domains of Knowledge, as described in the methodology for multi-expert system generation, are:<br /> Accept, Plan, Develop and Install. <br /> 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.<br />     The four Prime domains of knowledge are:<br />
    • 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 elements (English grammatical sentences) of the decision making activity rule set.
    2. _Plan (Knowledge Engineering): Knowledge engineers construct models of enterprise language relationships to normalize knowledge at the enterprises operational level.<br />3. _Develop (Knowledge Codification): Knowledge engineers, using the 5th generation programming language LIPS1, codify normalized knowledge into a relational knowledge base/warehouse.<br />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.<br /> 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.<br /> Knowledge Normalization = Natural Language Processing<br />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.<br />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.<br />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.<br />     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 or processing a credit card exchange of funds, 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. <br />Whenever the process identifies a dependency between any enterprises 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.<br /> 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:<br />*  Is willing to provide knowledge;*  Has the time to present knowledge;*  Is capable of imparting knowledge.<br />     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.<br />Knowledge Engineering Titles and Responsibilities<br />* 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. *Path Finder: Manages enterprise modeling to determine rule set titles. *Designer: Assimilates web site and K/D base management. *Bridge Builder: Integrates all software, network and hardware configuration. <br />      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.<br />Job Descriptions:<br />* The Business Alignment knowledge engineer and the Path Finder knowledge engineer identify and title a grouping of English Grammatical Sentences as a rule set.* The Path Finder publishes the knowledge domain profile for a new knowledge rule set.* The Designer incorporates the new knowledge rule set into the enterprise knowledge base/warehouse.*The Bridge Builder designs and coordinates integration of software and hardware components. <br />The process results in a continuing flow of knowledge from human sources to a computer system.<br />The CAMBO Multi-EXPERT System Lab is viewed as an engine that when inserted into an enterprise operates on a continuing basis to record and help manage an organization's rules by which business is conducted.<br />The development of systems based upon the new foundation of 'Relational Knowledge' in a Natural Language interface will reach beyond the business enterprise arena to all forms of human endeavor. <br />‘Knowledge Engineering Storyboards Map Knowledge Pathing.’<br />CAMBO Publications<br />•The IDC Story: The First Successful AI Based Multi-Expert System in Arizona. <br />•Examine a multi-expert system generator, Rose Navigator, and an Enterprise Resource Plan to help manage the need for human engineers against the dynamics of customer expectations and orders.  www.pcai.com/web/6a72/522.11.42/TOC.htm <br />•Abstract<br />•This article introduces a new paradigm to the discipline of engineering human knowledge, one that we divide into four tenets of knowledge representation: 1. The four prime domains of knowledge.2. All human knowledge has, at its root, a language to communicate the knowledge.3. A single language sentence contains the smallest unit of knowledge, and it is possible to normalize and codify this unit of knowledge into a multi-expert computer system (Language representation). 4. A knowledge based computer system can learn as well as teach.<br />Magazine Article: <br />http://www.pcai.com/web/6t6y6t/6t6y6y.7.02/TOC.htm<br />