XpertRule Knowledge Builder

Category Intelligent Software>Expert (Knowledge Based) Systems/Tools, Intelligent Software>Genetic Algorithm Systems/Tools and Intelligent Software>Fuzzy Logic Systems/Tools

Abstract XpertRule Knowledge Builder is an enterprise strength environment for developing and deploying knowledge-based (KB) applications and components.

Knowledge-based applications are software components which incorporate rules, expertise, know-how, procedures, policies and regulations which can collectively be called “Business Rules”.

Knowledge Builder offers integrated knowledge technologies, a comprehensive development environment and scalable deployment options; which makes it ideally suited to a wide range of knowledge-based applications.

Applications of Knowledge Builder include:

1) Making Recommendations & Advice - This class of application provides recommendations and advice on the most suitable products, services and courses of actions.

2) Troubleshooting, in Customer Support & Help Desk applications - Capturing troubleshooting and diagnostic knowledge allows an organization to effectively support internal users (such as call centre agents) and customers.

3) Risk & Condition Assessment & Monitoring - These are knowledge applications, that monitor your business transactions for patterns that represent a high risk to your business processes.

4) Workflow - A knowledge based decision making engine can use rules to decide on the next task/action in a workflow system, based on current events and available data.

5) Resource Optimization - The Genetic Algorithm (GA) optimizer within Knowledge Builder can be used to determine optimal solutions to problems that have many possible solutions, such as design, resource scheduling and planning, and component blending - in other words resource optimization.

You do Not have to tell Knowledge Builder how to solve the problem - instead you specify a method of evaluating solutions, define the constraints on resources and Knowledge Builder will evolve the best possible solution according to your criteria.

How Knowledge Builder Optimization works --

1) Randomly generate a limited number of possible solutions. Not all possible solutions, which is impractical, but a starting group - an initial generation;

2) Assess the quality of these random solutions;

3) Crossbreed solutions with more emphasis on better solutions;

4) Randomly alter (mutate) some of the solutions, to introduce variations;

5) Continue the natural selection process until good solutions evolve.

These techniques are inspired by the theory of biogenetics and evolution.

Using Genetic Algorithms (GA) for optimization --

1) Avoids the need to design a strategy to converge towards a solution. The developer only needs to design a cost or fitness function to evaluate each solution considered by the algorithm. Good solutions evolve by 'natural' selection;

2) Allows the developer to impose constraints (or weighted penalties) for parameters Not being satisfied by the solutions. This has the effect of killing off bad solutions.

Hybrid systems - Many complex applications will require both Knowledge Based Systems and Optimization to be used. The Knowledge Based System element can be used to decide on the values to be set for constraints, or to process optimized results further.

It can also be advantageous to have the Knowledge Based System ‘narrow down the universe’ being presented to the optimization process - in other words to plant the seeds for good solutions in the initial generation.

The potential of such advanced hybrid systems is unique to Knowledge Builder developers. For example, such hybrid systems allow the optimization engine to call external cost evaluation functions implemented as Dynamic Link Libraries (DLLs).

Knowledge Representation in Knowledge Builder -- Decision Making Knowledge is also now often called “Decisioning” and covers diagnostic, selection, recommendation, advice, assessment, monitoring, workflow and similar applications. Product represents “Decisioning” Knowledge using Decision Trees and Cases Tables.

A decision tree relates an outcome or decision to a number of attributes. A table of Cases contains a list of examples or rules each showing how an outcome or decision relates to a combination of attribute values.

An attribute in a decision tree, or in cases table, can itself be represented by another decision tree or cases table. This is called knowledge “chaining”.

Knowledge is executed by a “runtime” or “inference” engine. In a Decision Making application, the “runtime engine” has the task of deriving all the required decisions/outcomes. It does this by executing the knowledge in the decision trees and/or the case tables.

Case Based Reasoning can also be used in diagnostic applications, whereby the user can be asked to volunteer any number of symptoms (attributes) with the application still being able to generate a narrowed down list of diagnosis values.

Fuzzy Logic can also be used where there are applications, such as performance assessment and diagnostics, in which the human expert applies fuzzy reasoning in their decision making. The following rule is an example of such reasoning:

IF income is low AND person is young THEN credit limit is low

This rule is fuzzy because of the imprecise definitions of “income”, “young” and “credit limit”. Product allows you to implement such fuzzy reasoning which can be integrated seamlessly with crisp reasoning and with GA optimization. This leads to accurate systems using small rules sets.

Constraint Inference - enabling the unique graphical knowledge representation of Knowledge Builder to be used to define constraint rules which ensure that only valid / desirable combinations of features and options can be selected.

Hierarchy Inference - enabling hierarchical knowledge, such as a generic “bill of material” both physical and logical, to overlay selection rules.

Capturing Knowledge in Knowledge Builder --

Knowledge Structuring - Product enables highly complex knowledge applications to be structured into intuitive units of knowledge - each unit being a decision tree or a cases table - and to be able to visualize the overall structure using a knowledge Map.

Knowledge Acquisition - Product utilizes Rule Induction as a catalyst for knowledge acquisition. Rule induction can convert a table of cases (examples) into a decision tree. Rule induction reveals the generic patterns, logic gaps and conflicts in the table of cases.

Learning from Data - XpertRule Miner (see G6G Abstract Number 20032) is a dedicated software product for learning from data (Data Mining). The overall objective is to derive decision tree rules or patterns from data files. Knowledge derived from data can be exported to Knowledge Builder.

Deploying the Captured Knowledge - Once captured and tested, the decision-making knowledge captured within Knowledge Builder can be deployed in a number of platforms and configurations.

XpertRule Software announces release 9.0 of XpertRule Knowledge Builder -- February 2010 --

Release 9.0 is the most significant release of the XpertRule flagship Knowledge Builder product since Release 4.0 in 2001. It provides major enhancements on three (3) fronts:

1) Major Overhaul of the Multi-user Rules Development Environment --

2) Enhanced Ajax .NET Rules Deployment Environment --

3) New JavaScript Application Deployment Engine --

This is a unique and innovative development that allows full applications (rules, calculations, navigation and user interfaces) to be run within a browser without needing a server.

This can deliver massive scalability and performance from a single HTTP server, as the server is only hosting the JavaScript engine files, data files and CSS/HTML assets which are demand loaded to the browser client for running.

This unique technology allows a developer to deliver an intelligent presentation layer running fully within the browser.

The presentation layer can be used as part of a multi-tier architecture (presentation, transactions, services), providing functionality for rich data capture screens, complex data validation and intelligent navigation between screens, services, and transactions.

System Requirements

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Manufacturer Web Site XpertRule Knowledge Builder

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G6G Abstract Number 20031U9

G6G Manufacturer Number 103005