Logist^TM is a rule-based expert system with a data-driven inference engine that implements the "forward chaining" inference method.

Based on artificial intelligence technology, Logist^TM mimics the behavior of human experts who acquire information from a source (data) and apply their accumulated knowledge (rules) to it. Logist^TM reaches conclusions in the form of operational tasks or recommendations to the users who question them.

The process whereby knowledge is applied to data to reach a conclusion is called inference. The software component used for this process is accordingly referred to as an inference engine. In addition to the inference engine, Logist^TM incorporates software components for generating information that explains the rationale behind the conclusions it has reached. These components constitute the explanation mechanism.

During the inference process, a Logist^TM application that operates in an interactive environment may issue requests for additional information or clarifications about the goals of the process or it may "consult" with the user before reaching its conclusions.


As an expert system, Logist^TM's structure comprises three components:


Knowledge Base
The knowledge and logic are stored separately from the programs, in a special type of database called a knowledge base. The knowledge base allows a business analyst to define the business requirements - policies, procedures and practices - of the organization, using Logist^TM's rich and, expressive rule language. With the rule language, requirements can be expressed in plain language using business vocabulary and business concepts specific to the line of business.

Examples of rule language:

• If annual maintenance cost is more than 50% of purchase price, recommend ordering the item.
• If the customer is in the diamond industry, his sales are higher than industry average, he did not exceed his credit limit during the past year, and the company's owners provided personal guarantees, then approve a credit increase of 20% based on company owners signing personal guarantees.

Knowledge Engine
At the heart of Logist^TM lays a knowledge engine that processes the rules stored in the knowledge base. This process is similar to the analysis performed by a human expert while reaching a conclusion.


Decision Support and Knowledge Navigation
Logist^TM's runtime functionality facilitates thorough research of the decision process, recommendations, evaluations, reasoning and conclusions. "What-if" tools further expand Logist^TM's decision support capabilities.

At run time, all the relevant information from all the available sources is presented to the decision-maker. A data form displays the information in the format and layout customized to the specific requirements and practices of the organization. Logist^TM highlights the items, problems and recommendations using a customized color scheme; for example, red indicates a problem that needs urgent attention, yellow is warning; green is a positive action item. The information presented in the data form contains data from different databases and various processing systems, including conclusions and terms maintained by the Logist^TM inference engine (knowledge engine).

By pointing to any field in the data form, the decision-maker can view the knowledge that exists behind that field. By pointing to any field, term or conclusion in a rule, the decision-maker can explore the chain of deductions that led to the recommendation. The decision-maker navigates and researches the knowledge base to achieve a thorough understanding of each situation.



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