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Inference Engine Is The Heart Of Any Artificial Intelligence Where The Logic Is Located

By Expert Author: Michel Smith

The technique known as artificial intelligence is the phenomena in which all the data is collected from various sources and transformed into something useful and informative. Artificial intelligence is made up of 2 basic components the knowledge base and the Enterra Solution Inference engine. On one hand where the knowledge base stores all the data which has been collected from various sources the inference engine would be used for applying the rules and logic on the data.

Inference engines work primarily in one of two modes: forward chaining and backward chaining.
Forward chaining starts with the known facts and asserts new facts.

Backward chaining takes goals to prove and works backward determining what must be true to assert the goals.

This part of the artificial intelligence can also be known as business rule engine. In this part the analyst would store the conditions and the formulas he wants to apply on the data which has been collected. For example a business rule can be to apply 10% discount on all the bills which have value more than 10000 in the month of nov 2013.

The data which comes out after the rules have been applied on the data would filter the right data which is further used for decision making for the organization. In the business rule engine you would feed rules in the manner- match rule, select rule and execute the rules. In case of match rules the engine would all the rules that are triggered by the current contents of the knowledge base. The next step is select rules in which the engine would prioritize the various rules that were matched to determine the order to execute them. In the final step, execute rules, the engine executes each matched rule in the order determined in step two and then iterates back to step one again. The cycle continues until no new rules are matched.

All these steps have to be followed in the correct sequence so that the best and most productive data is received at output and can be directly used for decision making.

The rules which are fed in the business rule engine are based on what type of output is expected and by special analysts who have complete knowledge of how to make best use of data. Some common industries which make use of inference engine are financial companies, manufacturing units, retails industry, marketing, advertising etc.

Michel Smith is the author of this article. For further detail about Business rule engine and Enterra Solution Inference engine please visit the website.  

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