Automation generally requires some key plugins, one is existing knowledge or know-how of whatever system is being automated.
The second, an analytic or calculative system that can take existing data and use it to churn out new actionable data.
Lastly, a decision making process that takes into account the existing knowledge base and factors that stem from it then uses that information as the basis to decide what actions to take.
Inference engines are computer programs that operates based on a rules or facts model.
Simply put, an inference engine is one part or component of the structure that makes up an automated system and it is responsible for taking verified and known facts about the subject matter from the knowledge base(another component), then use that fact or data to produce or deduce new information which will determine what action to take based on the facts provided.
Inference engines use either preexisting rules or newly formulated ones to analyze facts in order to come to a conclusion.
For example, it is common knowledge that an iPhone X is a dual camera phone. Let's say we have an inference engine that accepts the IMEI number of a phone as input.
Now, let us assume that we input the IMEI number of a iPhone X. If such knowledge exists in the connected knowledge base the engine could easily deduce that the IMEI number entered into the system belongs to a dual camera phone.
This newly inferred information can now be put into consideration by a decision making system(another component of automation) that needs such information to provide a solution, let's say in this case a list of online stores that sell iPhone camera converters that can be used to make an iPhone X appear like an 11 pro.
The instance described above is pretty much generic, however much more specific use cases exist for inference engine applications. These use cases and applications vary based on the goals of the organization.
A lot of the conclusions made by an inference engine is based on the conditional statements
The meaning of term
if-then in itself should be self explanatory enough in layman terms, it is as straight-forward as thinking
if something then something, if a certain expression is made then something relating to that expression happens.
In reality though,
if-then conditionals have layers of complexities that have to do with operators usually associated with the expressions being used as the conditions.
Such operators like logical operators
or, all arithmetic operators(+, -, >, <= e.t.c) e.t.c have effects on the flow of the conditional statements made and results gotten from the expressions.
Within these complexities lie the main sauce of inference engines, the ability to use predefined sets of rules and data to generate new sets of rules and data based on the existing knowledge and conditions.
Forward and Backward Chaining
Inference engines generally employ either of the below mentioned strategies to arrive at strategic conclusions.
- Forward chaining
- Backward chaining
These strategies are different in their individual approaches but both are aimed towards the same end.
Forward chaining applies a bottom-up approach where it is fed with raw data about an instance and it then uses that data to deduce more data using the already laid out conditions.
Backward chaining on the other hand uses a top-down style where a result is supplied and that result or goal is analyzed using the knowledge base to determine the process that led to that result.
Both approaches have their own pros and cons and have varying degrees of efficiencies based on their applications and scenarios which they are applied to.
Forward chaining can be easily applied to predictive scenarios while backward chaining can be applied to diagnostic situations.
Judging from all the information that shared above, I believe now we should be able to infer that inference engines are a gateway to enabling computers to make more efficient humanlike decisions.
What have you heard or read about inference engines? What do you think of them personally. Kindly share in the comments