The inference engine is a protocol that runs on the basis of an efficient set of rules and procedures to acquire an appropriate and flawless solution to a problem. It applies logical rules to data present in the knowledge base and tends to obtain the most significant output or new knowledge. In other words, the inference engine is an application programming interface (API) or a processing component used to find out the most appropriate information from the gathered facts and data, apply a set of rules to it, manipulate the data, and deduce out an error-free solution. Inference engines typically work in two modes, namely, forward chaining and backward chaining. The forward chaining process initiates with the known and already existing facts, while the backward chaining process starts with the goal or the desired output. The forward chaining process establishes new facts and knowledge, while the backward chaining process is used to determine what facts must be used to achieve the desired goal.
Examples of Inference Engine
1. Rule-based Production Systems
A rule-based production system comprises three main components, namely, working memory, a set of rules, and an inference engine. The working memory of such a system is the hub to store all the data related to the problem that is to be solved. The set of rules contains the knowledge related to the solution of the problem. The main function of the inference engine is that it takes up the knowledge and information present in the set of rules and applies it to the data contained by the working memory. For this purpose, the data from working memory and the information from the set of rules is retrieved. The inference engine then feeds the combination of both to a match algorithm to generate an output known as a conflict set. The inference engine runs in an infinite loop of match, resolve, and execute until the problem is solved.
2. Artificial Intelligence
Artificial intelligence makes use of inference engines to obtain all the possible solutions for a particular problem and helps the machine choose the most appropriate solution. In general, the inference system used in artificial intelligence equipped devices and gadgets is a program used to deduce out the response to an input signal based on the data available in the knowledge base.
3. Expert Systems
Inference engine finds its prime application in expert systems. An expert system is a computer system that tends to imitate the decision-making ability of a human being. For instance, in the case of a knowledge-based expert system, the inference engine obtains the information from the knowledge base, manipulates it, obtains the solutions to the input problem, and chooses the most appropriate response.
4. Fuzzy Modelling
An inference system is generally used in fuzzy modelling. It is best suited for applications where an accurate conclusion or result is required to be extracted out of a bulk of approximated input data. There are a number of fuzzy inference engines out of which product inference engine, root sum square inference engine, max-min inference engine, max product inference engine, etc., are the most commonly used.
5. Data Science
An inference system is also used in data science to analyse data and extract useful information out of it. The data can be structured, semi-structured, or unstructured. An inference system is very helpful in getting insights into marketing and business data. It takes the customer location, product preferences, and requirements as facts or input data. The data is then processed with the help of a set of algorithms to infer a logical conclusion. This can help the businessmen improve their customer loyalty, product sales, or sometimes both.
6. Neural Networks
Inference engines are an integral part of neural networks. It enables the networks to modify the already existing graphs as well as to create new ones. The advantage of using an inference engine in modifying neural network graphs is that the contents of the original graph are not altered, but instead, a new copy of the primary graph is formed that is modified later. It also helps the developer add new layers to the network and to alter the parameters of pre-existing layers. An inference engine helps the neural network to create and modify topologies in the source code.
7. Semantic Web
Inference engines are prominently used in the semantic web. The semantic web is the systematic organisation of a mesh of data in such a way that it is easy to be interpreted by the machine. It is an extension of the pre-existing World Wide Web and expresses data in the form of a globally linked database. Adding new facts and knowledge to the pre-existing data highly depends on the algorithm and data manipulation performed by the inference engines.
8. Declarative Network
Pega platform typically makes use of an inference engine and a declarative network to perform declarative processing. It simplifies the developed application and enables independent evaluation of the dynamic properties of the network.