Initiated as the avant-garde system under artificial intelligence (AI), expert systems have a history and business case worth exploring. The knowledge-driven systems are popular for paralleling human intelligence by using fixed rules and data. However, the role of expert systems in artificial intelligence is well beyond pre-defined outputs, expanding to recommendations, interactions, and predictions.
This blog post sheds light on the powerful duo of artificial intelligence services and expert systems to build effective business applications.
Understanding Expert Systems in Artificial Intelligence
Formally introduced in the 1960s, expert systems are computer programs that can emulate human intelligence and decision-making abilities using a rule-based approach. The technique is closely related to artificial intelligence for it enables machines to demonstrate human-like thinking capabilities through human knowledge.
Edward Feigenbaum is often termed as “the father of expert systems.”
To decode an AI expert system, let’s first understand the core of AI.
Under the vast umbrella of computer science, AI systems and techniques can be broadly divided into two categories, namely ‘Symbolic’ and ‘Subsymbolic’. While symbolic systems work explicitly on human knowledge in the form of symbols or rules, subsymbolic systems work partially on data and neural network-based calculations.
Expert systems are a type of symbolic AI as they completely rely on knowledge-base enveloping facts, data, and ‘if-this-then-that’ rules.
For this reason, AI expert systems are directed towards solving complex reasoning problems while exhibiting human-level intelligence and expertise. Unlike machine learning development that encompasses extensive model training, AI expert systems are programmed with comprehensive rules and domain-specific data to fetch pre-defined outputs.
It is, therefore, easier for businesses to build and deploy AI expert systems entailing the following advantages-
a) Easy development process
b) Low production costs
c) High efficiency with accurate knowledge-base
d) Minimum errors and risk involved, and
e) Steady response rate.
However, to build expert systems, it is essential for businesses to pre-determine the characteristics of the problem and provide a rich knowledge base.
Structure of Experts Systems in Artificial Intelligence
There are 3 main components of expert systems in artificial intelligence, namely-
1) Knowledge base
The knowledge base represents the information warehouse of expert systems, comprising of all the stored information acquired from human experts and other sources. In addition, the knowledge base envelopes a long list of rules, facts, and figures that can be categorized into factual and heuristic knowledge.
2) Inference Engine
Acting as the brainpower of the expert system, the inference engine processes the information and applies rules to deduce an output. For both deterministic and probabilistic types of outputs, inference engines use two approaches to generate output, i.e. forward chaining, and backward chaining.
Learn more: Expert Systems in Artificial Intelligence: Applications and Opportunities