Key Components of a Production System in AI

The key components of production system includes:

1. Knowledge Base: This is the core repository where all the rules and facts are stored. In AI, the knowledge base is critical as it contains the domain-specific information and the if-then rules that dictate how decisions are made or actions are taken.
2. Inference Engine: The inference engine is the mechanism that applies the rules to the known facts to derive new facts or to make decisions. It scans the rules and decides which ones are applicable based on the current facts in the working memory. It can operate in two modes:
   • Forward Chaining (Data-driven): This method starts with the available data and uses the inference rules to extract more data until a goal is reached.
   • Backward Chaining (Goal-driven): This approach starts with a list of goals and works backwards to determine what data is required to achieve those goals.
3. Working Memory: Sometimes referred to as the fact list, working memory holds the dynamic information that changes as the system operates. It represents the current state of knowledge, including facts that are initially known and those that are deduced throughout the operation of the system.
4. Control Mechanism: This governs the order in which rules are applied by the inference engine and manages the flow of the process. It ensures that the system responds appropriately to changes in the working memory and applies rules effectively to reach conclusions or solutions.

Every automatic system with a specific algorithm must have rules for its proper functioning and functioning differently. The production systems in artificial intelligence are rules applied to different behaviors and environments.

In this article, we will learn about production systems, their components, and production system rules.