Production System in AI
In artificial intelligence (AI), a production system refers to a type of rule-based system that
is designed to provide a structured approach to problem solving and decision-making. This
framework is particularly influential in the realm of expert systems, where it simulates human
decision-making processes using a set of predefined rules and facts.
Scenario: A patient comes to a healthcare facility with the following symptoms: fever,
severe headache, sensitivity to light, and stiff neck.
Mediacal diagnosis operates in the following manner:
1. Input: A healthcare professional inputs the symptoms into MediDiagnose.
2. Processing:
MediDiagnose reviews its knowledge base for rules that match the given symptoms.
It identifies several potential conditions but recognizes a strong match for meningitis
based on the combination of symptoms.
3. Output:
The system suggests that meningitis could be a possible diagnosis and recommends
further tests to confirm, such as a lumbar puncture.
It also provides a list of other less likely conditions based on the symptoms for
comprehensive differential diagnosis.
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.
Types of Production Systems
Production systems in AI can be categorized based on how they handle and process
knowledge. This categorization includes Rule-Based Systems, Procedural Systems, and
Declarative Systems, each possessing unique characteristics and applications.
1. Rule-Based Systems
1. Explanation of Rule-Based Reasoning
Rule-based systems operate by applying a set of pre-defined rules to the given data to
deduce new information or make decisions. These rules are generally in the form of
conditional statements (if-then statements) that link conditions with actions or
outcomes.
2. Examples of Rule-Based Systems in AI
Diagnostic Systems: Like medical diagnosis systems that infer diseases from
symptoms.
Fraud Detection Systems: Used in banking and insurance, these systems analyze
transaction patterns to identify potentially fraudulent activities.
2. Procedural Systems
1. Description of Procedural Knowledge
Procedural systems utilize knowledge that describes how to perform specific tasks.
This knowledge is procedural in nature, meaning it focuses on the steps or procedures
required to achieve certain goals or results.
2. Applications of Procedural Systems
Manufacturing Control Systems: Automate production processes by detailing step-
by-step procedures to assemble parts or manage supply chains.
Interactive Voice Response (IVR) Systems: Guide users through a series of steps to
resolve issues or provide information, commonly used in customer service.
3. Declarative Systems
1. Understanding Declarative Knowledge
Declarative systems are based on facts and information about what something is,
rather than how to do something. These systems store knowledge that can be queried
to make decisions or solve problems.
2. Instances of Declarative Systems in AI
Knowledge Bases in AI Assistants: Power virtual assistants like Siri or Alexa, which
retrieve information based on user queries.
Configuration Systems: Used in product customization, where the system decides
on product specifications based on user preferences and declarative rules about
product options.
How Production Systems Function?
The operation of a production system in AI follows a cyclic pattern:
Match: The inference engine checks which rules are triggered based on the current facts
in the working memory.
Select: From the triggered rules, the system (often through the control mechanism) selects
one based on a set of criteria, such as specificity, recency, or priority.
Execute: The selected rule is executed, which typically modifies the facts in the working
memory, either by adding new facts, changing existing ones, or removing some.
