Production System in AI
A production system in artificial intelligence is a kind of rule-based system intended to offer an organized method for solving problems and making decisions. This framework is especially important in the field of expert systems, where it uses a predetermined collection of facts and rules to mimic human decision-making processes.
Search structures, which are the foundation of many intelligent processes, are provided by production systems. Production systems are used in problem-solving applications that need to do several searches. They are helpful in organizing AI programs so that the search process may be described and carried out more easily.
Components of Production System in AI
1. Global Database:
Also referred to as the global database, this is a short-term data storage space that contains information about the current status of the issue or circumstance that the system is analyzing.
2. Production Rules
Production Rules: These are a set of guidelines that contain knowledge particular to a given domain. Generally, rules are expressed as "IF condition and THEN action". The global database is subject to these regulations. The associated rule may only be applied to the relevant state if one of the rule's prerequisites is met.
3. Control Strategy
The process of choosing rules is handled by a control strategy. It outlines the sequence in which the rules and database will be compared. Two categories of control strategies exist:
- Irrecoverable: In this case, the problem's state cannot be changed after a rule has been chosen and put into practice.
- Tentative: When a rule is chosen and used, a backup plan is put in place in case the computation needs to be done again at a later time.
Features of Production System in AI
- It offers a fantastic example of how to solve basic human problems while keeping to social standards.
- It offers a great tool for organizing AI applications.
- It mandates that knowledge be represented uniformly.
- Because individual rules can be added, withdrawn, or modified independently, production systems are very modular.
- The expression of the production rules is organic.
Example of Production system in AI
Let's build a basic production system to distinguish between different geometric shapes kinds according to their characteristics:
Rules for Knowledge Bases:
- A shape is a circle if it has Zero sides and Zero angles.
- A form is a square if it has a Green color.
- A shape is a parallelogram if it has four sides and its opposite sides are parallel.
There are Zero sides to the form. There are Zero angles on the form. The inference engine would detect that the form is a circle, match the first rule, put it on the agenda, and fire it. This would update working memory.
Advantages of Production System in AI
The advantages of the production system in AI are:
- Because individual rules can be added, changed, or withdrawn on their own, this technique is very modular.
- When applying the production principles to solve problems, they are expressed in a natural way.
- The representation of knowledge is consistent. This indicates that employees can study and comprehend the system.
- Production systems respond to minor modifications rapidly.
Disadvantages of Production System in AI
The disadvantages of the production system in AI are:
- Determining the path through the rules and database elements that the system will select to look at is difficult.
- Large rule bases can be difficult to search, and efforts to improve this process' efficiency are still ongoing.
- The mix of production rules produces this issue. The opacity arises from a lower ranking of the regulations. A regulation with greater priority has less complexity.
