Introduction to Production System in AI
A Production System in AI is a system program that is used to feed some form of artificial intelligence. It comprises a set of rules to design the characteristic behaviour and involves a mechanism to obey the rules of the system and respond accordingly. That set of rules is referred to as production, and it is a fundamental representation for action selection, expert system, and automated planning. The upcoming section of the articles explains features, generated rules, advantages, and limitations of the production system in artificial intelligence.
Features of the Production System in AI
The fundamental components of the production system in artificial intelligence includes a global database, a set of production rules, and a control system. The central data structure is provided by a global database that is utilized for the operation of the production system as the global database provides a set of predefined rules. If the defined precondition is accepted, then the rule is executed. But the implementation of rule changes or updates the database. The applicable rule is selected by the control system and continues for further computation until terminating rule on the database is accepted. The control system has the ability to manage conflicts if multiple rules are executed simultaneously.
The architecture of every sentence in a production system is uniform and simple. The entire system is unique, as they execute IF-THEN code in every set of executions. It is a source of knowledge representations and increases the readability of production rules. Hence it is user-friendly and can be managed without any complexities and difficulties as they are less prone to challenging tasks.
The code of the production rule and its related knowledge are available in distinct units. So that the information can be accessed without any dependencies, it is an array of independent facts that can be edited easily which has no reflections in the production system. The modularity of the production system possesses a set of finite dimensions that are easily flexible to any modifications in the system.
The adaptability for editing or altering the rules is easy and enables the enhancement of production rules in a skeletal format and then selects a concern application that is perfect and accurate to execute without any delay or imperfections.
The knowledge base of the production system is intensive and doesn’t find any corrupted data or false information. The data is stored in a pure format and doesn’t comprise of any controlling strategy or programming information. The production rule is stated in a simple sentence in English. The semantic problem is rectified by every part of the representation.
Rules of Production System in AI
The rules in the production system fall into two broad categories, such as abductive inference rules and deductive inference rules. The representation of rules in the production system is an important part of the functions of the entire system is dependent on rules. The rules are fed into the operation of database and control system and can be written as follows,
It is based on the IF-THEN condition.
If (condition) then (condition):
It is also called as antecedent-consequent, pair of feedback and results, response to condition and actions, an act to pattern and action, condition to situation, and response.
Advantages and Disadvantages of the Production System
Below are mentioned some of the advantages and disadvantages:
- The representation of rules in the production system is natural and expressed in a simple format. It has a rapid response to the action cycle, which can recognize and react according to the separation of control and knowledge. The data or goal-driven is a natural mapping which is onto research on state space.
- The modularity and adaptability of the production rules are efficient and user-friendly. The flexibility to any modification in the rules is high without affecting the production system.
- The production system executes pattern directed control which is more adaptable than algorithmized control. It enables the exploratory control of search in a hierarchical way if any complexities occur.
- The troubleshooting methods in the production system are reliable, and it takes minimum time to find the affected parts and provides simple tracing of the systems. It provides a generic control and informative rules to manage the challenging tasks.
- It is a reliable model because of the state-driven attitude of the intelligent machines and behaves as a reasonable design to the decision making and problem-solving act of humans. It is robust and provides a rapid response in real-time applications.
- Apart from this, the significant features of the production system include incompetence, opaqueness, lack of learning ability, and resolution of conflicts.
- The occurrence of opaqueness is due to the less prioritization of rules. It is executed when there is any merging or combination of two or more production rules. If the priority of rule is predetermined, then the probability of opaqueness is less.
- Most of the production systems are prone to incompetence in the applied environment. But well-assembled control methodologies minimize this kind of problem, especially when a program is executed, multiple rules became active and executed. It happens because there are many predefined rules in the production system, and a complex search is carried in the hierarchical method throughout every set of rules for every iteration of a control program.
- The production system that depends on the rules doesn’t store the outcomes of the problem, which helps to solve any future issues. Instead, it goes for every new solution for the same particular problems and doesn’t exhibit any kind of learning capacities. Hence the lack of learning capabilities in the production system in artificial intelligence needs to be improvised for better efficacy and operation.
- The rules in the production system should not get involved in any conflict operations. If the database is updated with new rules, the system should check that there should not be any conflicts executed between the existing rules and newly updated rules.
The significant features of the production system include user-friendly attitude, modularity, adaptability and flexibility, and enriched knowledge which can be applied in a real-time environment without any discrepancies.
This is a guide to the Production System in AI. Here we discuss an introduction to the Production System in AI along with the features, rules, advantages, and disadvantages. You can also go through our other related articles to learn more –