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What’s An Artificial Intelligence Production System? Here Are The Basics

What’s An Artificial Intelligence Production System? Here Are The Basics


Production systems can be defined as a kind of cognitive architecture, in which knowledge is represented in the form of rules. So, a  system that uses this form of knowledge representation is called a production system. To simply put, production systems consists of rules and factors. Knowledge is usually encoded in a declarative from which comprises of a set of rules of the form.

The Major Components Of An AI Production System

  • A global database
  • A set of production rules
  • A control system

The global database is the central data structure which used by an AI production system. The production system. The production rules operate on the global database. Each rule usually has a precondition that is either satisfied or not by the global database. If the precondition is satisfied, the rule is usually be applied. Application of the rule changes the database. The control system then chooses which applicable rule should be applied and ceases computation when a termination condition on the database is satisfied. If multiple rules are to fire at the same time, the control system resolves the conflicts.



4 Major Features Of Production System

  • Simplicity
  • Modularity
  • Modifiability
  • Knowledge Intensive

Advantages Of Production Systems In AI

  • Provides excellent tools for structuring AI programs
  • The system is highly modular because individual rules can be added, removed or modified independently
  • Expressed in natural form.
  • Separation of knowledge and Control – Recognises Act Cycle
  • A natural mapping onto state space research – data or goal-driven
  • Modularity of production rules
  • The system uses pattern directed control which is more flexible than algorithmic control
  • Provides opportunities for heuristic control of search
  • Tracing and Explanation – Simple Control, Informative rules
  • Language Independence
  • A plausible model of human problem solving -SOAR, ACT
  • A good way to model the state-driven nature of intelligent machines
  • Quite helpful in real time in environment and applications.

Disadvantages Of Production Systems In AI

  • It’s very difficult to analyse the flow of control within a production system
  • It describes the operations that can be performed in a search for a solution to the problem. They can be classified as follows.
  • There is an absence of learning due to a rule-based production system which does not store the result of the problem for future use.
  • The rules in the production system should not have any type of conflict resolution as when a new rule is added to the database it should ensure that it does not have any conflict with any existing rules.

4 Major Classifications Of Production Systems

Production system describes the operations that can be performed in a search for a solution to the problem. These are 4 classifications 

See Also

  1. A monotonic production system
  2. A non-monotonic production system
  3. A partially commutative production system
  4. A commutative production system.
  • Monotonic Production System: It’s a production system in which the application of a rule never prevents the later application of another rule, that could have also been applied at the time the first rule was selected.
  • Partially Commutative Production System: It’s a type of production system in which the application of a sequence of rules transforms state X into state Y, then any permutation of those rules that is allowable also transforms state x into state Y. Theorem proving falls under the monotonic partially communicative system.
  1. Blocks world and 8 puzzle problems like chemical analysis and synthesis come under monotonic, not partially commutative systems.
  2. Although, playing the game of bridge comes under non-monotonic, not partially commutative system. For any problem, several production systems do exist. Some will be efficient than others.
  • Non-Monotonic Production Systems are useful for solving ignorable problems. These systems are important for man implementation standpoint because they can be implemented without the ability to backtrack to previous states when it is discovered that an incorrect path was followed. This production system increases the efficiency since it is not necessary to keep track of the changes made in the search process.
  • Commutative Systems are usually useful for problems in which changes occur but can be reversed and in which the order of operation is not critical for example the, 8 puzzle problem. Production systems that are not usually not partially commutative are useful for many problems in which irreversible changes occur, such as chemical analysis. When dealing with such systems, the order in which operations are performed is very important and hence correct decisions must be made at the first time itself.

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