PRODUCTION SYSTEM AND ITS CHARACTERISTICS
The production system is a model of computation that can be applied to implement search algorithms and model human problem solving. Such problem solving knowledge can be packed up in the form of little quanta called productions. A production is a rule consisting of a situation recognition part and an action part. A production is a situation-action pair in which the left side is a list of things to watch for and the right side is a list of things to do so. When productions are used in deductive systems, the situation that trigger productions are specified combination of facts. The actions are restricted to being assertion of new facts deduced directly from the triggering combination. Production systems may be called premise conclusion pairs rather than situation action pair.
A production system consists of following components.
(a ) A set of production rules, which are of the form A®B. Each rule consists of left hand side constituent that represent the current problem state and a right hand side that represent an output state. A rule is applicable if its left hand side matches with the current problem state.
(b) A database, which contains all the appropriate information for the particular task. Some part of the database may be permanent while some part of this may pertain only to the solution of the current problem.
(c) A control strategy that specifies order in which the rules will be compared to the database of rules and a way of resolving the conflicts that arise when several rules match simultaneously.
(d) A rule applier, which checks the capability of rule by matching the content state with the left hand
side of the rule and finds the appropriate rule from database of rules.
The important roles played by production systems include a powerful knowledge representation scheme. A production system not only represents knowledge but also action. It acts as a bridge between AI and expert systems. Production system provides a language in which the representation of expert knowledge is very natural. We can represent knowledge in a production system as a set of rules of the form
If (condition) THEN (condition)
along with a control system and a database. The control system serves as a rule interpreter and sequencer. The database acts as a context buffer, which records the conditions evaluated by the rules and information on which the rules act. The production rules are also known as condition – action, antecedent – consequent, pattern – action, situation – response, feedback – result pairs.
If (you have an exam tomorrow)
THEN (study the whole night)
The production system can be classified as monotonic, non-monotonic, partially commutative and commutative.
Figure Architecture of Production System
Features of Production System
Some of the main features of production system are:
Expressiveness and intuitiveness: In real world, many times situation comes like “i f this happen-you
will do that”, “if this is so-then this should happ en” and many more. The production rules essentially tell
us what to do in a given situation.
1. Simplicity: The structure of each sentence in a production system is unique and uniform as they use “IF-THEN” structure. This structure provides simpli city in knowledge representation. This feature of production system improves the readability of production rules.
2. Modularity: This means production rule code the knowledge available in discrete pieces. Information can be treated as a collection of independent facts which may be added or deleted from the system with essentially no deletetious side effects.
3. Modifiability: This means the facility of modifying rules. It allows the development of production rules in a skeletal form first and then it is accurate to suit a specific application.
4. Knowledge intensive: The knowledge base of production system stores pure knowledge. This part does not contain any type of control or programming information. Each production rule is normally written as an English sentence; the problem of semantics is solved by the very structure of the representation.
Disadvantages of production system
1. Opacity: This problem is generated by the combination ofproduction rules. The opacity is generated because of less prioritization of rules. More priority to a rule has the less opacity.
2. Inefficiency: During execution of a program several rules may active. A well devised control strategy reduces this problem. As the rules of the production system are large in number and they are hardly written in hierarchical manner, it requires some forms of complex search through all the production rules for each cycle of control program.
3. Absence of learning: Rule based production systems do not store the result of the problem for future use. Hence, it does not exhibit any type of learning capabilities. So for each time for a particular problem, some new solutions may come.
4. Conflict resolution: The rules in a production system should not have any type of conflict operations. When a new rule is added to a database, it should ensure that it does not have any conflicts with the existing rules.
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