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Production system (computer science)
A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior, but also includes the mechanism necessary to follow those rules as the system responds to states of the world.[citation needed] Those rules, termed productions, are a basic knowledge representation found useful in automated planning and scheduling, expert systems, and action selection.
Productions consist of two parts: a sensory precondition (or "IF" statement) and an action ("THEN"). If a production's precondition matches the current state of the world, then the production is said to be triggered. If a production's action is executed, it has fired. A production system also contains a database, sometimes called working memory, which maintains data about the current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.[citation needed]
Rule interpreters generally execute a forward chaining algorithm for selecting productions to execute to meet current goals, which can include updating the system's data or beliefs. The condition portion of each rule (left-hand side or LHS) is tested against the current state of the working memory.
In idealized or data-oriented production systems, there is an assumption that any triggered conditions should be executed: the consequent actions (right-hand side or RHS) will update the agent's knowledge, removing or adding data to the working memory. The system stops processing either when the user interrupts the forward chaining loop; when a given number of cycles have been performed; when a "halt" RHS is executed, or when no rules have LHSs that are true.
Real-time and expert systems, in contrast, often have to choose between mutually exclusive productions—since actions take time, only one action can be taken, or (in the case of an expert system) recommended. In such systems, the rule interpreter, or inference engine, cycles through two steps: matching production rules against the database, followed by selecting which of the matched rules to apply and executing the selected actions.
Production systems may vary on the expressive power of conditions in production rules. Accordingly, the pattern matching algorithm that collects production rules with matched conditions may range from the naive—trying all rules in sequence, stopping at the first match—to the optimized, in which rules are "compiled" into a network of inter-related conditions.
The latter is illustrated by the Rete algorithm, designed by Charles L. Forgy in1974, which is used in a series of production systems, called OPS and originally developed at Carnegie Mellon University culminating in OPS5 in the early 1980s. OPS5 may be viewed as a full-fledged programming language for production system programming.
Production systems may also differ in the final selection of production rules to execute, or fire. The collection of rules resulting from the previous matching algorithm is called the conflict set , and the selection process is also called a conflict resolution strategy.
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Production system (computer science)
A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior, but also includes the mechanism necessary to follow those rules as the system responds to states of the world.[citation needed] Those rules, termed productions, are a basic knowledge representation found useful in automated planning and scheduling, expert systems, and action selection.
Productions consist of two parts: a sensory precondition (or "IF" statement) and an action ("THEN"). If a production's precondition matches the current state of the world, then the production is said to be triggered. If a production's action is executed, it has fired. A production system also contains a database, sometimes called working memory, which maintains data about the current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.[citation needed]
Rule interpreters generally execute a forward chaining algorithm for selecting productions to execute to meet current goals, which can include updating the system's data or beliefs. The condition portion of each rule (left-hand side or LHS) is tested against the current state of the working memory.
In idealized or data-oriented production systems, there is an assumption that any triggered conditions should be executed: the consequent actions (right-hand side or RHS) will update the agent's knowledge, removing or adding data to the working memory. The system stops processing either when the user interrupts the forward chaining loop; when a given number of cycles have been performed; when a "halt" RHS is executed, or when no rules have LHSs that are true.
Real-time and expert systems, in contrast, often have to choose between mutually exclusive productions—since actions take time, only one action can be taken, or (in the case of an expert system) recommended. In such systems, the rule interpreter, or inference engine, cycles through two steps: matching production rules against the database, followed by selecting which of the matched rules to apply and executing the selected actions.
Production systems may vary on the expressive power of conditions in production rules. Accordingly, the pattern matching algorithm that collects production rules with matched conditions may range from the naive—trying all rules in sequence, stopping at the first match—to the optimized, in which rules are "compiled" into a network of inter-related conditions.
The latter is illustrated by the Rete algorithm, designed by Charles L. Forgy in1974, which is used in a series of production systems, called OPS and originally developed at Carnegie Mellon University culminating in OPS5 in the early 1980s. OPS5 may be viewed as a full-fledged programming language for production system programming.
Production systems may also differ in the final selection of production rules to execute, or fire. The collection of rules resulting from the previous matching algorithm is called the conflict set , and the selection process is also called a conflict resolution strategy.