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 it also includes the mechanism necessary to follow those rules as the system responds to states of the world. Those rules, termed productions, are a basic representation found useful in automated planning, expert systems and action selection.
Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "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 is said to have fired. A production system also contains a database, sometimes called working memory, which maintains data about current state or knowledge, and a rule interpreter. The rule interpreter must provide a mechanism for prioritizing productions when more than one is triggered.
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 has 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.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
A business rules engine is a software system that executes one or more business rules in a runtime production environment. The rules might come from legal regulation ("An employee can be fired for any reason or no reason but not for an illegal reason"), company policy ("All customers that spend more than $100 at one time will receive a 10% discount"), or other sources. A business rule system enables these company policies and other operational decisions to be defined, tested, executed and maintained separately from application code.
The Rete algorithm (ˈriːtiː , ˈreɪtiː , rarely ˈriːt , rɛˈteɪ ) is a pattern matching algorithm for implementing rule-based systems. The algorithm was developed to efficiently apply many rules or patterns to many objects, or facts, in a knowledge base. It is used to determine which of the system's rules should fire based on its data store, its facts. The Rete algorithm was designed by Charles L. Forgy of Carnegie Mellon University, first published in a working paper in 1974, and later elaborated in his 1979 Ph.
Forward chaining (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems. The opposite of forward chaining is backward chaining. Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached.
Defining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and be ...
We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate ...
2019
Architects are increasingly using digital tools during the design process, particularly as they approach complex problems such as designing for successful daylighting performance. However, while simulation tools may provide the designer with valuable infor ...