Production Computing System
A Production Computing System is a Computing System that uses computer programs to provide some form of Artificial Intelligence.
- AKA: Production Rule System, Production System.
- Context:
- It can solve a Production Task by implementing Production Algorithms.
- It consists of a set of behavioral rules (productions) that are a basic representation used in AI Systems, Expert Systems and Action Selections.
- It requires Database (working memory) and a Rule Interpreter.
- It can range from being a Software-based Production System to being a Physical Production System.
- It can be preceded by a Production-Ready System.
- …
- Example(s):
- Counter-Example(s):
- See: Sensory Precondition, Production Rule, Production Rule-based Model, Shadow Testing in Production, RETE Match Algorithm, TREAT Match Algorithm, DADO Machine.
References
2014a
- (Melli, 2014) ⇒ Gabor Melli. (2014). “Shallow Semantic Parsing of Product Offering Titles (for Better Automatic Hyperlink Insertion).” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623343
- QUOTE: With billions of database-generated pages on the Web where consumers can readily add priced product offerings to their virtual shopping cart, several opportunities will become possible once we can automatically recognize what exactly is being offered for sale on each page. We present a case study of a deployed data-driven system that first chunks individual titles into semantically classified sub-segments, and then uses this information to improve a hyperlink insertion.
To accomplish this process, we propose an annotation structure that is general enough to apply to offering titles from most e-commerce industries while also being specific enough to identify useful semantics about each offer. To automate the parsing task we apply the best-practices approach of training a supervised conditional random fields model and discover that creating separate prediction models for some of the industries along with the use of model-ensembles achieves the best performance to date.
- QUOTE: With billions of database-generated pages on the Web where consumers can readily add priced product offerings to their virtual shopping cart, several opportunities will become possible once we can automatically recognize what exactly is being offered for sale on each page. We present a case study of a deployed data-driven system that first chunks individual titles into semantically classified sub-segments, and then uses this information to improve a hyperlink insertion.
2014b
- (Miranker, 1990) ⇒ Daniel P. Miranker. (1990). "TREAT: A New and Efficient Match Algorithm for AI Production Systems". Morgan Kaufmann Publishers Inc.. ISBN:0-934613-71-0
- BOOK OVERVIEW: TREAT: A New and Efficient Match Algorithm for AI Production Systems describes the architecture and software systems embodying the DADO machine, a parallel tree-structured computer designed to provide significant performance improvements over serial computers of comparable hardware complexity in the execution of large expert systems implemented in production system form.
This book focuses on TREAT as a match algorithm for executing production systems that is presented and comparatively analyzed with the RETE match algorithm. TREAT, originally designed specifically for the DADO machine architecture, handles efficiently both temporally redundant and non-temporally redundant production system programs.
This publication is suitable for developers and specialists interested in match algorithms for AI production systems.
- BOOK OVERVIEW: TREAT: A New and Efficient Match Algorithm for AI Production Systems describes the architecture and software systems embodying the DADO machine, a parallel tree-structured computer designed to provide significant performance improvements over serial computers of comparable hardware complexity in the execution of large expert systems implemented in production system form.
2009
- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Production_system
- 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. These rules, termed productions, are a basic representation found useful in AI planning, expert systems and action selection. A production system provides the mechanism necessary to execute productions in order to achieve some goal for the system.
- 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.
1988
- (Ohno, 1988) ⇒ Taiichi Ohno. (1988). “Toyota Production System: Beyond Large-scale Production. .” CRC Press. ISBN:0-915299-14-3
- BOOK OVERVIEW: In this classic text, Taiichi Ohno - inventor of the Toyota Production System and Lean manufacturing - shares the genius that sets him apart as one of the most disciplined and creative thinkers of our time. Combining his candid insights with a rigorous analysis of Toyota's attempts at Lean production, Ohno's book explains how Lean principles can improve any production endeavor. A historical and philosophical description of just-in-time and Lean manufacturing, this work is a must read for all students of human progress. On a more practical level, it continues to provide inspiration and instruction for those seeking to improve efficiency through the elimination of waste.