It's a common misconception that the automobile industry is slow to adapt new technologies, such as AI, into the manufacturing sector. In reality, many early adaptations of AI were in the automotive sector, where such diverse technologies as expert systems, neural networks, genetic algorithms, and fuzzy logic were among the first to be used. Ford Motor Company is applying AI and knowledge-based technologies within its manufacturing arena, including an AI-based approach for vehicle assembly process planning, an application of AI for ergonomics analysis, and a system that uses machine translation to translate assembly-build instructions for assembly plants that don't use English as their primary language. Furthermore, specific technologies such as natural language processing, controlled languages, and ontologies can effectively deal with different types of knowledge, both structured and unstructured, prevalent in the manufacturing environment.

James A. Hendler, Edward A. Feigenbaum: Knowledge Is Power: The Semantic Web Vision.

Most legal expert systems attempt to implement complex models of legal reasoning. This book argues that a complex model is unnecessary. It advocates a simpler, pragmatic approach in which the utility of a legal expert system is evaluated by reference, not to the extent to which it simulates a lawyer's approach to a legal problem, but to the quality of its predictions and of its arguments. The author describes the development of a legal expert system, called SHYSTER, which takes a pragmatic approach to case law. He discusses the testing of SHYSTER in four different and disparate areas of case law, and draws conclusions about the advantages and limitations of this approach to legal expert system development.

Note: Archived here to protect original source article from being censored by Government. Article posted to archive.org in PDF files to protect it from Government caused destruction of information. Aaron did not personally endorse uploading this article backup to Archive.org. I feel that backups of news articles are necessary in this dsy and age, where Government can secretly take stuff down. Brian D. Hill is... Topics: Aaron Kesel, article, We Are Change, USWGO, Brian D. Hill, Alternative News, media, framed, set up,...

The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. Because one of the core competencies of Siemens is to provide such highly engineered and customized systems, ranging from solutions for medium-sized and small businesses up to huge industrial plants, the efficient implementation and maintenance of configurators are important goals for the success of many departments. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period. In particular, we highlight the main technology factors regarding knowledge representation, reasoning, and integration which were important for our achievement. Finally we describe selected key application areas where the business success vitally depends on the high productivity of configuration processes.

Whenever one separates sociology and technology, one misses the point.

We consider existential rules (aka Datalog+) as a formalism for specifying ontologies. In recent years, many classes of existential rules have been exhibited for which conjunctive query (CQ) entailment is decidable. However, most of these classes cannot express transitivity of binary relations, a frequently used modelling construct. In this paper, we address the issue of whether transitivity can be safely combined with decidable classes of existential rules. First, we prove that transitivity is incompatible with one of the simplest decidable classes, namely aGRD (acyclic graph of rule dependencies), which clarifies the landscape of `finite expansion sets' of rules. Second, we show that transitivity can be safely added to linear rules (a subclass of guarded rules, which generalizes the description logic DL-Lite-R) in the case of atomic CQs, and also for general CQs if we place a minor syntactic restriction on the rule set. This is shown by means of a novel query rewriting algorithm that is specially tailored to handle transitivity rules. Third, for the identified decidable cases, we pinpoint the combined and data complexities of query entailment.

Machine learning and data mining are research areas of computer science whose quick development is due to the advances in data analysis research, growth in the database industry and the resulting world construction market needs for methods that are capable of extracting valuable knowledge from large data stores. Here are some of the examples of machine learning being applied in the construction industry. It's important to note that machine learning techniques apply mostly on a business side, less so in the actual construction. At the same time, the construction industry is currently experiencing explosive growth in its capability to generate and collect data. Advances in data storage technology, such as faster, higher capacity, and less expensive storage devices, better database management systems, and data-warehousing technology, have allowed the transformation of an enormous amount of data into computerized database systems. These data, however, have no use until they are processed and interpreted.

Will AI make construction industry, civil engineering, and design more efficient? How will it benefit these industries? Starting from the 1980s professors and researchers from all over the world published an enormous amount of articles about use cases of artificial intelligence in the field of construction. We analyzed those articles and compiled a list of 10 most interesting examples, where AI technology used for construction performance diagnostics, intelligent planning of construction projects or creating construction robot fleet management systems. In 1994 professors Tarek Hegazy and Osama Moselhi published a technical paper, which presented a methodology for deriving analogy-based solutions to a class of unstructured problems in civil engineering.

Site-Layout Modeling: How AI Can Help Construction Industry? The efficient planning of site space through the construction project is referred to as site layout planning. Due to its impact on safety, productivity and security on construction sites, several site layout planning models have been developed in the past decades. These models have the common aim of generating best layouts considering the defined constraints and conditions. However, the underlying assumptions that were made during the development of these models seem disparate and often implicit.