Kurzbeschreibung
Automated diagnosis has always been an important AI problem not only for its potential practical applications but because it exposes issues common to all automated reasoning efforts and presents real challenges to existing paradigms. Diagnosis is probably the single largest category of expert systems in use, a substantial fraction of which are concerned with the diagnosis of engineered systems and devices. This readings book is about artificial intelligence techniques for the diagnosis of engineered systems based on a general purpose model of the internal structure and behavior of the target device. These general purpose models can be constructed using standard AI technologies such as predicate logic, frames, constraints and rules. Complementing the modeling technology are algorithms for diagnosis that are also based on standard AI techniques such as theorem proving, heuristic search, qualitative simulation, and Bayes nets. The 42 papers reprinted in the volume reflect the maturation of the field and include the most seminal and frequently referenced sources. The editors have provided introductions to the papers and an annotated bibliography of over 350 works. Readings in Model-based Diagnosis will be of interest to a wide range of professionals in the AI and engineering communities concerned with the building of diagnostic systems and with understanding the technology underlying them.
Synopsis
Automated diagnosis has always been an important AI problem not only for its potential practical applications but because it exposes issues common to all automated reasoning efforts and presents real challenges to existing paradigms. Diagnosis is probably the single largest category of expert systems in use, a substantial fraction of which are concerned with the diagnosis of engineered systems and devices. This readings book is about artificial intelligence techniques for the diagnosis of engineered systems based on a general purpose model of the internal structure and behavior of the target device. These general purpose models can be constructed using standard AI technologies such as predicate logic, frames, constraints and rules. Complementing the modeling technology are algorithms for diagnosis that are also based on standard AI techniques such as theorem proving, heuristic search, qualitative simulation, and Bayes nets. The 42 papers reprinted in the volume reflect the maturation of the field and include the most seminal and frequently referenced sources. The editors have provided introductions to the papers and an annotated bibliography of over 350 works.
Readings in Model-based Diagnosis will be of interest to a wide range of professionals in the AI and engineering communities concerned with the building of diagnostic systems and with understanding the technology underlying them.
