In order to tackle non-toy problems, AI researchers such as Ed Feigenbaum and Frederick Hayes-Roth realized that it was necessary to focus systems on more constrained problems. However, the amorphous problem definitions for systems such as GPS meant that they worked only for very constrained toy domains (e.g.
In these early days of AI, general search algorithms such as A* were also developed. It would then decompose that goal into sub-goals and then set out to construct strategies that could accomplish each subgoal. These systems featured data structures for planning and decomposition. The earliest work in computerized knowledge representation was focused on general problem-solvers such as the General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.Įxamples of knowledge representation formalisms include semantic nets, systems architecture, frames, rules, and ontologies. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning ( KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language.
0 kommentar(er)
