Bayes' theorem. A blue neon sign, showing the simple statement of Bayes's theorem In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) relates current to prior belief.
It also relates current to prior evidence. It is important in the mathematical manipulation of conditional probabilities.[1] Bayes' rule can be derived from more basic axioms of probability, specifically conditional probability. When applied, the probabilities involved in Bayes' theorem may have any of a number of probability interpretations. Intelligent agent. Simple reflex agent Intelligent agents are often described schematically as an abstract functional system similar to a computer program.
For this reason, intelligent agents are sometimes called abstract intelligent agents (AIA)[citation needed] to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Artificial intelligence. AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers.
AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI.
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