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In the field of artificial intelligence, an inference engine is a software component of an intelligent system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine.
Knowledge retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology.
A logic games section contained four 5-8 question "games", totaling 22-25 questions. Each game contained a scenario and a set of rules that govern the scenario, followed by questions that tested the test-taker's ability to understand and apply the rules, to draw inferences based on them.
Additionally, the term 'inference' has also been applied to the process of generating predictions from trained neural networks. In this context, an 'inference engine' refers to the system or hardware performing these operations. This type of inference is widely used in applications ranging from image recognition to natural language processing.
Inferences are kept in the middle of all categories. Inferential Theory of Learning ( ITL ) is an area of machine learning which describes inferential processes performed by learning agents. ITL has been continuously developed by Ryszard S. Michalski , starting in the 1980s.
Text inferencing describes the tacit or active process of logical induction or deduction during reading. Inferences are used to bridge current text ideas with antecedent text ideas or ideas in the reader's store of prior world knowledge. Text inferencing is an area of study within the fields of cognitive psychology and linguistics. Much of the ...
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Causal graphs can be used for communication and for inference. They are complementary to other forms of causal reasoning, for instance using causal equality notation. As communication devices, the graphs provide formal and transparent representation of the causal assumptions that researchers may wish to convey and defend.