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One particular motivating example is the use of commitment schemes in zero-knowledge proofs.Commitments are used in zero-knowledge proofs for two main purposes: first, to allow the prover to participate in "cut and choose" proofs where the verifier will be presented with a choice of what to learn, and the prover will reveal only what corresponds to the verifier's choice.
One well-known example of a zero-knowledge proof is the "Where's Waldo" example. In this example, the prover wants to prove to the verifier that they know where Waldo is on a page in a Where's Waldo? book, without revealing his location to the verifier. [9] The prover starts by taking a large black board with a small hole in it, the size of Waldo.
Key processes in the field of knowledge management are knowledge creation, knowledge storage, knowledge sharing, and knowledge application. Knowledge creation is the first step and involves the production of new information. Knowledge storage can happen through media like books, audio recordings, film, and digital databases.
In the context of knowledge management, the closed-world assumption is used in at least two situations: (1) when the knowledge base is known to be complete (e.g., a corporate database containing records for every employee), and (2) when the knowledge base is known to be incomplete but a "best" definite answer must be derived from incomplete information.
In philosophy, Occam's razor (also spelled Ockham's razor or Ocham's razor; Latin: novacula Occami) is the problem-solving principle that recommends searching for explanations constructed with the smallest possible set of elements.
Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
The following outline is provided as an overview of and topical guide to information science: . Information science – interdisciplinary field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information. [1]
For example, in written text each symbol or letter conveys information relevant to the word it is part of, each word conveys information relevant to the phrase it is part of, each phrase conveys information relevant to the sentence it is part of, and so on until at the final step information is interpreted and becomes knowledge in a given domain.