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Question answering systems have been extended in recent [may be outdated as of April 2023] years to encompass additional domains of knowledge [21] For example, systems have been developed to automatically answer temporal and geospatial questions, questions of definition and terminology, biographical questions, multilingual questions, and ...
A problem is informally called "AI-complete" or "AI-hard" if it is believed that in order to solve it, one would need to implement AGI, because the solution is beyond the capabilities of a purpose-specific algorithm. [44] There are many problems that have been conjectured to require general intelligence to solve as well as humans.
On the other hand, a problem is AI-Hard if and only if there is an AI-Complete problem that is polynomial time Turing-reducible to . This also gives as a consequence the existence of AI-Easy problems, that are solvable in polynomial time by a deterministic Turing machine with an oracle for some problem.
NLU has been considered an AI-hard problem. [2] There is considerable commercial interest in the field because of its application to automated reasoning, [3] machine translation, [4] question answering, [5] news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.
The missionaries and cannibals problem, and the closely related jealous husbands problem, are classic river-crossing logic puzzles. [1] The missionaries and cannibals problem is a well-known toy problem in artificial intelligence , where it was used by Saul Amarel as an example of problem representation.
Informally, an NP-complete problem is an NP problem that is at least as "tough" as any other problem in NP. NP-hard problems are those at least as hard as NP problems; i.e., all NP problems can be reduced (in polynomial time) to them. NP-hard problems need not be in NP; i.e., they need not have solutions verifiable in polynomial time.
In theoretical computer science, the closest string is an NP-hard computational problem, [1] which tries to find the geometrical center of a set of input strings. To understand the word "center", it is necessary to define a distance between two strings. Usually, this problem is studied with the Hamming distance in mind.
A cognitive walkthrough is task-specific, whereas heuristic evaluation takes a holistic view to catch problems not caught by this and other usability inspection methods. The method is rooted in the notion that users typically prefer to learn a system by using it to accomplish tasks, rather than, for example, studying a manual.