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  2. Knowledge representation and reasoning - Wikipedia

    en.wikipedia.org/wiki/Knowledge_representation...

    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.

  3. Means–ends analysis - Wikipedia

    en.wikipedia.org/wiki/Means–ends_analysis

    Means–ends analysis [1] (MEA) is a problem solving technique used commonly in artificial intelligence (AI) for limiting search in AI programs.. It is also a technique used at least since the 1950s as a creativity tool, most frequently mentioned in engineering books on design methods.

  4. List of programming languages for artificial intelligence

    en.wikipedia.org/wiki/List_of_programming...

    Stanford Research Institute Problem Solver (STRIPS) is a language to express automated planning problem instances. It expresses an initial state, the goal states, and a set of actions. It expresses an initial state, the goal states, and a set of actions.

  5. Trachtenberg system - Wikipedia

    en.wikipedia.org/wiki/Trachtenberg_system

    The method for general multiplication is a method to achieve multiplications with low space complexity, i.e. as few temporary results as possible to be kept in memory. . This is achieved by noting that the final digit is completely determined by multiplying the last digit of the multiplic

  6. Missionaries and cannibals problem - Wikipedia

    en.wikipedia.org/wiki/Missionaries_and_cannibals...

    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. [2] [3]

  7. DPLL algorithm - Wikipedia

    en.wikipedia.org/wiki/DPLL_algorithm

    The SAT problem is important both from theoretical and practical points of view. In complexity theory it was the first problem proved to be NP-complete, and can appear in a broad variety of applications such as model checking, automated planning and scheduling, and diagnosis in artificial intelligence.

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