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AIG uses a two-step process: first, a test specialist creates a template called an item model; then, a computer algorithm is developed to generate test items. [2] So, instead of a test specialist writing each individual item, computer algorithms generate families of items from a smaller set of parent item models.
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
Prolog is particularly useful for symbolic reasoning, database and language parsing applications. Artificial Intelligence Markup Language (AIML) [11] is an XML dialect [12] for use with Artificial Linguistic Internet Computer Entity (A.L.I.C.E.)-type chatterbots. Planner is a hybrid between procedural and logical languages. It gives a ...
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
Non-monotonic reasoning. Non-monotonic reasoning allows various kinds of hypothetical reasoning. The system associates facts asserted with the rules and facts used to justify them and as those facts change updates the dependent knowledge as well. In rule based systems this capability is known as a truth maintenance system. [25] Expressive ...
Java (Full Web Application including Java source, AspectJ source, XML, JSP, Spring application contexts, build tools, property files, etc.) T4: Passive T4 Template/Text File: Any text format such as XML, XAML, C# files or just plain text files. Umple: Umple, Java, Javascript, PHP Active Tier
Later symbolic AI work after the 1980's incorporated more robust approaches to open-ended domains such as probabilistic reasoning, non-monotonic reasoning, and machine learning. Currently, most AI researchers [citation needed] believe deep learning , and more likely, a synthesis of neural and symbolic approaches ( neuro-symbolic AI ), will be ...
In artificial intelligence, model-based reasoning refers to an inference method used in expert systems based on a model of the physical world. With this approach, the main focus of application development is developing the model.