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The XML dialect called AIML was developed by Richard Wallace and a worldwide free software community between 1995 [citation needed] and 2002. AIML formed the basis for what was initially a highly extended Eliza called "A.L.I.C.E." ("Artificial Linguistic Internet Computer Entity"), which won the annual Loebner Prize Competition in Artificial Intelligence [3] three times, and was also the ...
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 procedural interpretation to logical sentences where implications are interpreted with pattern-directed ...
AIML is a research institute focused on artificial intelligence and machine learning based at Lot Fourteen on North Terrace in Adelaide city centre.It has more than 160 members, and is the largest university-based research site dedicated to machine learning in Australia, as well as ranking among the global top sites for its computer vision research capability.
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Pandorabots implements and supports development of the Artificial Intelligence Markup Language [2] and makes portions of its code accessible for free. [ 3 ] [ 4 ] The Pandorabots Platform is "one of the oldest and largest chatbot hosting services in the world", [ 5 ] allowing creation of virtual agents to hold human-like text or voice chats ...
MIT OpenCourseWare (MIT OCW) is an initiative of the Massachusetts Institute of Technology (MIT) to publish all of the educational materials from its undergraduate- and graduate-level courses online, freely and openly available to anyone, anywhere.
OpenCourseWare (OCW) are course lessons created at universities and published for free via the Internet.OCW projects first appeared in the late 1990s, and after gaining traction in Europe and then the United States have become a worldwide means of delivering educational content.
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 ...