From mboxrd@z Thu Jan 1 00:00:00 1970 X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on ip-172-31-65-14.ec2.internal X-Spam-Level: X-Spam-Status: No, score=-1.9 required=3.0 tests=BAYES_00,T_SCC_BODY_TEXT_LINE autolearn=ham autolearn_force=no version=3.4.6 Path: eternal-september.org!reader01.eternal-september.org!aioe.org!QJLXApsvkYYOaKx3c4LRTg.user.46.165.242.91.POSTED!not-for-mail From: "Dmitry A. Kazakov" Newsgroups: comp.lang.ada Subject: Re: Artificial Intelligence libraries in ADA Date: Mon, 9 Jan 2023 12:21:04 +0100 Organization: Aioe.org NNTP Server Message-ID: References: <53481976-aebc-4d6d-bb8f-aa4f63f0b01b@googlegroups.com> Mime-Version: 1.0 Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: 7bit Injection-Info: gioia.aioe.org; logging-data="34149"; posting-host="QJLXApsvkYYOaKx3c4LRTg.user.gioia.aioe.org"; mail-complaints-to="abuse@aioe.org"; User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:102.0) Gecko/20100101 Thunderbird/102.6.1 Content-Language: en-US X-Notice: Filtered by postfilter v. 0.9.2 Xref: reader01.eternal-september.org comp.lang.ada:64783 List-Id: On 2023-01-09 11:49, J-P. Rosen wrote: > It's worth noting that these libraries may not be as feature-rich or > well-maintained as some of the more popular AI libraries available in > other programming languages. However, they can still be useful for > certain projects or as a way to learn more about AI and machine learning. I am always perplexed by such questions. A decision tree is not a method of machine learning, it is just a data structure. Depending on the type (crisp, probabilistic, fuzzy) a tree is quite easy to implement in Ada. And with any existing component library or else the standard Ada library, it is almost trivial. The crux is building such a tree from the samples so that the tree would classify the input, i.e. to learn from the training set. It puzzles me what people are expecting. Initial training and maybe reinforcement training are highly dependent on the problem at hand. There is no universal solution, otherwise it would not be AI. Depending on the training method specific tree operations are used such as rotations etc which require certain ways of tree representation especially if the problem size is massive. All this is very, very specific. The bottom line. Instead of looking for some miracles, just use Ada, which is an excellent language for AI (because AI requires performance and is almost impossible to debug). -- Regards, Dmitry A. Kazakov http://www.dmitry-kazakov.de