List of programming languages for artificial intelligence

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Historically, some programming languages have been specifically designed for artificial intelligence (AI) applications. Nowadays, many general-purpose programming languages also have libraries that can be used to develop AI applications.

Languages

  • Artificial Intelligence Markup Language (AIML)[1] is an XML dialect[2] for use with Artificial Linguistic Internet Computer Entity (A.L.I.C.E.)-type chatterbots.
  • C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform.
  • C++ is a compiled language that can interact with low-level hardware. In the context of AI, it is particularly used for embedded systems and robotics. Libraries such as TensorFlow C++, Caffe or Shogun can be used.[3]
  • Lisp was the first language developed for artificial intelligence. It includes features intended to support programs that could perform general problem solving, such as lists, associations, schemas (frames), dynamic memory allocation, data types, recursion, associative retrieval, functions as arguments, generators (streams), and cooperative multitasking.
  • Smalltalk has been used extensively for simulations, neural networks, machine learning, and genetic algorithms. It implements a pure and elegant form of object-oriented programming using message passing.
  • Prolog[4][5] is a declarative language where programs are expressed in terms of relations, and execution occurs by running queries over these relations. Prolog is particularly useful for symbolic reasoning, database and language parsing applications. Prolog is widely used in AI today.
  • 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. For each action preconditions (what must be established before the action is performed) and postconditions (what is established after the action is performed) are specified.
  • Planner is a hybrid between procedural and logical languages. It gives a procedural interpretation to logical sentences where implications are interpreted with pattern-directed inference.
  • POP-11 is a reflective, incrementally compiled programming language with many of the features of an interpreted language. It is the core language of the Poplog programming environment developed originally by the University of Sussex, and recently in the School of Computer Science at the University of Birmingham which hosts the Poplog website, It is often used to introduce symbolic programming techniques to programmers of more conventional languages like Pascal, who find POP syntax more familiar than that of Lisp. One of POP-11's features is that it supports first-class functions.
  • R is widely used in new-style artificial intelligence, involving statistical computations, numerical analysis, the use of Bayesian inference, neural networks and in general machine learning. In domains like finance, biology, sociology or medicine it is considered one of the main standard languages. It offers several paradigms of programming like vectorial computation, functional programming and object-oriented programming.
  • Python is widely used for artificial intelligence, with packages for several applications including general AI, machine learning, natural language processing, and artificial neural networks.[6] The application of AI to develop programs that do human-like jobs and portray human skills is machine learning. Both artificial intelligence and machine learning are closely connected and are being used widely today.[7]
  • Haskell is a very good language for AI. Lazy evaluation and the list and LogicT monads make it easy to express non-deterministic algorithms, which is often the case. Infinite data structures are great for search trees. The language's features enable a compositional way to express algorithms. The only drawback is that working with graphs is a bit harder at first because of functional purity.
  • Wolfram Language includes a wide range of integrated machine learning capabilities, from highly automated functions like Predict and Classify to functions based on specific methods and diagnostics. The functions work on many types of data, including numerical, categorical, time series, textual, and image.[8]
  • Julia, e.g. for machine learning, using native or non-native libraries.
  • Mojo can run some Python programs, and supports programmability of AI hardware.[9]

See also

Notes

  1. ↑ according to (the intro page to) the AIML Repository at nlp-addiction.com
  2. ↑ See the AIML "Intro" (web) page at www.alicebot.org
  3. ↑ Wodecki, Ben (May 5, 2023). "7 AI Programming Languages You Need to Know". AI Business. https://aibusiness.com/verticals/7-ai-programming-languages-you-need-to-know#close-modal. 
  4. ↑ History of logic programming:
  5. ↑ Prolog:
  6. ↑ Python For Artificial Intelligence Python Wiki 2015
  7. ↑ Lopez, Matthew (11 January 2021). "Top 10 Reasons Why Python is Good for Artificial Intelligence". https://www.technologysumo.com/why-python-is-good-for-artificial-intelligence/. 
  8. ↑ Wolfram Language
  9. ↑ Yegulalp, Serdar (7 June 2023). "A first look at the Mojo language" (in en). InfoWorld. https://www.infoworld.com/article/3697739/a-first-look-at-the-mojo-language.html. 

References

Major AI textbooks

See also the AI textbook survey

History of AI



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