List of programming languages for artificial intelligence

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Artificial intelligence researchers have developed several specialized programming languages for artificial intelligence:


  • AIML (meaning "Artificial Intelligence Markup Language")[1] is an XML dialect[2] for use with A.L.I.C.E.-type chatterbots.
  • IPL[3] 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.
  • Lisp[4] is a practical mathematical notation for computer programs based on lambda calculus. Linked lists are one of the Lisp language's major data structures, and Lisp source code is itself made up of lists. As a result, Lisp programs can manipulate source code as a data structure, giving rise to the macro systems that allow programmers to create new syntax or even new domain-specific programming languages embedded in Lisp. There are many dialects of Lisp in use today, among which are Common Lisp, Scheme, and Clojure.
  • Smalltalk has been used extensively for simulations, neural networks, machine learning and genetic algorithms. It implements the purest and most elegant form of object-oriented programming using message passing.
  • Prolog[5][6] 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.
  • STRIPS is a language for expressing 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 as one of the main standard languages. It offers several paradigms of programming like vectorial computation, functional programming and object-oriented programming. It supports deep learning libraries like MXNet, Keras or TensorFlow.
  • Python is widely used for artificial intelligence, with packages for several applications including General AI, Machine Learning, Natural Language Processing and Neural Networks.[7] 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.[8]
  • Haskell is also a very good programming 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 of expressing the algorithms. The only drawback is that working with graphs is a bit harder at first because of 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.[9]
  • C++ (2011 onwards)
  • Perl
  • Julia (programming language), e.g. for machine learning, using native or non-native libraries.

See also[edit]


  1. ^ according to (the intro page to) the AIML Repository Archived 2015-04-14 at the Wayback Machine at
  2. ^ See the AIML "Intro" (web) page Archived 2013-10-29 at the Wayback Machine at
  3. ^ Crevier 1993, pp. 46–48
  4. ^ Lisp:
  5. ^ History of logic programming:
  6. ^ Prolog:
  7. ^ Python For Artificial Intelligence Archived 2012-11-01 at the Wayback Machine Python Wiki 2015
  8. ^ Matthew Lopez (11 January 2021). "Top 10 Reasons Why Python is Good for Artificial Intelligence".
  9. ^ Wolfram Language


Major AI textbooks[edit]

See also the AI textbook survey

History of AI[edit]