THE SCOPE OF ARTIFICIAL INTELLIGENCE: Background document for a proposal for an artificial intelligence GCE/A-level syllabus.
Aaron Sloman
School of Computer Science
The University of Birmingham

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What is Artificial Intelligence (AI)?

This document provides backround information about the scope of Artificial Intelligence for use in conjunction with a document proposing a new syllabus for teaching AI in schools, available here
I start with a high level summary definition of AI, and then elaborate on various aspects of AI to provide an indiciation of the scope of the field. This document cannot hope to keep up with the development of AI and will always be incomplete and out of date. Some additional sources of information are given at the end.

What is AI?

AI is a (badly named) field of enquiry with two closely related strands: science and engineering.

The scientific strand of AI attempts to provide understanding of the requirements for, and mechanisms enabling, intelligence of various kinds in humans, other animals and information processing machines and robots.

The engineering strand of AI attempts to apply such knowledge in designing useful new kinds of machines and helping us to deal more effectively with natural intelligence, e.g. in education and therapy.

AI is inherently highly interdisciplinary because all kinds of intelligence, whether natural or artificial are concerned with subject matters that are studied in other disciplines, and the explanatory models of natural intelligence have to take account of and be evaluated in the disciplines that study the natural forms.


Like Alan Turing, in 'Computing machinery and intelligence', Mind, 59, pp. 433--460, 1950, I believe attempting to define 'intelligence' is a complete waste of time. We can collect many different examples of competences displayed by humans or other animals, and examples of challenging biologically-inspired behaviours required in future machines, and we can investigate requirements for modelling or replicating them without needing to draw any definite line between those that are and those that are not intelligent. We may find it useful to subdivide the cases in terms of either their capabilities, or the mechanisms required, or the kinds of information they use, or their potential usefulness in various contexts, and those divisions will be much more interesting and useful than any binary division based on a pre-theoretical concept like 'intelligence'.

Contents of the remainder of this document

The remainder of this document elaborates on the brief definition of AI given above. It is broadly based on the document on AI produced by the author for the QAA benchmarking panel on Computer Science degrees in 1999, available at
which was informed by consultation with university teachers and researchers in AI in the UK.

The aim is not to provide a syllabus for AI, but to provide a reminder of the scope of AI that can inform the more detailed design of a syllabus. However, many of these topics are too difficult to be included in a school syllabus, and are here merely for information.

The section on AI tools and languages gives some information about AI tools and languages, how they differ from some other programming languages and tools, and why they are needed. It is not intended to give a complete overview of AI tools or languages.

The final section additional information gives pointers to further information, including the ACM classification of areas of Computing, and information about some well known national and international AI Professional Organisations, some of which include information about the scope of AI in their own web pages. It is hoped that these pointers will help to compensate for any omissions or distortions found here.


Not all practitioners will agree with the characterisation of AI given below, which is deliberately broad, encompassing both AI as science and AI as engineering. For instance, some people who work in the field regard it as a branch of engineering, and would not include the study of human intelligence, whereas that is a central focus of many AI researchers, including some of the founders of the discipline.

The overview lists a number of sub-fields of AI which are sometimes regarded as parts of AI, and sometimes simply as ordinary applications of computer science, e.g. image interpretation. It has often happened that a technique originally developed for AI purposes has been accepted as part of "mainstream" computer science or software engineering, and it is likely that this will continue to happen.

The scope of artificial intelligence

AI is a vast, and misleadingly named, multi-disciplinary field of research and teaching which grew up in parallel with computer science and software engineering, while also building on and overlapping with other subjects like linguistics, philosophy, psychology, biology, mathematics, and logic. There are some who think it also needs advances in quantum physics in order to make progress. Not only is it multi-disciplinary in its origins and contents: courses in AI are taught not only in computer science departments, but also in others, e.g. psychology departments. Likewise degree courses in AI may include components that would often be found in other degrees, e.g. courses in philosophy of mind or philosophy of science, courses in linguistic theory, courses in human perception, or development or other aspects of human psychology.

It is clear that AI is still in its infancy: there have been many interesting theoretical developments and useful applications, but many hard problems remain unsolved, and the subject can be expected to evolve rapidly in coming years, especially as developments in the power and costs of computers both enable more effective research and also increase the need for AI.

AI has two main strands, a scientific strand and an engineering strand, which overlap considerably in their concepts, methods, and tools, though their objectives are very different.

AI as Science:

The scientific strand, which has motivated most of the pioneers and leaders in the field, is concerned with two main goals (a) attempting to understand and model the information processing capabilities of typical human minds, (b) attempting to understand the general principles for explaining and modelling intelligent systems, whether human, animal or artificial. This work is often inspired by research in philosophy, linguistics, psychology, neuroscience or social science. It can also lead to new theories and predictions in those fields.

AI as Engineering:

The engineering strand, which motivates most of the funding agencies and (consequently) younger researchers, is concerned with attempting to design new kinds of machines able to do things previously done only by humans and other animals and also new tasks that lie beyond human intelligence.

There is another nascent engineering application of AI: using the results of the scientific strand to help design machines and environments, and educational strategies that can help human beings. This may, but need not, including the production of intelligent machines. It could include the design of therapies or teaching strategies which engage more effectively with the information processing capabilities of patients or learners. Engineers also often need to take into account theories of natural intelligence when designing systems to interact usefully with humans.

Besides the contrast between Science and Engineering, there are other ways of dividing approaches to AI, e.g. according to the types of mechanisms or forms of representations thought to be most useful, or according to whether AI systems are explicitly designed or bootstrapped by some learning or evolutionary mechanism. New approaches often become fashionable for while and then are simply absorbed into the larger pattern of research and teaching.

Sub-areas of AI:

Sub-areas can be divided up in two main ways, according to the content of the study or according the tools and techniques used. These are expanded below.

(a) Sub-fields based on content.

This list is not well structured, and the order could be improved.

NB. The above is not intended to be a complete list.
There are many other sub-fields which could be listed. For a more comprehensive survey follow the pointers given in the final section, below.

(b) Sub-fields of application of AI

There is a very open-ended set of fields of application of AI. The following are merely examples, not a complete list:

Again this is not intended to be a complete list.
It is also not claimed that there are no other useful ways of dividing AI into possible course topics or research areas. Many of the above sub-headings were chosen simply because there are groups of researchers, conferences, journals, or books that focus on the topics given.

Far more information about types of AI applications can be found at

AI tools and languages

AI researchers have often found that existing software tools and programming languages, and standard design and development methodologies, were too restrictive, and therefore developed new special purpose versions. However many of the ideas have been taken up more generally, so that the distinctions are no longer clear. For example, many early AI systems were more concerned with manipulation of symbols and symbolic structures than manipulation of numbers, and they therefore included many constructs for non-numeric programming. However this is now a standard requirement for software engineering. Similarly early AI systemts included automatic 'garbage collection' mechanisms for working out which data structures were still in use and which were not after a series of complex manipulations. Now automatic garbage collection mechanisms have been added to other software development environments.

Some of the reasons for the special requirements of AI languages are

These requirements have led to the development of languages which

Some of the specialised languages developed to support AI research and applications include Prolog, Scheme, Smalltalk, OPS-5 and other production system interpreters, several varieties of Lisp including Common Lisp and its main precursors Maclisp and Interlisp, Pop2 and its derivatives, e.g. Pop-11, hybrid systems supporting more than one language, e.g. Loglisp, Poplog.

Using these and other languages, various tools have been developed to support knowledge acquisition and testing, theorem provers, planners, problem solvers, parsers and other forms of software for manipulating natural language, neural net toolkits, image processing tools, robot development tools, tools for designing and testing cognitively rich agents, tools for developing multi-agent systems, rule induction and learning systems, automatic program generating and testing tools, tools for doing experiments in artificial life, and tools for supporting evolutionary computation.

Some of the tools are closely related to particular theories, or intended to support particular types of techniques, e.g. constraint-manipulation toolkits, tools for building cognitive models based on SOAR, or ACT-R.

There have been some experiments in designing new forms of hardware to support AI, e.g. hardware tailored to playing chess, hardware for vision, hardware for implementing AI languages like Lisp or Prolog, hardware for neural computation, in addition to robots and robot components. In future there may be AI models or applications using entirely new forms of computers, e.g. quantum computers or DNA computers.

Many of the tools required for AI as engineering overlap with those required for AI as science, since the task of producing intelligent applied systems has much in common with the task of producing models of natural intelligent systems. Of course there are some AI researchers who would dispute that.

Should there be a separate section on AI techniques and representations?


This document is based to a large extent on the overview of AI produced for the QAA CS Benchmarking panel in 1999
The version of this document that existed around December 1999 was published as
A.Sloman, Artificial Intelligence, an Illustrative Overview, in AISB Quarterly Winter 2000, no 103, pages 31-35, with a brief introduction by the editor, Blay Whitby.


Useful comments on the precursor of this document were made by John Barnden (Birmingham University), Ann Blandford (Middlesex University), Benedict du Boulay (Sussex University), Inman Harvey (Sussex University), Bob Hendley (Birmingham University), Phil Husbands (Sussex University), Peter Ross (Edinburgh University).

Additional information

The Association for Computing Machinery has a list of sub-fields of computer science, which changes from time to time. Several versions are available at

It is not updated often, so it is now out of date and should be treated with caution.

AI Organisations

There are two main UK AI organisations.

The Society for the Study of Artificial Intelligence and Simulation of Behaviour, claims to be the oldest AI society in the world. See

The British Computer Society Specialist Group on Artificial Intelligence (BCS-SGAI) has a more applied focus, though its seminars and annual conferences are very wide ranging. See

The main European AI organisation is ECCAI (European Coordinating Committee on AI), to which national AI organisations are affiliated. See

The largest AI organisation is the Association for the Advancement of Artificial Intelligence (AAAI). Information about it is at . It includes AITOPICS, a collection of Web pages (under continual development) that attempt to characterise the scope of AI, and provide a steady stream of news about AI and its applications.

The major regular international conference on AI is The International Joint Conference on AI (IJCAI) held every two years since 1969. See

There are also many national Artificial Intelligence Societies, which organise conferences, and other activities.

There is a growing collection of web sites providing information about AI, some compiled by individuals and some by firms or organisations.

A document on AI for School Careers Advisers
A document was put together in 1998 for a conference of school careers advisers in England giving a more discursive overview of AI:

This includes pointers to several other sources of information about AI, including summaries by some of the founders of the field (e.g. Minsky, McCarthy), some textbooks, and UK universities known to be offering undergraduate degrees with AI in the title in May 1998.

This file maintained by Aaron Sloman
Last updated: 4 Mar 2007