THE SCOPE OF ARTIFICIAL INTELLIGENCE:
Background document for a proposal for
an artificial intelligence GCE/A-level syllabus.
School of Computer Science
The University of Birmingham
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AI and will always be incomplete and out of date.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
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
The final section
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.
ARTIFICIAL INTELLIGENCE: AN OVERVIEW
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
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
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.
Because humans and other animals, and equally robots and other
intelligent artefacts, have a wide variety of capabilities, all of them
very complex and hard to explain or model, both the scientific and
engineering work in AI has spawned several sub-fields, dealing with
particular aspects of intelligence.
Because the applications of AI are many and diverse some of the
sub-fields are clustered around techniques relevant to a class of
(a) Sub-fields based on content.
This list is not well structured, and the order could be improved.
Perception, especially vision but also auditory and tactile
perception, and more recently taste and smell. This breaks down into
the study of different types of processes including physical
transduction, analysis and pattern recognition, segmentation and
"parsing" of complex sensory data, interpretation, and control of
attention. This is a huge sub-field and could be broken down into
more specialised sub-sub-fields according to the sensory
modality, the kinds of things being perceived, the forms of
representations used, whether perception is purely data-driven or
includes top-down processes, the mechanisms used (e.g. neural or
symbolic), larger architecture which contains the perceptual
system, and the application domain.
Natural language processing, including production and interpretation
of spoken and written language, whether hand-written, printed, or
electronic throughout (e.g. email).
Learning and development, including symbolic learning processes (e.g.
rule induction), the use of neural nets (sometimes described as
sub-symbolic), the use of evolutionary algorithms, self-debugging
systems, and various kinds of self-organisation.
Planning, problem solving, automatic design: given a complex problem
and a collection of resources, constraints and evaluation
criteria create a solution which meets the constraints and does
well or is optimal according to the criteria, or if that cannot
be done propose some good alternatives.
Varieties of reasoning: This includes studies both of informal common
sense reasoning, and specialised expert reasoning. The former includes
the study of analogical reasoning, defeasible inferences, case based
reasoning. The latter includes logical and mathematical reasoning,
including design of theorem proving and inference systems either with
the intention of modelling various kinds of human mathematical and
inferential capabilities, or for practical purposes, e.g. in symbolic
algebra toolkits, or reasoning in robots or autonomous control systems.
Study of representations: investigating the formal properties of
various sorts of representations, the mechanisms required for them to
operate, and the kinds of tasks for which they are good or bad. This
can include the study of ontologies of various kinds. Some
mechanisms are sometimes claimed not to use any representations
(e.g. neural nets) while a counter-claim is that they are simply
a particular type of representation, e.g. numerical and
continuous as opposed to structural and discrete.
Memory mechanisms and techniques: analysis of requirements for
various kinds of memory, including very large knowledge stores
containing diverse types of knowledge, either for modelling human
knowledge or for use in various sorts of applications.
Search mechanisms and techniques:
finding a solution to some problem in a space of possibilities is a
recurring theme in AI. Many different forms of search have been studied,
related to different forms of representation, different problem domains
and different requirements (e.g. Must the solution be optimal, or is
satisficing the target? If satisficing is not enough, and optimality is
too hard could it suffice to find a solution that is guaranteed to be
close to optimal, within some limit?)
Multi agent systems: the study of various kinds of
communication (linguistic and non-linguistic, explicit and
implicit, intentional and unintended), types of cooperation and
conflict, recognition of plans and intentions in others, etc.
Some studies of multi-agent systems are concerned with
understanding human social interactions, whereas others are
concerned with the design of applications involving multiple
robots or multiple concurrent software systems. Some multi-agent
systems are proposed as an architecture for a single complex
Affective mechanisms: during the late 1990s there has been a
growing interest in the role of motivation and emotions in
intelligence. This is sometimes studied as a topic in its own
right, and sometimes as part of the study of complete
architectures for autonomous intelligent systems. A general theory would
need to account for a wide variety of affective states and processes,
including desires, preferences, dislikes, pleasures, pains, long term
goals, intentions, ideals, values, attitudes, moods, and more.
One of the current debates concerns whether emotions are required for
intelligence, or whether they are merely side-effects or emergent
features of mechanisms that are required for other functions.
Robotics: one of the oldest sub-fields of AI. This is
sometimes studied for the purpose of producing new kinds of
useful machines (which is why this heading is also in the tools section
below), and sometimes because designing complete working
robots provides a test bed for integrating theories and
techniques from various sub-areas of AI, e.g. perception,
learning, memory, motor control, planning, etc. I.e. it is a
context for exploring ideas about complete systems.
Sometimes robot designers attempt to show that certain sorts of
mechanisms are not required in systems with a certain type
of intelligence, e.g. showing what robots using no planning or
deliberative capabilities can do.
Architectures for complete systems. This was mentioned above in
connection with Affective mechanisms.
Up to the mid 1980s most of the work in AI was concerned with specific
forms of representation and specific algorithms for performing some
task. Since then there has been a growing realisation of the importance
of an architecture in which many different mechanisms are
combined to provide a system with many different sorts of functionality,
often concurrently active mechanisms. (There were some earlier
discussions of architectures in AI.)
Ontologies have been receiving considerable attention as a result of a
growing realisation that it is not enough to specify the forms of
representation that an intelligent system uses. It is important also to
investigate what sorts of things should be represented. An ontology
specification is a specification of what kinds of things are taken to
exist: two individuals that share an ontology may, however, disagree as
to which particular things allowed in that ontology actually exist, or
which laws govern their behaviour. (This topic is closely related to old
philosophical theories about what exists or what can exist.) Development
of an ontology as a result of interacting with some environment is an
important kind of learning.
The word 'ontology' is often used to refer to what is here described as
specification of an ontology. That specification could be a
particular document in a particular formal language. However, the same
ontology (the same set of entities, properties, relations, states,
processes, etc.) could be specified using different notations, in many
different documents. So it is less confusing to use the word 'ontology'
to refer to the content not the vehicle, i.e. to what is specified
rather than the means of specifying it.
Development of languages and tools listed below in the tools section.
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
(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:
AI in medicine, including interpretation of medical images, diagnosis,
expert systems to aid GPs, monitoring and control in intensive care
units, design of prosthetics, design of drugs, intelligent tutoring
systems for various aspects of medicine.
AI in robotics: including vision, motor control, learning, planning,
linguistic communication, cooperative behaviour
AI in many aspects of engineering: fault diagnosis, intelligent
control systems, intelligent manufacturing systems, intelligent
design aids, integrated systems for sales, design, production,
maintenance, expert configuration tools (e.g. ensuring sales staff
don't sell system that won't work). AI in software engineering
includes work on program synthesis, verification, debugging,
testing and monitoring of software.
AI in interfaces and "help" systems: as computers are used for more
and more applications that involve interaction with human beings,
there are ever growing pressures to make the machines easier for
non-experts to use. One approach to this is to give machines more
intelligence so that they can guide, or advise users. This
type of application can include many of the sub-fields listed above
in section C.4.(a)
AI in education: including various kinds of intelligent tutoring
systems and student management systems. Particular applications
might include diagnosis of a student's knowledge gaps, various kinds
of drill and practice tutors, automatic marking of programming
assignments and essays, etc.
AI in information management: this includes the use of AI in data
mining, web crawling, email filtering, etc.
AI in mathematics: design of tools to help with various kinds of
mathematical tasks, now used so widely that they are not
recognised as AI products.
AI in entertainment: increasingly AI is being used in computer games
and in systems for generating and controlling synthetic characters
either for textual interaction or generating films with cartoon
characters or interactive avatars in virtual worlds.
AI in biology: there are many hard problems in biology where
more or less intelligent computer-based systems are being developed,
e.g. analysis of DNA, prediction of folded structure of complex
molecules, prediction, modelling many biological processes,
evolution, development of embryos, behaviours of various organisms.
AI in Law: e.g. expert systems to help lawyers, or systems to give
legal advice and help to non-lawyers.
AI in architecture, urban design, traffic management: tools to help
solve design problems involving multiple constraints, helping to
predict the behaviours of people in new environments, tools to
analyse patterns in observed phenomena.
AI in literature, art, music: identification of authors, modelling
of processes of generation and appreciation, teaching applications.
AI in crime prevention and detection: e.g. detection of forgeries,
learning to detect evidence of crooked police officers, software
to monitor internet transactions, helping to plan police operations,
searching police databases for evidence that crimes are committed by
the same person, etc.
AI in commerce: a fast growing application area of AI enabled by the
internet involves commerce, especially electronic commerce and the
use of software agents of various sorts to provide, search for,
analyse or interpret information, take decisions, negotiate with
other agents, etc.
AI in space: control of space vehicles and autonomous robots too far
from earth to be directly manipulated by humans on earth, because
of transmission delays.
AI in military activities. This may be the area in which most funds
have been spent. It is also not easy to learn about the details.
Again this is not intended to be a complete list.
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
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
- Many AI problems are not well defined initially: in such cases the
process of developing and testing software helps to clarify the problem.
- Often the tasks require development of very complex designs which
are hard to get right. This requires powerful interactive testing
facilities, without having to start the program again and run it with a
separate debugging tool.
- AI problems often require different kinds of knowledge to be
represented and different kinds of inference mechanisms to be used.
This requires AI languages to support mixed-paradigm programming, and to
be extendable so as to allow constructs suited to new problems to be
- Intelligent systems may need to modify themselves at run time,
including modifying the code they run. E.g. a result of learning could
be the development of a new techniques or skill requiring new programs
to be used. This meant that AI development systems were often based on
interpreted or incrementally compiled programming languages.
- AI systems often need to construct complex networks of temporary
structures, where different portions have different life-spans.
AI systems often need to improve their own performance either by
learning or by simulating biological evolution.
AI systems often have to perform complex searches for solutions
to problems, e.g. searching for a plan, searching for an
interpretation of an image, searching for a proof of a theorem,
or searching for the best generalisation to
summarise a collection of learning experiences.
These requirements have led to the development of languages which
- Are extendable using macros and other facilities
- Include support for automatic garbage collection (once described
by a well known computer scientist as a luxury for the idle rich).
- Use interpreters or incremental compilers
- Have built in inference mechanisms or libraries providing inference
- Allow modules to be modified or replaced at run time
- Support multiple programming paradigms
- Provide run-time type-checking instead of compile-time checking
based on syntactic types.
- Include editors and other development tools as part of the run-time
Provide mechanisms for building neural nets or evolutionary computation
Provide mechanisms to support controlled searching possibly using
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
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
A.Sloman, Artificial Intelligence, an Illustrative Overview,
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).
Association for Computing Machinery
has a list of sub-fields of computer science, which changes from time to
time. Several versions are
It is not updated often, so it is now out of date and should be treated
There are two main UK AI organisations.
The Society for the Study of
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.
The largest AI organisation is the Association for the Advancement
Artificial Intelligence (AAAI). Information about it is at
AITOPICS, a collection of Web
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.
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Last updated: 4 Mar 2007