An ontology represents knowledge as a set
of concepts within a domain and the
relationships between those concepts.
The general problem of simulating (or creating) intelligence has been broken into subproblems. These
consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits
described below have received the most attention and cover the scope of AI research.[a]
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they
solve puzzles or make logical deductions.[13] By the late 1980s and 1990s, methods were developed for
dealing with uncertain or incomplete information, employing concepts from probability and
economics.[14]
Many of these algorithms are insufficient for solving large reasoning problems because they experience a
"combinatorial explosion": They become exponentially slower as the problems grow.[15] Even humans
rarely use the step-by-step deduction that early AI research could model. They solve most of their
problems using fast, intuitive judgments.[16] Accurate and efficient reasoning is an unsolved problem.
Knowledge representation and knowledge engineering[17]
allow AI programs to answer questions intelligently and
make deductions about real-world facts. Formal knowledge
representations are used in content-based indexing and
retrieval,[18] scene interpretation,[19] clinical decision
support,[20] knowledge discovery (mining "interesting" and
actionable inferences from large databases),[21] and other
areas.[22]
A knowledge base is a body of knowledge represented in a
form that can be used by a program. An ontology is the set
of objects, relations, concepts, and properties used by a
particular domain of knowledge.[23] Knowledge bases need
to represent things such as objects, properties, categories,
and relations between objects;[24] situations, events, states,
and time;[25] causes and effects;[26] knowledge about
knowledge (what we know about what other people
know);[27] default reasoning (things that humans assume are true until they are told differently and will
remain true even when other facts are changing);[28] and many other aspects and domains of knowledge.
Among the most difficult problems in knowledge representation are the breadth of commonsense
knowledge (the set of atomic facts that the average person knows is enormous);[29] and the sub-symbolic
form of most commonsense knowledge (much of what people know is not represented as "facts" or
"statements" that they could express verbally).[16] There is also the difficulty of knowledge acquisition,
the problem of obtaining knowledge for AI applications.[c]
Reasoning and problem-solving
Knowledge representation
Planning and decision-making