The onion model is a graph-based diagram template for describing an expanding or extending relationship between several concepts. The name is a metaphor of the layered shells that become visible when you cut open an onion.
The Basic Elements of AI
Nilsson, a pioneer in AI, likes to characterize the components of AI in terms of
what he calls the onion model (See Figure 1). The inner ring depicts the basic
elements from which the applications shown in the next ring are composed. We will
first consider the quadrant designated as heuristic search.
2.2.1 Heuristic Search
Much of the early work in AI was focused on deriving programs that would
search for solutions to problems. Note that every time one makes a decision, the
situation is changed opening up new opportunities for further decisions. Therefore
there are always branch points. Thus, one of the usual ways of representing problem
solving in AI is in terms of a tree, starting at the top with an initial condition and
branching every time a decision is made. As one continues down the tree many
different decision possibilities open up, so that the number of branches at the bottom
can get to be enormous for problems requiring many solution steps. Therefore, some
way is needed to efficiently search the trees.
Knowledge Representation
Early on, AI researchers discovered that intelligent behavior is not so much
due to the methods of reasoning, as it is dependent on the knowledge one has to
reason with. (As humans go through life they build up tremendous reservoirs of
knowledge.) Thus, when substantial knowledge has to be brought to bear on a
problem, methods are needed to efficiently model this knowledge so that it is readily
accessible. The result of this emphasis on knowledge is that knowledge
representation is one of the most active areas of research in AI today. The needed
knowledge is not easy to represent, nor is the best representation obvious for a given
task.
Common Sense Reasoning and Logic
AI researchers found that common sense (virtually taken for granted in
humans) is the most difficult thing to model in a computer. It was finally concluded
that commonsense is low level reasoning, based on a wealth of experience. In
acquiring common sense we learn to expect that when we drop something, it falls,
and in general what things to anticipate in everyday events. How to represent
common sense in a computer is a key AI issue that is unlikely to be soon solved.
Another area that is very important in Al is logic. How do we deduce
something from a set of facts? How can we prove that a conclusion follows from a
given set of premises? Computational logic was one of the early golden hopes in AI to
provide a universal problem solving method. However, solution convergence proved to
be difficult with complex problems, resulting in a diminishing of interest in logic.
Logic is now enjoying a revival based on new formulations and the use of heuristics
to guide solutions.
AI Languages and Tools
In computer science, specific high level languages have been developed for
different application domains. This has also been true for AI. Currently, LISP and
PROLOG are the principal AI programming languages. To date, LISP (List Processing
Language, developed in the late 50’s by John McCarthy) has been the prime language
2.3 Principal AI Application Areas
Based on these basic elements, Nilsson identified four principal AI application
areas (shown in the outer ring of the Figure 1)
2.3.1 Natural Language Processing (NLP)
NLP is concerned with natural language front ends to computer programs,
computer-based speech understanding, text understanding and generation, and
related applications. A detailed overview of NLP is given in Lesson 16.
2.3.2 Computer Vision
Computer Vision is concerned with enabling a computer to see, to identify or
understand what it sees, to locate what it is looking for, etc. A detailed overview of
Computer Vision is beyond the scope of this paper.
2.3.3 Expert Systems
Expert Systems is perhaps the hottest topic in AI today. How do we make a
computer act as if it was an expert in some domain? For example, how do we get a
computer to perform medical diagnosis or VLSI design? A detailed overview of Expert
Systems is given in Lesson 14.
2.3.4 Problem Solving and Planning
There are many problems for which there are no experts, but nevertheless
computer programs for their solutions are needed. In addition there are some basic
planning systems that are more concerned with solution techniques than with
knowledge. A comprehensive overview of problem solving and planning is given in
The Basic Elements of AI
Nilsson, a pioneer in AI, likes to characterize the components of AI in terms of
what he calls the onion model (See Figure 1). The inner ring depicts the basic
elements from which the applications shown in the next ring are composed. We will
first consider the quadrant designated as heuristic search.
2.2.1 Heuristic Search
Much of the early work in AI was focused on deriving programs that would
search for solutions to problems. Note that every time one makes a decision, the
situation is changed opening up new opportunities for further decisions. Therefore
there are always branch points. Thus, one of the usual ways of representing problem
solving in AI is in terms of a tree, starting at the top with an initial condition and
branching every time a decision is made. As one continues down the tree many
different decision possibilities open up, so that the number of branches at the bottom
can get to be enormous for problems requiring many solution steps. Therefore, some
way is needed to efficiently search the trees.
Knowledge Representation
Early on, AI researchers discovered that intelligent behavior is not so much
due to the methods of reasoning, as it is dependent on the knowledge one has to
reason with. (As humans go through life they build up tremendous reservoirs of
knowledge.) Thus, when substantial knowledge has to be brought to bear on a
problem, methods are needed to efficiently model this knowledge so that it is readily
accessible. The result of this emphasis on knowledge is that knowledge
representation is one of the most active areas of research in AI today. The needed
knowledge is not easy to represent, nor is the best representation obvious for a given
task.
Common Sense Reasoning and Logic
AI researchers found that common sense (virtually taken for granted in
humans) is the most difficult thing to model in a computer. It was finally concluded
that commonsense is low level reasoning, based on a wealth of experience. In
acquiring common sense we learn to expect that when we drop something, it falls,
and in general what things to anticipate in everyday events. How to represent
common sense in a computer is a key AI issue that is unlikely to be soon solved.
Another area that is very important in Al is logic. How do we deduce
something from a set of facts? How can we prove that a conclusion follows from a
given set of premises? Computational logic was one of the early golden hopes in AI to
provide a universal problem solving method. However, solution convergence proved to
be difficult with complex problems, resulting in a diminishing of interest in logic.
Logic is now enjoying a revival based on new formulations and the use of heuristics
to guide solutions.
AI Languages and Tools
In computer science, specific high level languages have been developed for
different application domains. This has also been true for AI. Currently, LISP and
PROLOG are the principal AI programming languages. To date, LISP (List Processing
Language, developed in the late 50’s by John McCarthy) has been the prime language
2.3 Principal AI Application Areas
Based on these basic elements, Nilsson identified four principal AI application
areas (shown in the outer ring of the Figure 1)
2.3.1 Natural Language Processing (NLP)
NLP is concerned with natural language front ends to computer programs,
computer-based speech understanding, text understanding and generation, and
related applications. A detailed overview of NLP is given in Lesson 16.
2.3.2 Computer Vision
Computer Vision is concerned with enabling a computer to see, to identify or
understand what it sees, to locate what it is looking for, etc. A detailed overview of
Computer Vision is beyond the scope of this paper.
2.3.3 Expert Systems
Expert Systems is perhaps the hottest topic in AI today. How do we make a
computer act as if it was an expert in some domain? For example, how do we get a
computer to perform medical diagnosis or VLSI design? A detailed overview of Expert
Systems is given in Lesson 14.
2.3.4 Problem Solving and Planning
There are many problems for which there are no experts, but nevertheless
computer programs for their solutions are needed. In addition there are some basic
planning systems that are more concerned with solution techniques than with
knowledge. A comprehensive overview of problem solving and planning is given in
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