TYPES OF ARTIFICIAL INTELLIGENCE
There are three types of AI: symbolic, connectionist, and evolutionary. Each has characteristic strengths and weaknesses.
A Symbolic AI
Symbolic AI is based in logic. It uses sequences of rules to tell the computer what to do next. Expert systems consist of many so-called IF-THEN rules: IF this is the case, THEN do that. Since both sides of the rule can be defined in complex ways, rule-based programs can be very powerful. The performance of a logic-based program need not appear “logical”, since some rules may cause it to take apparently irrational actions. “Illogical” AI programs are not used for practical problem-solving, but are useful in modelling how humans think. Symbolic programs are good at dealing with set problems, and at representing hierarchies (in grammar, for example, or planning). But they are brittle: if part of the expected input data is missing or mistaken, they may give a bad answer, or no answer at all.
B Connectionist AI
Connectionism is inspired by the brain. It is closely related to computational neuroscience, which models actual brain cells and neural circuits. Connectionist AI uses artificial neural networks made of many units working in parallel. Each unit is connected to its neighbours by links that can raise or lower the likelihood that the neighbour unit will fire (excitatory and inhibitory connections respectively). Neural networks that are able to learn do so by changing the strengths of these links, depending on past experience. These simple units are much less complex than real neurons. Each can do only one thing: for instance, report a tiny vertical line at a particular place in an image. What matters is not what any individual unit is doing, but the overall activity-pattern of the whole network.
Consequently, connectionist systems are less brittle than symbolic AI programs: even if the input data is faulty, the network may give the right answer. They are therefore good at pattern recognition, where the input-patterns within a certain class need not be identical. But connectionism is weak at doing logic, following action sequences, or representing hierarchies of goals. What symbolic AI does well, connectionism does badly, and vice versa. “Hybrid” systems combine the two, switching between them as appropriate. And work on recurrent neural networks, where the output of one layer of units is fed back as input to some previous layer, aims to enable connectionist systems to deal with sequential action and hierarchy.
C Evolutionary AI
Evolutionary AI draws on biology. Its programs make random changes in their own rules, and select the best daughter programs to breed the next generation. This method develops problem-solving programs, and can evolve the “brains” and “eyes” of robots. It is often used in modelling artificial life (A-Life). A-Life studies self-organization: how order arises from something that is ordered to a lesser degree. Biological examples include the flocking patterns of birds and the development of embryos. Technological examples include the A-Life flocking algorithms used for computer animation.
There are three types of AI: symbolic, connectionist, and evolutionary. Each has characteristic strengths and weaknesses.
A Symbolic AI
Symbolic AI is based in logic. It uses sequences of rules to tell the computer what to do next. Expert systems consist of many so-called IF-THEN rules: IF this is the case, THEN do that. Since both sides of the rule can be defined in complex ways, rule-based programs can be very powerful. The performance of a logic-based program need not appear “logical”, since some rules may cause it to take apparently irrational actions. “Illogical” AI programs are not used for practical problem-solving, but are useful in modelling how humans think. Symbolic programs are good at dealing with set problems, and at representing hierarchies (in grammar, for example, or planning). But they are brittle: if part of the expected input data is missing or mistaken, they may give a bad answer, or no answer at all.
B Connectionist AI
Connectionism is inspired by the brain. It is closely related to computational neuroscience, which models actual brain cells and neural circuits. Connectionist AI uses artificial neural networks made of many units working in parallel. Each unit is connected to its neighbours by links that can raise or lower the likelihood that the neighbour unit will fire (excitatory and inhibitory connections respectively). Neural networks that are able to learn do so by changing the strengths of these links, depending on past experience. These simple units are much less complex than real neurons. Each can do only one thing: for instance, report a tiny vertical line at a particular place in an image. What matters is not what any individual unit is doing, but the overall activity-pattern of the whole network.
Consequently, connectionist systems are less brittle than symbolic AI programs: even if the input data is faulty, the network may give the right answer. They are therefore good at pattern recognition, where the input-patterns within a certain class need not be identical. But connectionism is weak at doing logic, following action sequences, or representing hierarchies of goals. What symbolic AI does well, connectionism does badly, and vice versa. “Hybrid” systems combine the two, switching between them as appropriate. And work on recurrent neural networks, where the output of one layer of units is fed back as input to some previous layer, aims to enable connectionist systems to deal with sequential action and hierarchy.
C Evolutionary AI
Evolutionary AI draws on biology. Its programs make random changes in their own rules, and select the best daughter programs to breed the next generation. This method develops problem-solving programs, and can evolve the “brains” and “eyes” of robots. It is often used in modelling artificial life (A-Life). A-Life studies self-organization: how order arises from something that is ordered to a lesser degree. Biological examples include the flocking patterns of birds and the development of embryos. Technological examples include the A-Life flocking algorithms used for computer animation.
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