Механизм структурного отображения в искусственном интеллекте.

In artificial intelligence and cognitive science, the structure mapping engine (SME) is an implementation in software of an algorithm for analogical matching based on the psychological theory of Dedre Gentner. The basis of Gentner's structure mapping idea is that an analogy is a mapping of knowledge from one domain (the base) into another (the target). The structure mapping engine is a computer simulation of the analogy and similarity comparisons. The theory is useful because it ignores surface features and finds matches between potentially very different things if they have the same representational structure. For example, SME could determine that a pen is like a sponge because both are involved in dispensing liquid, even though they do this very differently.

Structure mapping theory is based on the systematicity principle, which states that connected knowledge is preferred over independent facts. Therefore, the structure mapping engine should ignore isolated source target mappings unless they are part of a bigger structure. The SME, the theory goes, should map objects that are related to knowledge that has already been mapped. The theory also requires that mappings be done one to one, which means that no part of the source description can map to more than one item in the target and no part of the target description can be mapped to more than one part of the source. The theory also requires that if a match maps subject to target, the arguments of subject and target must also be mapped. If both these conditions are met, the mapping is said to be "structurally consistent."

SME maps knowledge from a source into a target. SME calls each description a dgroup. Dgroups contain a list of entities and predicates. Entities represent the objects or concepts in a description — such as an input gear or a switch. Predicates are one of three types and are a general way to express knowledge for SME. Relation predicates contain multiple arguments, which can be other predicates or entities. An example relation is: (transmit (what from to)). This relation has a functor transmit and takes three arguments: what, from, and to. Attribute predicates are the properties of an entity. An example of an attribute is (red gear) which means that gear has the attribute red. Function predicates map an entity into another entity or constant. An example of a function is (joules power source) which maps the entity power source onto the numerical quantity joules. Functions and attributes have different meanings, and consequently SME processes them differently. For example, in SME's true analogy rule set, attributes differ from functions because they cannot match unless there is a higher order match between them. The difference between attributes and functions will be explained further in this section's examples. All predicates have four parameters. They have (1) a functor, which identifies it, and (2) a type, which is either relation, attribute, or function. The other two parameters (3 and 4) are for determining how to process the arguments in the SME algorithm. If the arguments have to be matched in order, commutative is false. If the predicate can take any number of arguments, N ary is false. An example of a predicate definition is: (sme:defPredicate behavior set (predicate) relation :n ary? t :commutative? t)
The predicate's functor is “behavior set,” its type is “relation,” and its n ary and commutative parameters are both set to true. The “(predicate)” part of the definition specifies that there will be one or more predicates inside an instantiation of behavior set.

Chalmers, French, and Hofstadter [1992] criticize SME for its reliance on manually constructed LISP representations as input. They argue that too much human creativity is required to construct these representations; the intelligence comes from the design of the input, not from SME. Forbus et al. [1998] attempted to rebut this criticism. Morrison and Dietrich [1995] tried to reconcile the two points of view. Turney [2008] presents an algorithm that does not require LISP input, yet follows the principles of Structure Mapping Theory. Turney [2008] state that their work, too, is not immune to the criticism of Chalmers, French, and Hofstadter [1992]. In her article How Creative Ideas Take Shape, Liane Gabora writes "According to the honing theory of creativity, creative thought works not on individually considered, discrete, predefined representations but on a contextually elicited amalgam of items which exist in a state of potentiality and may not be readily separable. This leads to the prediction that analogy making proceeds not by mapping correspondences from candidate sources to target, as predicted by the structure mapping theory of analogy, but by weeding out non correspondences, thereby whittling away at potentiality."