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A HYBRID MODEL OF REASONING BY ANALOGY

A HYBRID MODEL OF REASONING BY ANALOGY

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3) In AMBR a mechanism similar to ACME (Holyoak & Thagard, 1989b) is used which is a form<br />

of relaxation search in a connectionist network. In the case of AMBR where objects have<br />

corresponding descriptions (in contrast to ACME where objects are semantically empty) this<br />

mechanism makes it possible to establish the correspondences between objects and relations in<br />

parallel.<br />

There is a number of differences between the constraint networks in AMBR and in ACME. First,<br />

in contrast to ACME, in AMBR this is not a separate network but is an temporary built extension<br />

of the LTM of the reasoner. Consequently, the state of the reasoner's mind (the presently active<br />

elements of LTM and the relations between them) will influence the relaxation search. Second, in<br />

contrast to ACME, prior to relaxing the network, in the phase of its construction, it is possible to<br />

establish some correspondences (between objects or relations) when they are highly relevant and<br />

semantically similar. This will restrict the space of possible mappings described by the network.<br />

This makes it possible to model both cases: a) internal domain or close domain analogies where<br />

usually object similarity plays a major role and b) abstract analogies between far domains where<br />

usually higher-order relations dominate the mapping.<br />

Cognitive architecture<br />

GENERAL DISCUSSION<br />

Continuousness vs. Discreteness Dualism: A Hybrid Approach<br />

A hybrid cognitive architecture is proposed which combines the advantages of a symbolic approach<br />

(used for complex structured representation of situations, problems, plans, concepts, etc. as well<br />

as for the benefits of a marker passing mechanism in specialized search tasks) with the strength of<br />

connectionism in associative retrieval and soft constraint satisfaction.<br />

These different approaches are highly integrated. It is not the case that each part of the system is<br />

organized according to one of these approaches (communicating with the other parts). On the<br />

contrary, it is rather the case that different processes (symbolic and connectionist) work on the<br />

same structures which are considered as frames by the symbolic processes while the connectionist<br />

mechanisms consider them simply as nodes and links. This is possible because of the specific,<br />

rather distributed frame organization. Although there is a single frame representing a given object<br />

or concept, a lot of frames have to be traversed in order to extract all information about it, i.e. there<br />

is a whole network of frames describing an object. This is due to the fact that there is no local<br />

information in the frames but only references (pointers) to other frames. The same links are used<br />

both for spreading activation and for marker passing, but the two processes are not independent:<br />

the possibility for, and the speed of marker passing strongly depend on the activation of the nodes.<br />

In this way the connectionist aspect of the architecture continuously "restructures" the knowledge<br />

base of the reasoner represented by the symbolic aspect thus controlling the set of possible<br />

inferences at any moment. It makes some nodes more accessible and others inaccessible, thereby<br />

assigning priorities, restricting the search, etc. This makes the knowledge base dynamic and<br />

context-dependent.<br />

There is actually a dual representation of the current situation:<br />

a) an implicit distributed representation - the distribution of activity in the whole network (LTM)<br />

according to the associative relevance of each memory element, and<br />

b) an explicit local representation - a structured symbolic representation of the situation including<br />

its most important elements and the relations between them.<br />

In this way symbolism and connectionism are considered as dual aspects of human cognition

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