[ CogSci Summaries home | UP | email ]
http://www.jimdavies.org/summaries/

```@Book{,
ALTauthor = 	 {Patrick Henry Winston},
ALTeditor = 	 {},
title = 	 {Artificial Intelligence},
year = 	 {1992},
OPTkey = 	 {},
OPTvolume = 	 {},
OPTnumber = 	 {},
OPTseries = 	 {},
OPTedition = 	 {Third},
OPTmonth = 	 {},
OPTnote = 	 {},
OPTannote = 	 {}
}
```

### Author of the summary: Jim Davies, 2002, jim@jimdavies.org

#### Cite this paper for:

• SYSTEM: ANALOGY
I'm writing this summary now to get the information on Evan's ANALOGY program.

ANALOGY does traditional intelligence test analogy problems, limited to the image domain. e.g. A:B::C:?

It does this by describing how to turn A into B, then how C turns into all the choices. It matches the A to B transformation semantic net to the nets of the choices. The best match is determines ANALOGY's choice for the answer.

The system has an ontology of relations between objects in a figure:

• above
• left-of (these two are determined by seeing which quadrant the center of the other object is in.)
• inside (determined by drawing a line to infinity and counting the number of crossings. odd number means it's inside.)
Transformations describe how one figure could be changed into the next:
• rotate
• reflect
• expansion
• contraction
• delete
It's important to note, though, that relations can change, though a change in a relation is not considered a transformation. Geometric objects include, but are not limited to: 
• dot
• circle
• square
• rectangle
• triangle
The book took this information from:

Evans, T. G. (1968). A heuristic program to solve geometric analogy problems. In Semantic Information Processing edited by Minsky, M. MIT Press, Cambridge, MA.

### Summary author's notes:

• Differences with my system: No absolute locations

Back to the Cognitive Science Summaries homepage
Cognitive Science Summaries Webmaster:
JimDavies (jim@jimdavies.org)