[Apologies for illegal anti-spam address above.]
> James Pascoe
shu96jsp@reading.ac.uk (Jim) writes:
> Date: 20 Jul 1997 22:15:56 GMT
>
> I would be very gratefull if any one out there could offer me some advice
> on how to implement a "best first" or heuristically guided search in
> pop-11!
There is a lot of tutorial information on searching in two files in
the Birmingham Poplog online ftp directory
ftp://ftp.cs.bham.ac.uk/pub/dist/poplog/teach/tower
A long tutorial introduction, based around the problem
of finding a subset of bricks to build a tower of a
specified height from a collection of bricks of varying
height
ftp://ftp.cs.bham.ac.uk/pub/dist/poplog/teach/searching
Shows how to produce a general purpose problem solver
supporting a variety of search strategies.
>
> I have just finished constructing breadth and depth first search code, but
> have found the task of implementing a best first seacrh very difficult
> indeed.
The basic idea is fairly simple. If you maintain a list of
unexplored nodes in the search space, then each time you derive new
nodes you can vary your search strategy depending on how you put the
new ones in the unexplored list.
If you put them all at the front of the list, and then explore them
from the front (last in first out) you'll have depth first search.
If you put them at the end and explore them from the front (first in
first out) you'll have breadth first search.
If you insert them into the list in order of merit on some
heuristic measure, then you have best first search.
There are many variants. See also two books that introduce AI
techniques through Pop-11
Chris Thornton \& Benedict du Boulay (1992)
Artificial Intelligence Through Search
Kluwer Academic (Paperback version Intellect Books)
Sharples, M. et al. (1989)
Computers and Thought
MIT Press.
Aaron
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--
Aaron Sloman, ( http://www.cs.bham.ac.uk/~axs )
School of Computer Science, The University of Birmingham, B15 2TT, UK
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