Parametrized search

Recently I wrote about some simple AI stuff that involved different kinds of search routines. There was breadth first, depth first, best first and a* search. All of them share some common features that allowed me to write just a single routine and then tune it according to specifc search strategy I wanted to use. This blog post will show how that was done. While pretty basic concept, I found it interesting how different kinds of search strategies actually shared quite a bit between each other.

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Tinkering with simple AI stuff

Building Problem Solvers by Kenneth D. Forbus and Johan de Kleer inspired me to tinkering a bit with AI stuff again. This time, instead of writing routines for solving specific problems, I wanted to tackle writing more generic code that could be adapted to solve various kinds of problems. The result: stilpo, simple AI library that doesn’t do much yet (but hopefully will be doing bunch of stuff in the future).

Currently it supports solving problems where problem space is neatly defined and not overly large. User needs to specify how state of problem is represented, how to transition it to different states and how to recognize both identical states and goal state. After that they just choose a suitable search strategy (depth first and breadth first only currently) and hope for the best.

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