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Why search algorithms are not AI

Search algorithms are a technique, but, in my opinion, do not represent AI at all. However, search algorithms are useful for building some types of AI systems. For example, search is an important part in computer programs for game playing (like chess and Go) and planning systems.

Planning systems usually involve the automatic generation and evaluation of plans to accomplish specific goals. It a very simple form, one might (or might not) call our two sample Java applets simple planning systems (to answer questions like "what route will take me from node 0 to node 9?"). However, real AI planning systems involve more complex tasks such as planning routes for robots to take through real (physical) environment, etc.

So, we will consider search to be a tool, or technique, that can be used in AI planning systems, computer games, et.

One idea that came out of early AI research was the notion of separating domain independent code from domain dependent data. Probably the best example of this technique is so-called "expert systems". There are many good domain independent "engines" like OPS5, CLIPS, and Jess that are completely independent of any knowledge for solving specific problems. Instead, these "engines" use data in the for of "if/then" rules to solve problems. As an example (from my Java AI book) we might want to write an expert system to diagnose medical problems associated with skin diving and scuba diving. Here we would develop a large set of domain specific (i.e., specific to solving the problem of diving medical problems) rules. An example rule might be (translated to "English"):

If there are two puncture marks and the surrounding area
is red, then there is a strong probability of an octopus bite.

If there are two puncture marks and the surrounding area
is not red, then there is a weak probability of an octopus bite.

Most people consider so-called "expert systems" to be AI. Another example of AI is natural language processing. There are many aspects to automatically processing (with computer software) natural language text. Most systems start by calculating parts of speech for input text. For example (from my free Open Source NLPServer will calculate parts of speech for any input sentence (or at least try to!). The NLPserver outputs results in either plain text, HTML, or XML. For example:

Output type: TEXT
Part of speech query: the dog ran down the street

  art, the;
  noun, dog;
  verb, ran;
  adj, down;
  art, the;
  noun, street;

Other types of AI research involve the use of artificial neural networks, genetic algorithms, and genetic programming.

Next : How we can use search algorithms in AI systems

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