IPT - A Virtual Approach IPT A Virtual Approach by Peter Whitehouse
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AI Research

Search Techniques and
Natural Language Processing

AI - Search Techniques

Experience alone does not make an expert. An expert has a mechanism for organising their knowledge efficiently, and an ability to 'find' solutions (or goal states) efficiently also. Different people search through their personal experience in different ways

Many search methods can be easily machine modelled. Such techniques may use forward chaining. Backward chaining is where we start at the goal state and search backwards until we encounter the initial state. Depth First and Breadth Firts are alternative search strategies.

searching

Another approach is Heuristic searching. An example of this would be the hill- climbing heuristic, which is a variation of the depth-first search with some way of evaluating the effectiveness of the search path taken prior to reaching the goal state.

Search techniques overlap with a number of mathematical disciplines including Probability and Statistics and Operations Research.


Natural Language Processing

The goal of natural language processing research is to enable communication between computers and people in the peoples own language. Natural language is not only CONTENT, but also CONTEXT SPECIFIC -that is not only what you say but when and how you say it are important.

Allied with this is the MODE of communication - it is hardly 'natural' to talk by typing at a console. This mode of communication is precise, however, and can be structured to be un-ambiguous. Voice recognition has many problems -currently a computer can be 'trained' to recognise a particular persons pronunciation of a finite list of words. Intonation varies between people, as does speed of delivery of words, further complication this research.

A Common 'compromise' is a FORMALISED natural language (ie. a sub-set of the words we use, chosen so as not to be ambiguous, and clearly discernible). A further hurdle is the way we ask questions or express propositions. An intelligent natural language processor would be able to recognise a common request given a number of different expressions of that request.

There are many 'voice recognition' systems currently available, including dictation software and other control systems. These systems have clearly demonstrated that recognising words and phrases is practical and relatively accurate. What is less obvious is that these systems have no real ability to work out the MEANING of what is said to them - they are merely programmed to listen for particular words, and to act when those command words are issued.

 

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