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Product Description
This is a practical, highly-accessible introduction to the state-of-the-art in artificial intelligence.This
book demystifies artificial intelligence, making it concrete and transparent. It covers knowledge representation, inference,
expert systems, natural language processing, machine learning, neural networks, agents, robots, and more. The book includes
extensive self-test questions, case studies, figures, worked examples, sample algorithms and a complete glossary.For anyone
interested in artificial intelligence; no prior background is required.
From the Back Cover
This is a practical,
highly-accessible introduction to the state-of-the-art in artificial intelligence.This book demystifies artificial intelligence,
making it concrete and transparent. It covers knowledge representation, inference, expert systems, natural language processing,
machine learning, neural networks, agents, robots, and more. The book includes extensive self-test questions, case studies,
figures, worked examples, sample algorithms and a complete glossary.For anyone interested in artificial intelligence; no prior
background is required.
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p.9,10,12 One of the assumptions underlying most work in artificial intelligence is that intelligent
behavior can be achieved through the manipulation of symbol structures representing bits of knowledge...
Knowledge representation languages have been developed to make this easier... A knowledge representation language should allow
you to represent adequately complex facts in a clear and precise yet natural way, and in a way
that easily allows you to deduce new facts from your existing knowledge... Inferences should be made efficiently.
p.40,41 Expert systems solve real problems which normally would require a human expert...
Building an expert system first involves extracting the relevant knowledge from the human expert. Such knowledge is often
heuristic in nature, based on useful "rules of thumb" rather than absolute certainties. Extracting it from the expert in a
way that can be used by a computer is generally a difficult task, requiring its own expertise. A knowledge engineer
has the job of extracting this knowledge and building the expert system knowledge base.
p.43-44 Having decided that your problem is suitable you need to extract the knowledge from the
expert and represent it using some suitable knowledge representation scheme. This is the job of the knowledge
engineer, but involves close collaboration with the expert(s) and the end user(s).
p.77 Where the search space is too big to search every node it may be possible to construct some
scoring function that can be used to provide an estimate as to which paths or nodes seem promising. Then the promising
nodes are explored before the less promising ones. Search methods that use such a scoring function are referred to as heuristic
search techniques.
The basic idea of heuristic search is that, rather than trying all possible search paths,
you try to focus on paths that seem to be getting you nearer your goal state. You generally can't be sure that you
are really near your goal state... But we might be able to have a good guess. Heuristics are used to help us make that guess.
p.77 To use heuristic search you need an evaluation function that scores a node in the search tree
according to how close to the target/goal state it seems to be. This will just be guess, but it should still be useful.
p.95 Heuristic search methods attempt to use some knowledge of how close a state is to a target
state to select which paths to explore first.
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