While it's difficult to define a set of unique branches of AI techniques and methods , a standard taxonomy is provided in Table 1.2. Some of the items represent problems, while others represent solutions, but the list represents a good starting point to better understand the domain of AI.
Automatic Programming | Specify behavior, allow AI system to write the program |
Bayesian Networks | Building networks with probabilistic information |
Constraint Satisfaction | Solving NP-complete problems using a variety of techniques |
Knowledge Engineering | Transforming human knowledge into a form that a computer can understand |
Machine Learning | Programs that learn from past experience |
Neural Networks | Modeling programs that are structured like mammalian brains |
Planning | Systems that identify the best sequence of actions to reach a given goal |
Search | Finding a path from a start state to a goal state |
We'll touch on all of these topics within this book, illustrating not only the technology, but providing C language source code that illustrates the technique to solve a sample problem.