1. Mark the following statements as true or false.

1. An expert system doesn’t reason.

2. Expert systems can draw conclusions from complex relationships.

3. They can refine their own knowledge base.

4. The user cannot understand what rules were used in arriving at a decision.

5. The knowledge base contains all the relevant data, rules and relationships used in the expert system.

2. Put the letters in the following words into the correct order.

1) sicdeoin; 2) pliduetac; 3) dewongekl; 4) fectrinae; 5) ausilv; 6) pentolait

3. Fill in the blanks choosing from the variants given.

1. An expert system consists of integrated and related … .

a) numbers; b) units; c) components; d) statements

2. They can … symbolic information and draw conclusions from complex relationships.

a) change; b) manipulate; c) perform; d) present

3. The user … makes it easier to develop and use the expert system.

a) decision; b) network; c) knowledge; d) interface

4. Expert systems can … intelligent behavior.

a) display; b) neglect; c) leave; d) require

5. The knowledge acquisition facility helps … add or update knowledge in the knowledge base.

a) the developer; b) the client; c) the expert; d) the user

6. Expert systems may have high … costs.

a) development; b) energy; c) system; d) network

4. Give English equivalents.

1) предлагаемые решения; 2) включать (в); 3) делать выводы; 4) широко используются; 5) возможность ошибки; 6) пользовательский интерфейс; 7) совершенствовать.

5. Find the answers to the questions.

1. How can you define an expert system?

2. Do expert systems deal with uncertainty?

3. What does the knowledge base contain?

4. The rules are often composed of if-then statements, aren’t they?

5. Can expert systems refine their own knowledge base?

Text c: Neural Networks

Neural networking is a branch of artificial intelligence that allows computers to recognize and act on patterns or trends. A neural network is a computer system that can simulate for functioning of a human brain. The systems use parallel processors in an architecture that is based on the human brain structure. In a neural net thousands of computer processing units are connected in multiple ways, just as the neurons in a brain are connected. Neural nets aren't programmed – they are trained. The net learns by trial and error like humans do.

In addition, neural network software can be used to simulate a neural network using standard computers. Neural networks can process many pieces of data at once and learn to recognize patterns. The systems then program themselves to solve related problems on their own. Some of the specific features on neural networks include the following: the ability to retrieve information, fast modification of stored data, the ability to discover relationships and trends in large databases, the ability to solve complex problems for which all the information is not present.

Neural networks excel at pattern recognition. For example, neural network computers can be used to read bank check codes despite poor-quality printing. Neural networks are also used to identify threats in the sky and to detect submarines underwater by reading the pattern of radar waves. The power companies use neural networks to find electricity usage patterns so that they can analyze rate structures and forecast demand. Neural nets work particularly well when it comes to analyzing detailed trends – tasks that require precise analysis. This technology can also be employed to track individual users and their on-line preferences so that users at e-commerce sites don't have to input the same information each time they log-on – their data will be factored in each time they access a Web site.