1 Introduction to intel lig ent sy s tems and sof t c omputing 46
1.5 Soft c omputing 49
1.5.6 Technology needs
Even though a considerable effort has gone into the development of systems and machines that somewhat mimic humans in their actions, the present generation of intelligent systems do not claim to possess all the capabilities of human intelligence – for example, common sense, display of emotions, and inventiveness. Significant advances have been made, however, in machine implementation of characteristics of intelligence such as sensory perception,
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51 pattern recognition, knowledge acquisition and learning, inference fromincomplete information, inference from qualitative or approximate informa-tion, ability to handle unfamiliar situations, adaptability to deal with new yet related situations, and inductive reasoning. Much research and development would be needed in these areas, pertaining to techniques, hardware, and software, before a machine could reliably and consistently possess the level of intelligence of, say, a dog.
Example 1.10
Consider a handwriting recognition system, which is a practical example of an intelligent system. The underlying problem cannot be solved through simple template matching, which does not require intelligence. Handwriting of the same person can vary temporally, due to various practical shortcomings such as missing characters, errors, non-repeatability, physiological variations, sen-sory limitations, and noise. It should clear from this observation that a handwrit-ing recognition system has to deal with incomplete information and unfamiliar objects (characters), and should possess capabilities of learning, pattern recog-nition, and approximate reasoning, which will assist in carrying out intelligent functions of the system. Techniques of soft computing are able to challenge such needs of intelligent machines.
1.6 Summary
This chapter provides a general introduction to the subjects of machine intel-ligence, knowledge-based systems, and soft computing, and forms a common foundation for the remaining chapters of the book. The concept of artificial intelligence was critically discussed and the linkage between intelligence and approximation was examined. Several concepts of approximation were presented and an analytical basis for them was given. Knowledge-based intelligent systems were discussed in some detail. Their architectures and methods of representing and processing of knowledge were examined. Expert systems, which represent a practical class of knowledge-based systems, were studied. Techniques of soft computing, particularly fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning, were outlined.
The presentation given in this chapter is intended to be neither exhaustive nor highly mathematical. A more rigorous treatment of some of the topics introduced here is found in other chapters.
Problems
1. If “A → B” is F and “B → A” is T, what is the truth value of “(A → B) AND (B → A)”?
Using this result and the truth table of “A → B”, determine the truth table of
“A ↔ B”.
1 Introduction to intel lig ent sy s tems and sof t c omputing
52 2. Consider the following knowledge base:
The step response of the robot has large oscillations.
If a plant response is oscillatory then increase the derivative action.
What rule of inference may be used in processing this and what is the resulting inference? Indicate the steps involved in the reasoning process.
3. Consider the knowledge base:
LOW means less than 16°C.
SLIGHT means 1 division.
If the temperature is LOW then SLIGHTLY increase the thermostat setting.
Is this a fuzzy knowledge base? Explain.
4. Which method of knowledge representation and processing given below would be the most appropriate model for human reasoning?
(a) Two-valued logic (c) Frames
( b) Semantic networks (d) Production systems.
5. Define the term “information resolution.”
In a hierarchical control system, the degree of intelligence needed for various actions generally increases with the hierarchical level. Also, the information resolu-tion generally decreases with the hierarchical level. Considering the example of a manufacturing workcell, verify these relationships.
6. In conventional computer programs, data, instructions, and reasoning are all integrated. But knowledge-based application programs are usually “object-oriented” where these three items are separately structured and located. Give an advantage of this separation.
7. One definition of an expert system is “a computer program that embodies expert knowledge and understanding about a particular field of expertise, which can be used to assist in the solution of a problem in that field.” List several areas where expert systems are found useful. Choose a specific application and develop a simple rule base that may be used in a knowledge-based system.
8. In statistical process control (SPC ), decisions as to whether a process is in control or not are made using a control chart. A control chart consists simply of two lines, the lower control line and the upper control line, drawn parallel to the time axis, and are in units of the controlled (response) variable, as shown in the Figure P1.8.
Figure P1.8: A control chart
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53During operation of the plant, the variable (response) of interest is sampled at fixed intervals, and a sample average is computed. If this value remains within the two control lines, the process is in control. In this case the process error that exists is caused by inherent random causes and can be neglected. If the control limits are exceeded, appropriate control actions are taken to correct the situation. Do you consider SPC an intelligent control technique? Explain.
9. Suppose that a nonconventional, “intelligent” controller generates the following control inferences:
(a) Increase the control signal to some value.
( b) You may or may not increase the control signal.
(c) Increase the control signal to value (variable) x.
(d) Increase the control signal to 2.5 within a tolerance of ±5%.
(e) Increase the control signal to 2.5 with a probability of 90%.
(f ) Increase the control signal slightly.
Indicate which of these inferences are (i) vague, (ii) ambiguous, (iii) general, (iv) imprecise, (v) uncertain, and (vi) fuzzy (i.e., non-crisp).
10. Expert systems should be able not only to provide answers to questions but also to provide explanations for, or reasoning behind, these answers. Furthermore, they should have the capability to consider alternative goals, not just the single goal that is implied by a specific question. For example, suppose that an expert system for condition monitoring of a workcell is asked the question, “Is the per-formance acceptable?” With backward chaining, a response of “Yes” might be generated. This response might be true but would not be adequate. Explore this, and indicate what other details could be expected from the expert system.
11. A difficult and important task in the development of any knowledge-based (or artificial-intelligence) application is the process of knowledge engineering. This concerns the acquisition of expert knowledge and representation of the know-ledge in a form that is compatible with the specific knowknow-ledge-based system. What are some other important considerations in developing such an application?
12. Critically evaluate the statement “power of an expert system is derived from its know-ledge base, not from the particular formalism and inference scheme it employs.”
13. The decision tree shown in Figure P1.13 provides a simple knowledge base for climate control of a room. Translate this into a rule base.
Figure P1.13: The decision tree of a climate control system
1 Introduction to intel lig ent sy s tems and sof t c omputing
54 14. What is an intelligent machine? Describe a practical example of an intelligent system. Discuss the attributes that are commonly exhibited by an intelligent system that make it intelligent, and explain why these attributes make the system intelligent.
15. Discuss relevant stages of development of an intelligent product or system from its conception to widespread application and usage. What are obstacles that need to be overcome in each of these stages? Suggest ways to overcome these obstacles.
16. What is an expert system? What are performance goals of the next generation expert systems? Discuss whether fuzzy logic is appropriate for use in an expert system.
17. Critically compare and contrast “fuzziness” and uncertainty. In this comparison, specifically address the following issues, pertaining to each of these two concepts:
(a) The concept and its meaning.
( b) The mathematical representation and analysis.
(c) Appropriate application areas.
(d) Advantages and disadvantages.
Discuss how we might incorporate (combine) these two concepts in a single application. What is the rationale ( justification) for such a combined use? Give a practical example where the combined use of fuzziness and uncertainty would be important.
18. What is soft computing? Indicate biological analogies of the basic techniques of soft computing. Describe why soft computing is particularly useful in representing and reasoning with human-originated knowledge.
19. In what type of knowledge-based applications are the techniques of fuzzy logic particularly appropriate? These techniques can be strengthened and enhanced by using techniques of neural networks, evolutionary computing, and probability.
Explain the reasons for this fact.
20. The ability to approximate is a characteristic of an intelligent system. Giving ex-amples, justify this statement. How closely do the most sophisticated modern applications of machine intelligence resemble human intelligence?
References
1. Anderson, T.W. (1984) An Introduction to Multivariate Statistical Analysis, John Wiley & Sons, New York.
2. Davis, L. (1991) Handbook of Genetic Algorithms, Van Nostrand Rienhold, New York.
3. De Silva, C.W., and Lee, T.H. (1994) “Knowledge-based intelligent control,”
Measurements and Control, vol. 28, no. 2, pp. 102–113, April.
4. De Silva, C.W. (1995) Intelligent Control: Fuzzy Logic Applications, CRC Press, Boca Raton, FL.
5. De Silva, C.W. (1997) “Intelligent control of robotic systems with application in industrial processes,” Robotics and Autonomous Systems, vol. 21, pp. 221–
237.
Re fe renc es
556. De Silva, C.W. (ed.) (2000) Intelligent Machines – Myths and Realities, CRC Press, Boca Raton, FL.
7. De Silva, C.W. (2003) “The role of soft computing in intelligent machines,”
Philosophical Transactions of the Royal Society, Series A, UK.
8. Filippidis, A., Jain, L.C., and De Silva, C.W. (1999) “Intelligent control tech-niques,” Intelligent Adaptive Control (editors: Jain, L.C. and De Silva, C.W.), CRC Press, Boca Raton, FL.
9. Gupta, M.M., and Rao, H. (1994) Neural Control Theory and Applications, IEEE Press, Piscataway, NJ.
10. Hart, A. (1986) Knowledge Acquisition for Expert Systems, McGraw-Hill, New York.
11. Lee, T.H., Yue, P.K., and De Silva, C.W. (1994) “Neural networks improve control,” Measurements and Control, vol. 28, no. 4, pp. 148–153.
12. Mongi, A.A., and Gonzalez, R.C. (1992) Data Fusion in Robotics and Machine Intelligence, Academic Press, Orlando, FL.
13. Pao, Y.H. (1989) Adaptive Pattern Recognition and Neural Networks, Addison-Wesley, Reading, MA.
14. Shafer, G. (1976) A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ.
15. Staugaard, A.C. (1987) Robotics and AI, Prentice Hall, Inc., Englewood Cliffs, NJ.
16. Zadeh, L.A. (1984) “Making computers think like people,” IEEE Spectrum, pp. 26–32, August.