Problems of Handling Ambiguity with Computers | Computer Science

In this article we will discuss about:- 1. Need of Ambiguity 2. Aspect of Ambiguity 3. Suitability of Fuzzy Logic.

Need of Ambiguity:

It is said that the binary logic which allows ‘yes’ or ‘no’ is due to Aristotle, an ancient Greek philosopher. He recognised the existence of what should be determined not either ‘yes’ or ‘no’. This means that ambiguity was even acknowledged when the study of logic began. But, the main acceptance during the renaissance of symbolic logic, where only ‘yes’ and ‘no’ are assumed, remains upto the present.

There are two kinds of logic higher than the formal logic- Modal logic is a logic dealing with cases: there is a possibility or there is necessity of being true. This logic also originated with Aristotle, but it cannot be used for contemporary science and technology. The second type, Multiple-valued logic is the logic where a degree of truth between ‘yes’ and ‘no’ is acknowledged.

Modern science and technology definitely based on binary logic does not allow ambiguity. Therefore, an exclusion of ambiguity is an absolute and unconditional requirement in modern science and technology. Modern science and technology are also strongly influenced by Descarte’s theory of modern rationalism.

His methodology is analytical, in which any complicated problem can be understood by dividing the problem into several smaller problems until each problem can be clearly understood. After every sub-problem becomes clear they can be combined to grasp the whole problem.

The basic idea is that the ‘whole’ is understood by the composition of each part. Thus, the divided parts should be clearly agreed by everybody. The combining process has also to be consensus, where objectivity and universality are significant guiding principles. It implies that there is an assumption or a belief that everything has a unique answer which we must discover and clarify. Descartes theories have been favoured overtime.

The spirit of Descartes was succeeded by Newton, who established modern rationalism. It can be thus said that analytical and objective approach has succeeded. Because of Descartes guiding principles and modern rationalism, we have the physically affluent society with fully developing science and technology. Science makes unknown things clear. Simultaneously, ambiguity is getting sufficiently developed, which can be removed only by developing the science.

This is the common belief. Science and technology are for the betterment of human beings and as we have seen from the above examples, humans are essentially ambiguous. Therefore, now is the time that we have to change to conventional science so as to insert an allowance for ambiguity.

Aspect of Ambiguity:

Upto now we have used the term ambiguity without an exact definition. What does it really mean? What ambiguity means is itself ambiguous.

We have various ambiguities. The following are some semantics of ambiguities:

1. Incomplete:

Not understandable because of the lack of information. For example, I cannot speak Spanish. So I cannot understand something spoken in Spanish. Although the information has a meaning, I could not accept it because of lack of knowledge.

2. Ambiguity:

Indefiniteness is in several interpretations of one word. An English word ‘bank’ has several meanings; bank of river or canal, a financial institute or dependence etc. This is called the ambiguity of a word. Ambiguity of a picture can also exist.

3. Randomness:

Which side will come up by shaking a dice? Which leg will I use first when I leave for college, tomorrow. Randomness is often said to be accident which can be calculated with probability.

4. Imprecision:

Not precise or exact. This includes ambiguous cases which include errors or ‘noise’. This ambiguity is caused by the imprecision of information.

5. Fuzziness:

Unable to define, or have a meaningless definition. They refer to ambiguities with respect to words, that is ambiguity of semantics. For instance, it is ambiguous as to whether “she is beautiful” or not or “today it is hot or not”. The answers may depend upon individual’s perception.

There can be other kinds of ambiguities, but we restrict ourselves, only to the ambiguity of fuzziness. There are about 170 meanings for the word ‘ambiguity’. Here we are not concerned with the meanings but the usage. There are two usages; prime words such as ‘ambiguous’, ‘vague’ and ‘dim’ and compounds with negative prefix including ‘un’ or ‘in’ such as ‘uncertain’, ‘inaccurate’ etc.

The adjective ‘fuzzy’ means boundaries are vague like on a feather. Both probability and fuzzy theory are theories which can be used to deal with ambiguity. Fuzzy theory can be used for a numerical analysis of ambiguity. Of course it is also true that the two theories-probability and fuzzy, cannot solve all of the ambiguities. So we need yet new theories, which are sure to emerge.

Suitability of Fuzzy Logic for Dealing Ambiguity:

Fuzzy logic is suitable for many applications:

1. Inherently Robust:

It is inherently robust, since it does not require precise, noise-free inputs and can be programmed to fail safely if a feedback sensor quits or is destroyed. The output control is a smooth control function despite a wide range of input variations.

2. No limitation of Input and Output:

Due to the rule-based nature of, any reasonable number of inputs can be processed and numerous outputs generated. However defining the rule base becomes complex if too many inputs and outputs are chosen for a single implementing.

3. Control for Non-Linear Systems:

Fuzzy Logic can control non-linear systems that would be difficult or impossible to model mathematically. This opens doors for control systems that would normally be deemed unfeasible for automation.

4. Fuzzy Logic is a Convenient Way to Map an Input Space to an Output Space.

5. Fuzzy Logic is Conceptually Easy to Understand:

The mathematical concepts behind fuzzy reasoning are very simple. Fuzzy has the “naturalness” in its approach and not its far-reaching complexity.

6. Fuzzy Logic is Flexible:

With any given system, it’s easy to layer more functionality on top of it without starting again from scratch.

7. Fuzzy Logic is Tolerant of Imprecise Data:

Everything is imprecise if there is a careful inspection. Fuzzy reasoning builds this understanding into the process rather than tacking it into the end.

8. Fuzzy Logic can Model Non-Linear Functions or Arbitrary Complexity:

A fuzzy system can be created to match any set of input-output data. This process is made particularly easy by adaptive technique like ANFIS (Adaptive Neuro-Fuzzy Inference Systems), which are available in the Fuzzy Logic Toolbox.

9. Fuzzy Logic can be Built on Top of the Experience of Experts:

In direct contrast to neural networks, which take training data and generate opaque, impenetrable models, fuzzy logic can rely on the experience of people who already understand the system.

10. Fuzzy Logic can be Blended with Conventional Control Techniques:

Fuzzy systems don’t necessarily replace conventional control methods. In many cases fuzzy systems augment them and simplify their implementation.