REPRESENTING COMMONSENSE KNOWLEDGE
There is a relation among artificial intelligence (AI), mathematical logic and the formalization of common-sense knowledge and reasoning: It also treats other problems of concern to both AI and philosophy. Common-sense knowledge includes the basic facts about events (including actions) and their effects, facts about knowledge and how it is obtained, facts about beliefs and desires. It also includes the basic facts about material objects and their properties.
The Commonsense World
What is Commonsense Knowledge?
If a computer is to store facts about the world and reason with them, it needs a precise language, and the program has to embody a precise idea of what reasoning is allowed, i.e., mathematical logical languages to express what an intelligent computer program knows that is relevant to the problems we want it to solve and to make the program use logical inference in order to decide what to do, contains the first proposals to use logic in AI for expressing what a program knows and how it should reason.
An AI system capable of achieving goals in the common-sense world will have to reason about what it and other actors can and cannot do. For concreteness, consider a robot that must act in the same world as people and perform tasks that people give it. Its need to reason about its abilities, puts the traditional philosophical problem of free will in the following form. What view shall we build into the robot about its own abilities, i.e. how shall we make it reason about what it can and cannot do? (Wishing to avoid begging any questions, by reason we mean compute using axioms, observation sentences, rules of inference and non-monotonic rules of conjecture).
Let A be a task we want the robot to perform, and let B and C be alternate intermediate goals either of which would allow the accomplishment of A. We want the robot to be able to choose between attempting B and attempting C. It would be silly to program it to reason.
"I'm a robot and a deterministic device. Therefore, I have no choice between B and C. What I will do is determined by my construction". Instead it must decide in some way which of B and C it can accomplish. It should be able to conclude in some cases that it can accomplish B and not C, and therefore it should take B as a sub goal on the way to achieving A. In other cases it should conclude that it can accomplish either B or C and should choose whichever is evaluated as better according to the criteria we provide it.
McCarthy and Hayes (1969) proposes conditions on the semantics of any formalism within which the robot should reason.. The essential idea is that what the robot can do is determined by the place the robot occupies in the world not by its internal structure. For example, if a certain sequence of outputs from the robot will achieve B, then we conclude or it concludes that the robot can achieve B without reasoning about whether the robot will actually produce that sequence of outputs. Our contention is that this is approximately how and system, whether human or robot, must reason about its ability to achieve goals. The basic formalism will be the same, regardless of whether the system is reasoning about its own abilities or about those of other systems including people.
Difficulties in Representing Commonsense Knowledge
The reasons that proved that formalizing common sense knowledge is difficult may be as follows,
Because there is large bulk of commonsense knowledge. To develop expert systems which represent expert knowledge few hundred or thousand facts are enough but it is not known that how many of them are sufficient to represent the common sense knowledge.
Doug Lenat has started on effort to build a knowledge base of such facts which is called as CYC by thinking one and ten million facts will be needed but it is stated by CYC authors that there is no elegant way to represent the large knowledge base.
There are no well defined limit for the commonsense knowledge o we cannot separate its parts from others.
We cannot represent the facts regarding commonsense knowledge with the help of declarative sent of declarative sentences.
Example: We cannot represent the shape of a dog using a sentence so that it can be understood. In the same way the shapes of mountains, trees,- human faces etc.
As these cannot be represented in English or in any material language, we cannot represent them in logic.
In order to deal with the difficulty of capturing of representing knowledge in a declarative sentence approximations are use.
To use approximations several changes are made to ordinary logic.
It is also difficult to represent some of the subjects like time.
The difficulties are like,
whether to represent the time a.
- Set of real numbers.
- On intervals of real line.
- And how the future has to be imagined
