Expert: Saurabh Kudesia - 12/16/2004

Question
How does a linguistic variable differ from a symbolic variable in a conventional AI system?

Answer
Hi Subhranshu,
With the advent of computers, the questions addressed by the field of artificial intelligence (AI):
- Can knowledge be programmed in a digital computer?
Can computers encode and decode that knowledge in ordinary language?
- Can they use it to interact with people and with other computer systems in a more flexible or helpful way?

Artificial Intelligence raises the same issue about knowledge and its relationship to language and to the world that have been addressed by philosophers for the past two and a half millennia.

Knowledge is more than a static encoding of facts, it also includes the ability to use those facts in interacting with the world. Basic premise of AI is that knowledge of something is the ability to form a mental model that accurately represents the thing as well as the actions than can be performed by it and on it. By testing actions on the model, a person (or robot) can predict what is likely to happen in the real world.  To test possible actions, AI systems construct microworlds.
Language, a means of communication is organised in a system of complex level of rules, each level handles one aspect of a communication process:
Syntax studies the grammar rules for expressing meaning in a string of words;
Semantics is the study of meaning itself;
Pragmatics studies how the basic meaning is related to the current context and the listener's expectations.
Traditional grammar consists of informal rules that are taught in schools.
Transformational Grammar (Noam Chomsky) is a formal theory of syntax, but it largely neglects semantics and pragmatics. Thus, it has been criticised as an unlikely model of how people use language.
In defence, Chomsky distinguished competence from performance*. He maintains that transformational grammar is an abstract theory of competence and should not be judged as a theory of performance.
AI needs a theory of performance that could support communication between people and machines.
In AI systems, conceptual graphs are widely used for representing meaning.
Conceptual graphs emphasise semantics.
In linguistics, Lucien Tesniere (1959) used similar graph for his dependency grammar.
The earliest form implemented on a computer were the correlational nets by Silvio Ceccato (1961).
Under various names, such semantic nets, conceptual dependency graphs, partitioned nets, and structured inheritance nets, the graphs have been implemented in many AI systems.
Chomsky's students diverged from the master's path, due to disagreement over several issues:
roles of syntax and semantics in generating sentences;
nature of the underlying base structure;
logic, quantifiers, and methods of binding pronouns to their antecedents;
constraints that limit transformations to just those patterns that actually occur in natural languages.
Sgall (1964) proposed generative semantics: semantic rules generate the base structure, syntactic rules map the base into the surface structure of a sentence, and phonological rules map the surface structure into actual speech.
Jackendoff (1972) maintained that different aspects of meaning are contained in separate semantic structures. As a sentence is generated, transformations combine the separate aspects into a single utterance.
Similar arguments were raised with conceptual graphs.
Woods (1975) believed that the graph should contain all the information present in the sentence.
Like Jackendoff, Quillian maintained that the basic meaning is separate from the "stage direction" that determine how the meaning is expressed.
Note: The semantic base depends on what the speaker knows about the topic. The way the speaker presents the topic depends on pragmatics - context, external circumstances, and the listener's expectations.
There is no reason to believe that all these aspects of meaning originate in a single base structure.
A sentence is derived from six different kinds of information:
Conceptual graphs are the logical forms that state relationships between persons, things, attributes, and events.
Tense and modality describe how conceptual graphs relate to the real world. They state whether something has happened, can happen, will happen, or should happen.
Presupposition is the background information that the speaker and the listener tacitly assume.
Focus is the new point that the speaker is trying to make.
Coreference links show which concepts refer to the same entities. In a sentence, these links are expressed as pronouns and other anaphoric references.
Emotional connotations are determined by associations in the mind of the speaker and listener.

Finally, The procedural - declarative controversy revolves around the question of knowledge as knowing how or knowing that.  The procedural approach assumes that a person's knowledge of the world is embodied in procedures that actively interpret the environment and operate on it.
The declarative approach assumes that knowledge is a collection of facts that can be stated in logical propositions, conceptual graphs, or other symbols.

I hope that this answers your question. Feel free to mail me your doubts.

Regards
Saurabh