Representation of knowledge
The object of a knowledge representation is to express knowledge in a computer tractable form, so that it can be used to enable our AI agents to perform well.
A knowledge representation language is defined by two aspects:
Syntax The syntax of a language defines which configurations of the components of the language constitute valid sentences.
Semantics The semantics defines which facts in the world the sentences refer to, and hence the statement about the world that each sentence makes.
Suppose the language is arithmetic, then ‘x’, ‘=’ and ‘y’ are components (or symbols or words) of the language the syntax says that ‘x = y’ is a valid sentence in the language, but ‘= = x y’ is not the semantics say that ‘x = y’ is false if y is bigger than x, and true otherwise
The requirements of a knowledge representation are:
Representational Adequacy – the ability to represent all the different kinds of knowledge that might be needed in that domain.
Inferential Adequacy – the ability to manipulate the representational structures to derive new structures (corresponding to new knowledge) from existing structures.
Inferential Efficiency – the ability to incorporate additional information into the knowledge structure which can be used to focus the attention of the inference mechanisms in the most promising directions.
Acquisitional Efficiency – the ability to acquire new information easily. Ideally the agent should be able to control its own knowledge acquisition, but direct insertion of information by a ‘knowledge engineer’ would be acceptable. Finding a system that optimizes these for all possible domains is not going to be feasible.
In practice, the theoretical requirements for good knowledge representations can usually be achieved by dealing appropriately with a number of practical requirements:
The representations need to be complete – so that everything that could possibly need to be represented can easily be represented.
They must be computable – implementable with standard computing procedures.
They should make the important objects and relations explicit and accessible – so that it is easy to see what is going on, and how the various components interact.
They should suppress irrelevant detail – so that rarely used details don’t introduce unnecessary complications, but are still available when needed.
They should expose any natural constraints – so that it is easy to express how one object or relation influences another.
They should be transparent – so you can easily understand what is being said.
The implementation needs to be concise and fast – so that information can be stored, retrieved and manipulated rapidly.
The four fundamental components of a good representation
The lexical part – that determines which symbols or words are used in the representation’s vocabulary.
The structural or syntactic part – that describes the constraints on how the symbols can be arranged, i.e. a grammar.
The semantic part – that establishes a way of associating real world meanings with the representations.
The procedural part – that specifies the access procedures that enables ways of creating and modifying representations and answering questions using them, i.e. how we generate and compute things with the representation.
Knowledge Representation in Natural Language
Advantages of natural language
It is extremely expressive – we can express virtually everything in natural language (real world situations, pictures, symbols, ideas, emotions, reasoning).
Most humans use it most of the time as their knowledge representation of choice
ü Disadvantages of natural language
Both the syntax and semantics are very complex and not fully understood.
There is little uniformity in the structure of sentences.
It is often ambiguous – in fact, it is usually ambiguous.
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