symmetric network in artificial intelligence

symmetric network in artificial intelligence

Semantic Networks:

I'm sure many of you will have drawn diagrams in order to clarify your thoughts in some way. These may have represented causal information such as:

This offers evidence and there is much more from psychological studies), that humans tend to store and manipulate knowledge in terms of associations and hierarchies, rather than in terms of lists of statements in some logic. This gives us the starting point for ways of representing knowledge in graphical networks.

Graphs are very easy to store inside programs because they can be succinctly represented with nodes and edges. However, if they are going to be of any use to our agents, then we will need to impose some formalism. To see why suppose our agent wants to work out the approximate age differences between people, and has some information about a family represented thus:

If we tell our agent that Jason is 25 years younger than Bryan, who is 30 years younger Arthur, who is 5 years older than Jim, then this information will be of little use in telling roughage difference between Jason and Julia. If instead, we had arranged the knowledge of family relationships graphically like this:

Then we can see how our agent could guess at an age of 20 years between Jason and Julia, because of the links between the nodes are the same.

This highlights a big problem with concept networks: because the links between nodes can be so arbitrary, we have to work hard at formalizing things before we can use the graphs in intelligent tasks. Any such formalism which aims to capture semantics graphically is called a semantic network. Mostly with the goal of representing natural language sentences graphically in mind, many formalisms for concept networks have been introduced.

in Al, in particular by Roger Schank. He introduced the conceptual dependency theory which managed to narrow down the labels for edges in graphs to just a few possibilities. The advantage of this scheme is that when reduced to graphical form two sentences that have the same meaning are represented with identical graphs. Unfortunately, it is still not clear whether a program can reliably reduce natural language sentences to the conceptual dependency format.

A more recent semantic network scheme is given by: Conceptual Graphs. These were introduced by John Sowa, and are discussed in the Luger and Stubblefield AI textbook. Each conceptual graph represents a single proposition such as "my dog is called the spot" or "all buildings have windows". They have concept nodes, which can represent concrete concepts that we can visualize, such as "restaurant" or "dog", or "my dog spot". We have little trouble making an image of concepts such as "my dog spot", but equally we can visualize generic concepts such as a dog. Concept nodes can also represent abstract concepts such as "anger" which we may not be able to visualize.

Conceptual graphs do not use labels on their arcs for describing relationships between concepts. Instead, they put in an extra node between two related concepts. These extra nodes are called conceptual relations, and we usually draw them with oval borders in conceptual graphs, as opposed to rectangles for concept nodes. An advantage to using conceptual relations rather than labeling arcs is that it is easier to represent relationships between more than two concepts. A single relationship between multiple individuals, such as in the proposition "James, John