AI News, Artificial Intelligence/Knowledge Representation
- On Sunday, September 30, 2018
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Artificial Intelligence/Knowledge Representation
Knowledge representation (KR) is the name we give to how we encode knowledge, beliefs, actions, feelings, goals, desires, preferences, and all other mental states in artificial systems.
As nearly all of AI research is done by programming computers, these representations often end up being some kind of character set in software.
The world is a complex place, and the job of the AI programmer is to decide which details to encode in the KR and which to ignore.
For example, if you were programming a computer to decide on chess moves, it's not necessary to encode exactly where on the square each piece is.
The reason for this is that added representational complexity results in more complicated programming and computational processes when you run the AI program.
Having 'as much complexity as you need' is a call for 'representational adequacy,' which basically means that your representation has the information it needs to do what it needs to do.
In chess, it's not enough to know that a piece is on the board--the agent needs to know which square it's on, what kind of piece it is, and what color it is.
third criterion is naturalness, which is how easily the KR can be interpreted by people, or, perhaps, how easily it can be translated into something a person can understand.
In the language Smalltalk, for example, there is a data structure called 'bag' which can hold an unordered collection of items.
This also keeps the system from needing to change the explicitly represented number every time the list length changes.
Representational efficiency refers to how efficiently the AI can use the KR to do its reasoning--in terms of time or computational resources, as appropriate.
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