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l2 - Knowledge Representation Lecture 2 Introduction KR and...

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Knowledge Representation Lecture 2 Introduction KR and Logic
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Introduction Assumption of (traditional) AI work is that: Knowledge may be represented as “symbol structures” (essentially, complex data structures) representing bits of knowledge (objects, concepts, facts, rules, strategies..). E.g., “red” represents colour red. “car1” represents my car. red(car1) represents fact that my car is red. Intelligent behaviour can be achieved through manipulation of symbol structures
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Knowledge representation languages Knowledge representation languages have been designed to facilitate this. Rather than use general C++/Java data structures, use special purpose formalisms. A KR language should allow you to: represent adequately the knowledge you need for your problem ( representational adequacy ) do it in a clear, precise and “natural” way. allow you to reason on that knowledge, drawing new conclusions.
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Representational adequacy Consider the following facts: John believes no-one likes brussel sprouts. Most children believe in Santa. John will have to finish his assignment before he can start working on his project. Can all be represented as a string! But hard then to manipulate and draw conclusions. How do we represent these formally in a way that can be manipulated in a computer program? Some notations/languages only allow you to represent certain things. Time, beliefs, uncertainty, all hard to represent.
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Well-defined syntax/semantics Knowledge representation languages should have precise syntax and semantics. You must know exactly what an expression means in terms of objects in the real world. Representation of facts in World Real World New conclusions Real World Computer Computer Map to KR language Map back to real world Inference
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Well defined syntax/semantics
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