Nội dung

Chapter 3 Representing Knowledge in Computer K216 C: Studies on Intelligence School of Knowledge Science JAIST TuBao Ho 1 Outline of chapter 3 1. Introduction   2. 3. 4. 5. 6. Representing knowledge Metrics for assessing knowledge representation schemes Logic representation Inference rules Semantics networks Frames and Scripts Decision trees 2 Introduction  declarative knowledge is knowledge about things    location of JAIST, its transport links “JAIST is in Tatsunokuchi”, “Hokuriku Railroad Ishikawa line goes from Nomachi to Tsurugi” procedural knowledge is knowledge about how to do things   how to get to JAIST “Take the Hokuriku Railroad, Ishikawa line to go to Tsurugi”, “Get on the JAIST shuttle” 3 Introduction  domain-specific knowledge: specific knowledge on a particular subject Example: “JAIST shuttle goes from Tsurugi to JAIST”  domain-independent knowledge: general knowledge that applies throughout our experience Example: “shuttle bus is a means of transport”  Common sense: common knowledge about the world that is possessed by every schoolchild. It is evident for human but not for machine Example: “Bird can fly” 4 Introduction    In order to make use of knowledge in AI and intelligent systems we need to get it from the source (knowledge acquisition) and represent it in a form usable by the machine Human knowledge is usually expressed through language, which cannot be accurately understood by machine The representation of knowledge in computer must therefore be both appropriate for the computer to use and allow easy and accurate encoding from the source 5 Example of representing knowledge The “15 game”: two people A and B take turns selecting numbers from 1 to 9 without replacement. The person who first has exactly three numbers in his collection that add up to 15 wins the game  A 5; B 3 A 5, 9; B 3, 1  A 5, 9, 4; B 3, 1, 2  A 5 9 4 6 win!!  (A selects 5; B selects 3) (A selects 9: B chooses 1 to prevent A from achieving 15) (A selects 4; B chooses 2 to block A) (A selects 6 and wins with 4+5+6 = 15) 6 Example of representing knowledge  A choosing 5 is equivalent to putting A’s marker in the ticktack-toe board. Use tick-tack-toe representation for the “15 game” A 5; B 3 A 5, 9; B 3, 1 A 5, 9, 4; B 3, 1, 2 A 5 9 4 6 win!! A B A A B B B A A B 2 9 4 3 6 5 1 A B B A A A B A B 7 Aspects of representation languages 1. The syntax describes the possible configurations that can constitute sentences   External representation: how sentences are represented on the printed page Internal representation: the real representation inside the computer 2. The semantics determines the fact in the world to which the sentences refer. Without semantics, a sentence is just an arrangement of electrons or a collection of marks on a page 8 Metrics for assessing knowledge representation schemes  Expressiveness Handle different types and levels of knowledge  Effectiveness （効果） Effectiveness is doing the right thing  Efficiency （能率） Efficiency is doing the thing right  Explicitness Be able to provide an explanation of its inferences 9 Outline of chapter 3 1. Introduction 2. Logic representation 2.1 2.2 Propositional logic Predicate logic Inference rules 4. Semantics networks 5. Frames and Scripts 6. Decision trees 3. 10