Block II, Unit I, Fundamentals of Symbolic AI

1. Block II, Unit I, Fundamentals of Symbolic AI The starting point is: expressions that both humans and computers can reason about This idea led to PSSH Symbolic representation Symbolic reasoning 1

2. Block II, Unit I, Fundamentals of Symbolic AI This unit has nine sections Introduction What is intelligence Examples of intelligent behavior The physical symbol system hypothesis Representation and reasoning Developing representations Examples of symbol representations Representation Reasoning

3. Block II, Unit I, Fundamentals of Symbolic AI What is intelligence Are intelligent actions only ones that are learnt or could an instinctive? Are intelligent tasks only the ones that can only be done competently by a subset of ‘intelligent’ people, or do tasks that almost anyone can manage, such as speaking, planning or manipulating objects, require intelligence too? Is intelligence in some way related to expertise? 3

4. Block II, Unit I, Fundamentals of Symbolic AI What is intelligence Are intelligent actions only ones that are learnt or could an instinctive? AI has traditionally tended to focus on a number of behaviors that have come to be labelled as, or are assumed to be, ‘intelligent’. These include: natural language processing: the creation of machines capable of understanding and responding to human languages such as English; expert problem solving: for instance, diagnosing medical conditions from complex sets of symptoms; planning and scheduling tasks such as airline scheduling or planning the layout of a factory floor; logical reasoning, often under conditions of uncertainty. Intelligence comes from knowledge and the use of knowledge. 4

5. Block II, Unit I, Fundamentals of Symbolic AI Examples of intelligent behavior: Interior design: Using the space efficiently Finding the drawers … 5

6. Block II, Unit I, Fundamentals of Symbolic AI Examples of intelligent behavior: Route planning Maps are symbolic representations 6

7. Block II, Unit I, Fundamentals of Symbolic AI The PSSH: Many kinds of intelligent behaviour that AI scientists have tried to replicate on machines, including: natural language processing expert problem solving logical reasoning planning and scheduling. This list is by no means exhaustive. One might add, for example: visual processing and recognition controlling physical movement 7

8. Block II, Unit I, Fundamentals of Symbolic AI The PSSH: One conventional view of intelligence is that it is a process of: taking in information from the world; processing it some sophisticated way and identifying a sensible solution; doing something with the solution. Activity 1.1 Page 18, the paper “computer science and the empirical inquiry: symbols and search” by Newell and Simon, the course DVD 8

9. Block II, Unit I, Fundamentals of Symbolic AI The PSSH: Following this article, the aim of all science is to pin down the essential facts of some aspect of nature. Newell and Simon propose just such a model of an intelligent system, which they call a symbolic system or a physical symbol system. 9

10. Block II, Unit I, Fundamentals of Symbolic AI The PSSH: Symbols can be gathered together into symbolstructures or expressions the two terms seem to be interchangeable. Expressions (which can be single symbols) designate things in the world, which Simon and Newell term objects. Some expressions specify processes that operate on other expressions within the physical symbol system. The task of identifying a process, applying it to an expression and so changing the expression in some way is termed interpretation. physical symbol systems are capable of manipulating not only the designations of objects, but also their own processes. 10

11. Block II, Unit I, Fundamentals of Symbolic AI The PSSH: symbol, defining this simply as some entity that exists in the real world and obeys the laws of physics. a physical symbol system is a collection of symbols and symbol structures. Certain symbols or symbol structures designate objects in the real world; others represent processes that can occur. The combination of all the symbol expressions within a physical symbol system determines the system’s state. The state evolves over time as processes within the system (also expressed as symbols and symbol structures) operate on it. The set of states that a physical symbol system can be in is termed the system’s state space, 11

12. Block II, Unit I, Fundamentals of Symbolic AI Representation and reasoning: Example one: the water jar problem Example two, the Missionaries and cannibals problem. 12

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14. Block II, Unit I, Fundamentals of Symbolic AI Developing Representation: Exercises 1.10, 1.11, 1.12, 1.13 P.29 The 32 dominos covering two squares on the chessboard 14

15. Block II, Unit I, Fundamentals of Symbolic AI Example of symbol representations: Chess is the classic problem of AI. 15

16. Block II, Unit I, Fundamentals of Symbolic AI Example of symbol representations: Chess is the classic problem of AI. Assigning symbols: w for white and b for black, and the positions: on(a,1,_) on(b,1,_) on(c,1,w(K)) on(d,1,w(R)) ... on(b,6,_) on(c,6,b(N)) on(d,6,b(P)) 16

17. Block II, Unit I, Fundamentals of Symbolic AI Each square is represented by an expression consisting of a predicate name (in this case on) and three arguments (the symbols inside the brackets, separated by commas). The first argument represents the square’s column (known as the file in chess jargon); the second represents the row (known as the rank); The third gives the type of piece there. The piece is itself represented by a predicate name (w or b) and one argument (the kind of piece), with _ representing an empty square. 17

18. Block II, Unit I, Fundamentals of Symbolic AI Processes: the moves that pieces are legally capable of making. Each chess piece moves in its own particular way. The rook moves horizontally or vertically as far as you like, but must stop when reaching another piece (it can never leap over an occupied square). No piece, including the rook, may land on a square occupied by a piece of its own colour, but it is possible to move onto a square occupied by a piece of the opposite colour 18

19. Block II, Unit I, Fundamentals of Symbolic AI Processes: the moves that pieces are legally capable of making. 19

20. Block II, Unit I, Fundamentals of Symbolic AI Processes: the moves that pieces are legally capable of making. Taking the position shown in Figure 1.11, two of the expressions representing the state of the board at this point would be: on( d , 1 , w ( R )) on( d , 5 ,_) Now suppose it is white’s turn to move and he decides to move his rook on d1 to d5 The move will then alter these two expressions to: on( d , 1 ,_) on( d , 5 , w ( R )) 20

21. Block II, Unit I, Fundamentals of Symbolic AI Exercise 1.15 P.34: amazing conclusion. There are around 1043 possible state of a chess game 32 pieces to store, 6 types, we need 194 bits=24 bytes to store each position To hold all the reasonable games of chess, we need 2.4  1035 Gb This requires a hard disk of 1.2  1030 m2 To compare, the surface area of the earth is around 5  1014 m2 Possible solution: heuristic search. 21

22. Block II, Unit I, Fundamentals of Symbolic AI Known patterns: the pin and the fork. 22

23. Block II, Unit I, Fundamentals of Symbolic AI Expert systems: One particular type of intelligent systems is the expert system. Its design is as follows: 23

24. Block II, Unit I, Fundamentals of Symbolic AI Expert systems: example Car maintenance Doctor diagnostics Production rules: IF NOT engine-turns-over AND NOT headlights-are-bright THEN problem-with-battery These rules can be used for inference, where new knowledge is discovered about a situation. The rule above is able to determine whether the battery in a car is flat. The rule is in two parts: the antecedent or premise (everything between the IF and the THEN) and the consequent or conclusion (everything after the THEN). If the inference process can show that the antecedent is true, then the consequent is also true. When a rule is used to prove its consequent, that rule is said to fire. 24

25. Block II, Unit I, Fundamentals of Symbolic AI Expert systems dialogue 25

26. Block II, Unit I, Fundamentals of Symbolic AI Representations: facts and actions Declarative knowledge: can be explicitly state: my cup is empty Procedural knowledge: knowledge how to speak French, or how to learning how to drive, or prepare a satisfying meal. Base knowledge: concerns the rules of the game Meta knowledge: can be used to guide the player’s reasoning to explore the promising moves while quickly disregarding the bad ones. Meta reasoning: it makes possible for the system to reason about what it is doing and how it is doing it. For example, a chess player may know that a particular move will set up an attack later. 26

27. Block II, Unit I, Fundamentals of Symbolic AI Representations: features of good representation Coverage Parsimony (concise) Clarity Use of derived knowledge Specificity: advantage of using existing notations: predicate logic is often used as a notation for expressing knowledge. 27

28. Block II, Unit I, Fundamentals of Symbolic AI Examples of representations: symbolic logic: propositional logic and predicatelogic Propositional logic R represents the proposition “it is raining” U represents the proposition “I am carrying an umbrella” W represents the proposition “I am wet” (R  U)   W 28

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31. Block II, Unit I, Fundamentals of Symbolic AI Examples of representations: symbolic logic: propositional logic and predicatelogic Predicate logic: allows us to state sentence like “All men are mortal” Mortal(man), If man(turing) → Mortal(turing) Predicate logic use two quantifiers Universal quantifier ,  x, man(x) → mortal(x) Existential quantifier  Predicate logic sees the world as collections of objects that have properties and relations with other objects; objects are different from each other because they have different properties (such as names). 31

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33. Block II, Unit I, Fundamentals of Symbolic AI Semantic networks: they represent a set of entities and relationship between them. The entities can be either objects, collections of objects or concepts. There exits two types 1- definitional networks (define concepts in term of other concepts) 2- assertional networks (describe sets of assertions or propositions) 33

34. Block II, Unit I, Fundamentals of Symbolic AI Ovals represent concepts Asterisk marking the integer oval indicates that this concept is of primitive type Broad arrows represent subtype Arrows with a circle represents role 34

35. Block II, Unit I, Fundamentals of Symbolic AI Reasoning: A goal can be a single goal: jugs problem It can be anyone of a huge number of states (chess) Satisfaction problem: if any valid solution is as good as any other. Optimization problem: if the objective is not to find ANY solution, but to find a good (or even the best) solution (TSP) Forward chaining: start from the hypothesis Backward chaining: start from the potential goal Example: car diagnosis problem. 35

36. Block II, Unit I, Fundamentals of Symbolic AI Reasoning: 36