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 2

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 symbol structures 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 10 43 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  10 35 Gb  This requires a hard disk of 1.2  10 30 m 2  To compare, the surface area of the earth is around 5  10 14 m 2  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 predicate logic  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 predicate logic  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