Chapter 3: Methods of Inference

1. Chapter 3:Methods of Inference

2. Objectives • Learn the definitions of trees, lattices, and graphs • Learn about state and problem spaces • Learn about AND-OR trees and goals • Explore different methods and rules of inference • Learn the characteristics of first-order predicate logic and logic systems

3. Objectives • Discuss the resolution rule of inference, resolution systems, and deduction • Compare shallow and causal reasoning • How to apply resolution to first-order predicate logic • Learn the meaning of forward and backward chaining

4. Objectives • Explore additional methods of inference • Learn the meaning of Metaknowledge • Explore the Markov decision process

5. Trees • A tree is a hierarchical data structure consisting of: • Nodes – store information • Branches – connect the nodes • The top node is the root, occupying the highest hierarchy. • The leaves are at the bottom, occupying the lowest hierarcy.

6. Trees • Every node, except the root, has exactly one parent. • Every node may give rise to zero or more child nodes. • A binary tree restricts the number of children per node to a maximum of two. • Degenerate trees have only a single pathway from root to its one leaf.

7. Figure 3.1 Binary Tree

8. Graphs • Graphs are sometimes called a network or net. • A graph can have zero or more links between nodes – there is no distinction between parent and child. • Sometimes links have weights – weighted graph; or, arrows – directed graph. • Simple graphs have no loops – links that come back onto the node itself.

9. Graphs • A circuit (cycle) is a path through the graph beginning and ending with the same node. • Acyclic graphs have no cycles. • Connected graphs have links to all the nodes. • Digraphs are graphs with directed links. • Lattice is a directed acyclic graph. • A Degenerate tree is a tree with only a single path from the root to its one leaf.

10. Figure 3.2 Simple Graphs

11. Making Decisions • Trees / lattices are useful for classifying objects in a hierarchical nature. • Trees / lattices are useful for making decisions. • We refer to trees / lattices as structures. • Decision trees are useful for representing and reasoning about knowledge.

12. Binary Decision Trees • Every question takes us down one level in the tree. • A binary decision tree having N nodes: • All leaves will be answers. • All internal nodes are questions. • There will be a maximum of 2N answers for N questions. • Decision trees can be self learning. • Decision trees can be translated into production rules.

13. Decision Tree Example

14. State and Problem Spaces • A state space can be used to define an object’s behavior. • Different states refer to characteristics that define the status of the object. • A state space shows the transitions an object can make in going from one state to another.

15. Finite State Machine • A FSM is a diagram describing the finite number of states of a machine. • At any one time, the machine is in one particular state. • The machine accepts input and progresses to the next state. • FSMs are often used in compilers and validity checking programs.

16. Using FSM to Solve Problems • Characterizing ill-structured problems – one having uncertainties. • Well-formed problems: • Explicit problem, goal, and operations are known • Deterministic – we are sure of the next state when an operator is applied to a state. • The problem space is bounded. • The states are discrete.

17. Figure 3.5 State Diagram for a Soft Drink Vending Machine Accepting Quarters (Q) and Nickels (N)

19. AND-OR Trees and Goals • 1990s, PROLOG was used for commercial applications in business and industry. • PROLOG uses backward chaining to divide problems into smaller problems and then solves them. • AND-OR trees also use backward chaining. • AND-OR-NOT lattices use logic gates to describe problems.

22. Types of Logic • Deduction – reasoning where conclusions must follow from premises (general to specific) • Induction – inference is from the specific case to the general • Intuition – no proven theory-Recognizing a pattern(unconsciously) ANN • Heuristics – rules of thumb based on experience • Generate and test – trial and error – often used to reach efficiency.

23. Types of Logic • Abduction – reasoning back from a true condition to the premises that may have caused the condition • Default – absence of specific knowledge • Autoepistemic – self-knowledge…The color of the sky as it appears to you. • Nonmonotonic – New evidence may invalidate previous knowledge • Analogy – inferring conclusions based on similarities with other situations  ANN • Commonsense knowledge – A combination of all based on our experience

24. Deductive Logic • Argument – group of statements where the last is justified on the basis of the previous ones • Deductive logic can determine the validity of an argument. • Syllogism – has two premises and one conclusion • Deductive argument – conclusions reached by following true premises must themselves be true

25. Syllogisms vs. Rules • Syllogism: • All basketball players are tall. • Jason is a basketball player. Jason is tall. • IF-THEN rule: IFAll basketball players are tall and Jason is a basketball player THENJason is tall.

26. Categorical Syllogism Premises and conclusions are defined using categorical statements of the form:

27. Categorical Syllogisms

28. Categorical Syllogisms

29. Proving the Validity of Syllogistic Arguments Using Venn Diagrams • If a class is empty, it is shaded. • Universal statements, A and E are always drawn before particular ones. • If a class has at least one member, mark it with an *. • If a statement does not specify in which of two adjacent classes an object exists, place an * on the line between the classes. • If an area has been shaded, no * can be put in it. Review pages 131 to 135

30. Proving the Validity of Syllogistic Arguments Using Venn Diagrams Invalid Valid

31. Proving the Validity of Syllogistic Arguments Using Venn Diagrams Valid

32. Rules of Inference • Venn diagrams are insufficient for complex arguments. • Syllogisms address only a small portion of the possible logical statements. • Propositional logic offers another means of describing arguments  Variables

33. Direct Reasoning Modus Ponens

34. Truth Table Modus Ponens

35. Some Rules of Inference (Modus Ponens)

36. Rules of Inference

37. The Modus Meanings

38. The Conditional and Its Variants

39. Requirements of a Formal System • An alphabet of symbols • A set of finite strings of these symbols, the wffs. • Axioms, the definitions of the system. • Rules of inference, which enable a wff to be deduced as the conclusion of a finite set of other wffs – axioms or other theorems of the logic system.

40. Requirements of a FS Continued • Completeness – every wff can either be proved or refuted. • The system must be sound – every theorem is a logically valid wff.

41. Logic Systems A logic system consists of four parts: • Alphabet: a set of basic symbols from which more complex sentences are made. • Syntax: a set of rules or operators for constructing expressions (sentences). • Semantics: for defining the meaning of the sentences • Inference rules: for constructing semantically equivalent but syntactically different sentences

42. WFF and Wang’s Propositional Theorem Proofer • Well Formed Formula for Propositional Calculus • Wang’s Propositional Theorem Proofer Handouts are provided in the class

43. Predicate Logic • Syllogistic logic can be completely described by predicate logic. • The Rule of Universal Instantiation states that an individual may be substituted for a universe. • Compared to propositional logic, it has predicates, universal and existential quantifies.

44. Predicate Logic The following types of symbols are allowed in predicate logic: • Terms • Predicates • Connectives • Quantifiers

45. Predicate Logic Terms: • Constant symbols: symbols, expressions, or entities which do not change during execution (e.g., true / false) • Variable symbols: represent entities that can change during execution • Function symbols: represent functions which process input values on a predefined list of parameters and obtain resulting values

46. Predicate Logic Predicates: • Predicate symbols: represent true/false-type relations between objects. Objects are represented by constant symbols.

47. Predicate Logic Connectives: • Conjunction • Disjunction • Negation • Implication • Equivalence … (same as for propositional calculus)

48. Predicate Logic Quantifiers: • valid for variable symbols • Existential quantifier: “There exists at least one value for x from its domain.” • Universal quantifier: “For all x in its domain.”

49. First-Order Logic • First-order logic allows quantified variables to refer to objects, but not to predicates or functions. • For applying an inference to a set of predicate expressions, the system has to process matches of expressions. • The process of matching is called unification.

50. PROLOGProgramming in Logic