CSM6120 Introduction to Intelligent Systems

1. CSM6120Introduction to Intelligent Systems Knowledge representation

2. What is knowledge?

3. Representation • AI agents deal with knowledge (data) • Facts (believe & observe knowledge) • Procedures (“how to” knowledge) • Meaning (relate & define knowledge) • Right representation is crucial • Early realisation in AI • Wrong choice can lead to project failure • Active research area

4. Choosing a representation • For certain problem solving techniques • ‘Best’ representation already known • Often a requirement of the technique • Or a requirement of the programming language (e.g. Prolog) • Examples • First order theorem proving… first order logic • Inductive logic programming… logic programs • Neural networks learning… neural networks • Some general representation schemes • Suitable for many different (and new) AI applications

5. Knowledge representation • Representation of • Declarative knowledge (what, objects, structure) • Procedural knowledge (how, actions, performance) • Representation formalisms • Declarative knowledge • Frames, Semantic Networks, Inheritance Hierarchies, Schemata,... • Procedural knowledge • Algorithms, Procedures, Plans, Rules,...

6. What is a Logic? • A language with concrete rules • No ambiguity in representation (may be other errors!) • Allows unambiguous communication and processing • Very unlike natural languages e.g. English • Many ways to translate between languages • A statement can be represented in different logics • And perhaps differently in same logic • Not to be confused with logical reasoning • Logics are languages, reasoning is a process (may use logic)

7. What is a Logic? • Knowledge can be represented using first-order predicate logic • More on this later in the module

8. Non-logical representations • Logic representations have restrictions and can be hard to work with • Many AI researchers searched for better representations • Non-logical • Semantic networks • Conceptual graphs • Frames • Scripts • Production rules • ...

9. What we’ve ignored • Objects in the world tend to be related to each other • Classes, superclasses & subclasses • Part / whole hierarchies • Properties are inherited across relationships • The state of the world can change over time • Explicit representation of time • Frame problem • Non-monotonic reasoning • We must reason without complete knowledge Closed world assumption • Not all knowledge is “black & white” (later modules on CSM…) • Uncertainty • Statistics, fuzzy logic

10. Classes • “I want to buy a basketball” • I want to buy BB27341 (NO) • I want to buy an object that is a member of the basketball class (YES) • Objects organized into a hierarchy by class • BB27341  basketballs • basketballs  balls • Facts (objects) & rules (classes) • All balls are round • All basketballs are <size> in diameter • BB27341 is red, white and blue • BB27341 is a basketball • (Therefore BB27341 is round and <size> in diameter)

11. Inheritance • If a property is true of a class, it is true of all subclasses of that class • If a property is true of a class, it is true of all objects that are members of that class • (If a property is true of a class, it is true of all objects that are members of subclasses of that class) • There are exceptions (to be dealt with later…)

12. Part/whole inheritance • A cow has 4 legs • Each leg is part of the cow • The cow is in the field • All of the cow’s parts are also in the field • The cow is (entirely) brown • All of the cow’s parts are brown • The cow is happy • All of the cow’s parts are happy (?) • Note: some properties are inherited by parts, others are not. This is generally made explicit by rules, such as • part-of(x,y) and location(y,z) -> location(x,z)

13. Defaults and exceptions • Exception for a single object • Set a property of the object to the (exception) value • Default for a class • Set a property of the class to the (default) value • If there are multiple default values, the one “closest” to the object wins

14. Example • All birds can fly • Birds with broken wings are birds, but cannot fly • Penguins are birds, but they cannot fly • Magical penguins are penguins that can fly • Who can fly? • Tweety is a bird • Peter is a penguin • Penelope is a magical penguin • Note that beliefs can be changed as new information comes in that changes the classification of an object

15. Semantic networks • Semantic networks are essentially a generalization of inheritance hierarchies • Each node is an object or class • Each link is a relationship • is-a (the usual subclass or element relationship) • has-part or part-of • Any other relationship that makes sense in context • Note: semantic networks pre-dated OOP • Inheritance: follow one member-of link, as many subclass or other links as necessary

16. Graphical representation • Graphs easy to store in a computer • To be of any use must impose a formalism • Jason is 15, Bryan is 40, Arthur is 70, Jim is 74 • How old is Julia?

17. Semantic networks • Because the syntax is the same, we can guess that Julia’s age is similar to Bryan’s

18. Semantic networks • Knowledge represented as a network or graph has-part Animal head subclass subclass Reptile Mammal subclass lives-in Elephant Africa large size instance apples Nellie likes

19. Semantic networks • By traversing network we can find: • That Nellie has a head (by inheritance) • That certain concepts related in certain ways (e.g., apples and elephants) • But: meaning of semantic networks not always well defined • Are all Elephants large, or just typical elephants? • Do all Elephants live in the “same” Africa? • Do all animals have the same head? • For machine processing these things must be defined

20. Frames • Devised by Marvin Minsky, 1974 • Incorporates certain valuable human thinking characteristics: • Expectations, assumptions, stereotypes. Exceptions. Fuzzy boundaries between classes • The essence of this form of knowledge representation is typicality, with exceptions, rather than definition

21. Frames • Frames allow more convenient “packaging” of facts about an object • We use the terms “slots” and “slot values” mammal: subclass: animal elephant: subclass: mammal size: large has-part: trunk Nellie: instance: elephant likes: apples

22. Frames • Frames often allowed you to say which things were just typical of a class, and which were definitional, so couldn’t be overridden • Frames also allow multiple inheritance (Nellie is an Elephant and is a circus animal) Elephant: subclass: mammal has-part: trunk * colour: grey * size: large

23. Frame representations • Semantic networks where nodes have structure • Frame with a number of slots (age, height, ...) • Each slot stores specific item of information • When agent faces a new situation • Slots can be filled in (value may be another frame) • Filling in may trigger actions • May trigger retrieval of other frames • Inheritance of properties between frames • Very similar to objects in OOP

24. Example: Frame representation

25. Flexibility in frames • Slots in a frame can contain • Information for choosing a frame in a situation • Relationships between this and other frames • Procedures to carry out after various slots filled • Default information to use where input is missing • Blank slots: left blank unless required for a task • Other frames, which gives a hierarchy • Can also be expressed in first order logic

26. How frames are organised • In the higher levels of the frame hierarchy, typical knowledge about the class is stored • The value in a slot may be a range or a condition • In the lower levels, the value in a slot may be a specific value, to overwrite the value which would otherwise be inherited from a higher frame • Note that a frames system may allow multiple inheritance but, if it does so, it must make provision for cases when inherited values conflict...

27. Multiple inheritance and views • Multiple inheritance • Sub-concept inherits descriptions from several superconcepts • Possibly conflicting information ( = ambiguity) • Skepticalreasoners: “don’t know” (no conclusion) • Credulous reasoners: “whatever” (several conclusions) • Views • Description of concept from different viewpoints • Inheritance of multiple, complementing descriptions • e.g. view computer as machine or as equipment

28. Multiple inheritance - views

29. Multiple inheritance - ambiguity Person subclass subclass pacifist non-pacifist Quaker Republican instance instance Nixon

30. Other varieties of structured object • Knowledge representation researchers - particularly Roger Schank and his associates - devised some interesting variations on the theme of structured objects • In particular, they invented the idea of scripts (1973) • A script is a description of a class of events in terms of contexts, participants, and sub-events

31. Scripts • Rather similar to frames: uses inheritance and slots; describes stereotypical knowledge • (i.e. if the system isn't told some detail of what's going on, it assumes the "default" information is true) • but concerned with events • These are somewhat out of the mainstream of expert systems work • More a development of natural-language-processing research

32. Scripts • Why represent knowledge in this way? • Because real-world events follow stereotyped patterns • Human beings use previous experience to understand verbal accounts; computers can use scripts instead • Because people, when relating events, leave large amounts of assumed detail out of their accounts • People don't find it easy to converse with a system that can't fill in missing conversational detail

33. Non-monotonic logic • Once true (or false) does not mean always true (or false) • As information arrives, truth values can change (Penelope is a bird, penguin, magic penguin) • Implementations • Circumscription • Bird(x) and not abnormal(x) -> flies(x) • We can assume not abnormal(x) unless we know abnormal(x) • Default logic • “x is true given x does not conflict with anything we already know”

34. Truth maintenance systems • These systems allow truth values to be changed during reasoning (belief revision) • When we retract a fact, we must also retract any other fact that was derived from it • Penelope is a bird (can fly) • Penelope is a penguin (cannot fly) • Penelope is magical (can fly) • Retract (Penelope is magical) (cannot fly) • Retract (Penelope is a penguin) (can fly)

35. Types of TMS • Justification-based TMS • For each fact, track its justification (facts and rules from which it was derived) • When a fact is retracted, retract all facts that have justifications leading back to that fact, unless they have independent justifications • Assumption-based TMS • Represent all possible states simultaneously • For each fact, use list of assumptions that make that fact true; each world state is a set of assumptions • When a fact is retracted, change state sets

36. Rule-based systems

37. Structured objects: final comments • At best, structured objects provide data & control structures which are more flexible than those in conventional languages, and so more suited to simulating human reasoning • However, if we just want an AI system to act rationally (rather than think/act humanly), then logic might be better...

38. Resources • A good introduction to the area: • http://www.cse.buffalo.edu/~shapiro/Papers/kr.pdf