1. rkj@aber.ac.uk CSM6120 Introduction 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 in diameter  BB27341 is red, white and blue  BB27341 is a basketball  (Therefore BB27341 is round and 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.  Knowledge represented as a network or graph subclass has-part subclass instance likes size Animal Reptile Elephant Nellie Mammal apples large head Africa lives-in Semantic networks

19.  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 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 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)  Skeptical reasoners: “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 non-pacifist Nixon RepublicanQuaker pacifist subclass instance

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 http://www.cse.buffalo.edu/~shapiro/Papers/kr.pdf