II - Cognitive approach

1. II - Cognitive approach A - Knowledge representation

2. 1 - Representation • People understand the world by building mental models

3. 2 - Knowledge • Knowledge is an abstract concept. To memorize or use a piece of information, it is necessary to represent it. We must use a language. It’s the same for a computer system, in order to memorize and use knowledge, it is necessary to represent it as constructed as possible. • Knowledge representation is necessary to • keep knowledge, • exchange knowledge (to acquire, transmit), • modify knowledge, • use knowledge. • No perfect representation exists, every time it’s a compromise it depends on the field dealt with and the use.

4. 2.1 - Different levels of knowledge • Structure • The carburettor is a part of the engine. • Classification • Bicycle is a kind of means of transportation. • Definition • Prime numbers are only divisible by one and themselves. • Law • The intensity multiplied by the voltage gives the impedance. • Rule • If the engine stops and ignition is correct you must suspect the fuel. • Strategy • When the patient is a child, consider infantile diseases first

5. 2.2 - Knowledge and reasoning • The transcription of a knowledge into a symbolic language has to allow its utilisation by a reasoning system.Therefore, reasoning associates two overlapped aspects : • The structure of data representing the problem • The associated method to use the information. • The reasoning mechanism will allow to deduce dynamically new knowledge. • Here is a great difference between a database and a knowledge based system. A database doesn’t give you anything, but the things that have been explicitly taught.

6. 2.3 - Knowledge representation families

7. First example : the factorial function Figure 2.1 - Factorial function definition, both in procedural and declarative manners

8. Second example : Adjective accordance in French Figure 2.2 - Adjective accordance

9. 3 - Representation in logics3.1 - Propositional calculus • A proposition is an assertion (with a given syntax) that can only be valued in two ways : right or wrong (true or false). • Example : Figure 2.3 - “John is a man” represents “John is a man”, assertion that can be true or false (here, we will assume it’s true) • A predicate can have several arguments : • job (jean, architect) • Temperature (machine2,lower_pump, 50) • Following (task2, task1) • ...

10. 3.2 - Lack of propositional calculus • This logic isn’t powerful enough to represent properties for a group of persons. • For example how can we tell that ‘all sailors live near the sea’ without saying it for each of them ?

11. 3.3 - Predicate calculus • First order predicate calculus can be seen as an extension to propositional calculus. • It has the same kernel, but adds • The use of variables • The ability of quantify them • Interpretation • There exists, at least one x for which P is true : $x P(x) • Q is true for all the x in the domain : "x Q(x) • Well formed formula, in predicate calculus, can have two states, just like in propositional calculus : true or false.

12. Examples • $Y (dog(Y) Ù dangerous(Y)) • "XYZ (father(X,Y) Ù father(Y,Z) É grandfather(X,Z)) (It reminds me of something) • "S (job(S,sailor) É live(S,sea))

13. Reasoning in predicate calculus

14. 3.4 - Fuzzy logics • Uncertain reasoning, fuzzy reasoning, incomplete reasoning… all those problem have their own logics. • Three prisoners, five hats (modal logics)

15. 4 - Semantic networks (Quillian 68)4.1 - Introduction • This formalism comes from work on memory organisation. • Semantic networks are especially used in natural language. • In a semantic network, knowledge is figured by a graph, nodes stand for concepts, links for relations between concepts. • Two relations are preferred : • Is a kind of (AKO) < • Is a (ISA) Î Figure 2.4 - “Bagheraa is a panther”

16. 4.2 - Reasoning in semantic networks • Reasoning in a semantic network is a graph extraction. It’s a structural correspondence. • Thus, It is possible, when looking at the graph below to answer the question : “Where does the person, john gave the book to, live ?” Figure 2.7 - Reasoning in a SN.

17. II - Cognitive approach B - How does a Knowledge Based System work ?

18. 6 - Rule based system6.1- Introduction

19. 6 - Rule based system6.1- Introduction (2) • Each rule has the following syntax IF <premises> THEN <conclusions> Which means that when all premises are true, it is possible to deduce that the conclusions are also true. • Knowledge is given to the system in a rule form (in a declarative way). • There is a separation between the knowledge (the rules) and its utilisation (the inference engine)

20. 6.2 - The inference engine • The inference engine order has the same level as the logical representation. • Most of nowadays inference engine are‘first order’. • Figure 2.9 - Inference engine order

21. 6.3 - Functioning of an inference engine

22. 6.3.1 - Forward chaining • The reasoning is led by data, this functioning is also called ‘saturation with the fact base’. • For example in a medical diagnose we could have... • patient-age = 15 years old • temperature = normal • headache = awful …and the system would look for all possible deductions.

23. Milou uses forward chaining

24. Three steps of forward chaining

25. Example • FB : A,E, I • RB : 1 - if A and B then C 2 - if B then K 3 - if G and A then B 4 - if I et E then B 5 - if E then J 6 - if J and K then F 7 - if E and D then G

26. Functioning of an inference engine • Cycle Rules Chosen Activated Facts rule rules base { } stops the engine. The final FB is : AEIBCKJF (all these facts are true)

27. 6.3.2 - Backward chaining • It’s a type of reasoning driven by a goal (hypothesis verification) • Example • Patient is a child • He’s got red spots on his face • He’s got a temperature Verify “The child has a measles” • A goal is given, the inference engine tries to activate rules that conclude towards this goal. The premises of these rules become new goals to be solved.

28. Milou uses backward chaining

29. Example inbackward chaining • With the same facts base as in forward chaining, let us try to prove the fact F. Figure 2.13 - The AND/OR tree used in backward chaining

30. (formal) Explanations

31. (industrial) ExplanationsPeugeot-Citroën’s expert system : SIRIUS

32. (industrial) Explanations (2)Peugeot-Citroën’s expert system : SIRIUS

33. 6.3.3 - “Mixt” chaining • Some rule based systems can use both chaining : forward and backward. Just like a human who sometimes tries to deduce things without aims or who may also want to prove something before going further in his reasoning.

34. 6.4 - Nota-bene • All examples given were taken from the propositional calculus. In the predicate calculus, inferences are more difficult because a same rule can be activated more than once. • The following rule written in the predicate calculus formalism : • IF FATHER(x,y) AND FATHER(y,z) THEN GRANFATHER(x,z) • Will be activated …… times on this fact base : • FATHER(John,Louis) • FATHER(John,Paul) • FATHER(Paul,Herbert) • FATHER(Louis,Yann) • FATHER(Arthur, John)

35. 6.5 - Advantages • Each rule is one part of the knowledge.It has to be independent from other rules. • Modifications are easy • It’s easy to follow the reasoning built by the machine • Explanations are given to human • The use of other logics (fuzzy for example) can introduce the idea of weight in rules

36. 7 - Expert Systems (E.S.)7.1 - History • Expert Systems (ES) comes from rule based systems, but add other reasoning techniques or knowledge representation formalisms. • The first known is DENDRAL (1965, Feigenbaum, Stanford University). It was able to determine the chemical structure of an element from its chemical formula and its mass spectogram. • It was an enormous program written in Fortran. Every change in knowledge was very difficult to make, because physics knowledge and reasoning were closely linked in the code-program.

37. 7 - Expert system (E.S.)7.1 - History (2) • The idea that has given birth to ES was to separate specific knowledge from the way of using it. • This idea is the foundation of all Expert Systems. • The idea ‘came to life’ with • MYCIN (1976, Shortliffe) blood illnesses diagnosis. • PROSPECTOR (1979) Geology expert system

38. 7.2 - MYCIN & E-MYCIN

39. 7.3 - Expert System - Definition • Software able to reach performances equal to a human, on a precise area, using a set of knowledge acquired from one or several experts of the considered area. • It is composed of (as a production system) • an inference engine (IE) • a fact base (FB) • a rule base RB) • One often finds, integrated to the IE: • a heuristic choice strategy of rules • a meta-knowledge (ex : meta-rule for the choice of other rules). • Feigenbaum’s definition • ES are programs conceived to reason cleverly about tasks which are believed to require a considerable human expertise.

40. 7.4 - Advantages • Separation between knowledge and mechanism (algorithm) • A piece of knowledge is a production rule • knowledge can be ‘read’ • construction and evolution are easy • explanation capacity by examining the series of rules that were used.

41. 7.5 - Applications • ES bring assistance on problem resolution with the following characteristics: • There is a great quantity of knowledge • The knowledge on the area will change • The symbolic calculation (by opposition to the numerical calculation) is the most important.

42. 7.6 - Teaching • Expert Systems can also be found in teaching. Not only does it know the solution of the exercise, but it also knows the method of resolution, which enables to explain it all.

43. 7.7 - TD

44. II - Cognitive Approach C - Knowledge acquisition, knowledgemanagement

45. 8 - Context8.1 - Motivation in the choice of an ESS • The problem deals with qualitative information (not only quantitative). • Knowledge is not only knowledge of good sense, but it’s got something intuitive. • There exists one or severalrecognized experts, motivated and available, that are able to solve the problem and to explain their way of reasoning. • Economic reasons are not the only ones in favour of using ESS : • The expertise is rare or fragile (overworked expert, leaving expert, retirement...) • Decisions have to be made in difficult situations (quickly, when it’s hard to decide, stress...)

46. 8.2 - Steps of an SE development • Step 1: demonstrator • Development of a model (demonstrator) on a part of the problem studied. The purpose is to prove that the problem can be solved with AI techniques. • Step 2: prototype • Development of a prototype, that integrates a complete knowledge base, but that doesn’t care about the real environment (i.e. : without interfaces). Tests. • Step 3: integration • Integration of the product in its final environment. Interfaces with other software (bases on data, CAD programs, I/O processing…) that were simulated on the prototype are now effective. • Step 4: maintenance • In the case of knowledge that changes, updating the knowledge base, while keeping the coherence with the operational base.

47. 9 - Human means • An ES development needs two actors (or two groups of actors) • an expert of the area considered • a knowledge engineer • These two actors will work together to develop the knowledge base. The expert, that knows perfectly his problem, will answer the knowledge engineer’s questions, but above all, will explain his answers by giving the reasoning that drove him to them.

48. 9.1 - Difference between an Expert and a Specialist

49. Questions, problems Formalisation Answers, knowledge Help 9.2 - Work • The knowledge engineer creates a link between the expert and the machine, he mainly works with the expert, whose behaviour he observes and analysis behaviour, and with a computer to formalise knowledge information (with objects, rules...). Figure 2.14 - The task of a knowledge engineer

50. 9.3 - Four steps in knowledge formalisation (not counting methodological documents like specifications…) • Step 1 : Interviews with the expert, in order to determine the nature of reasoning. • useful to choose the engine order, 0 for diagnosis, 1 for scheduling…) • Step 2 : Note all the descriptors and concepts used by the expert. • for example, when using an object representation, it enables to create classes, attributes, the hierarchy… • Step 3 : Transcribe in the chosen language (depending on the ESS), the knowledge that is useful to solve the problem. • Step 4 : Correct and validate in presenting the same (new) problem to both the expert and to the expert system. Compare results and explanations.