Knowledge Based Systems

1. Knowledge Based Systems Khurshid Ahmad, Professor of Computer Science, Department of Computer Science Trinity College, Dublin-2, IRELAND October 6th /12th , 2010. https://www.cs.tcd.ie/Khurshid.Ahmad/Teaching/Teaching.html 1

2. Knowledge Based Systems A computer program which, with its associated data, embodies organised knowledge concerning some specific area of human activity. Such a system is expected to perform competently, skilfully and in a cost-effective manner; it may be thought of as a computer program which mimics the performance of a human expert.

3. Knowledge Based Systems The Nature of Expertise One view of human expertise is that some people have spent so much time solving problems in one particular domain that they ‘know all there is to know’ (nearly) and are able to see any problem as an instance of a class of problems with which they have been confronted before. Once the expert has successfully classified or recognised a new problem as an instance of a previously experienced problem type, all the expert has to do is apply whatever solution proved successful in dealing with that type of problem in the past.

4. Knowledge Based Systems The Nature of Expertise This model of human expertise that relies (a) on domain-specific knowledge and (b) experience-based recognition of solutions of problems, has served as the basis of numerous expert systems. The idea that cognition is a ‘recognition’-based phenomenon. That is, when a medical doctor, say, examines a patient or hears what the patient has to say about his or her problem, the configuration of symptoms or signs suggests a particular illness with which the doctor is already completely familiar.

5. Knowledge Based Systems The Nature of Expertise The ‘recognition-based’ phenomenon can be viewed as setting up a key goal in the problem solving process and then attempt to find out data that satisfies the goal. The goal is then broken up into sub-goals and data sought to prove each of the sub-goals. (The sub-goals can be broken into further sub-goals and so on and on). While proving a sub-goal, the experts suspend the goal and try and satisfy individual goals. Once all sub-goals are satisfied then the key goal is deemed to be satisfied and the problem solved!

6. Knowledge Based Systems:Condition-Action Rules Pneumonia Expert System Consider a small chunk of a medical experts knowledge about diagnosing pneumonia and fever rulepneumonia if ‘the patient has chest pain’ & ‘the patient has a fever’ & ‘the patient produces purulent sputum’ then ‘the patient has pneumonia’ rulefever if ‘the patient has a temperature above 100’ then ‘the patient has a fever’. The nurse has just come in a with a patient with the following symptoms: ‘the patient has a chest pain’ & ‘the patient has a temperature above 100’& ‘the patient produces purulent sputum’ And the expert has to deduce whether or not the patient has pneumonia?

7. Knowledge Based Systems:Condition-Action Rules Pneumonia Expert System The nurse has just come in a with a patient with the following symptoms: ‘the patient has a chest pain’ & ‘the patient has a temperature above 100’& ‘the patient produces purulent sputum’ And the expert has to deduce whether or not the patient has pneumonia? In order to prove whether or not the patient has pneumonia, the expert has to prove: prove ‘the patient has pneumonia’  GOAL prove ‘the patient has a chest pain’  SUB-GOAL prove ‘the patient has a fever’ SUB-GOAL prove ‘the patient produces purulent sputum’ SUB-GOAL

8. Knowledge Based Systems:Condition-Action Rules • Intelligent beings perceive, reason and act.  • Intelligent beings are creative, learn from their mistakes.  • Intelligent beings can learn from their environment.  • Intelligent beings can learn with the help of tutors.  • Intelligent beings can work on their own/form groups.  • Intelligent beings have a value system, an exchange system.

9. Knowledge Based Systems:Classical Case Studies MYCIN, microbial infection therapy system comprised over 400 rules of thumb such as the above which help in microbial infection therapy, for example in the diagnosis and therapy of meningitis. IF: 1) The stain of the organism is grampos, and 2) The morphology of the organism is coccus, and 3) The growth conformation of the organism is chains THEN: There is suggestive evidence (0.7) that the identity of the organism is streptococcus. Each of the antecedent clauses and the consequent clause is essentially a complex relationship between the terms of this subject: the stain, morphology and growth, conformation of an organism and its identity and the attributes of these = grampos, coccus, chains, streptococcus. The rules show the interrelationship between various domain objects ('the stain' etc.) and how such a relationship could be used to infer new facts. The objects and their attributes, (for example, "morphology" and its attribute "coccus") viewed here as a data structure, are each viewed by the terminologist as terms representing concepts.

10. Knowledge Based Systems:Classical Case Studies The expert system R1/XCON was used by DEC(now a part of COMPAQ) for configuring computer systems. This system was in routine commercial use and contains over 10,000 rules of the type shown below IF: The most current active context is assigning a power supply & an sbi module of any type has been put in a cabinet & the position it occupies in the cabinet (its nexus) is known & there is space available in the cabinet for a power supply for that nexus & there is an available power supply THEN: put the power supply in the cabinet in the available space. The above rule is much more complex than the rule shown for MYCIN in that although we are talking about putting the power supply in the cabinet in the available space, each of the domain objects and its attributes is a complex entity in itself: for example an SBI moduleof any type, the power supply and the available space in the cabinet.

11. Knowledge Based Systems:An input-output Typology

12. U S E R I N T E R F A C E A L G O R I T H M (S) D A T A B A S E Data Facts C O M P U T E R S Y S T E M COMPUTER SYSTEM Knowledge Based Systems:Architecture A conventional computing system

13. Data Facts Knowledge Based Systems:Architecture A conventional computing system U S E R I N T E R F A C E H E U R I S T I C S & A L G O R I T H M (S) K N O W L E D G E B A S E Rules Meta Rules C O M P U T E R S Y S T E M COMPUTER SYSTEM

14. Knowledge Based Systems:Architecture A computer program which, with its associated data, embodies organised knowledge concerning some specific area of human activity. Such a system is expected to perform competently, skilfully and in a cost-effective manner; it may be thought of as a computer program which mimics the performance of a human expert.

15. Knowledge Based Systems:Knowledge base An organised and structured repository of ‘human knowledge’ in a given specialist domain. This repository can be updated or deleted in parts. Usually the knowledge is encoded as conditional ‘if <antecedent> then <consequent>‘ statements (‘rules’ and ‘problem-solving tasks’) together with the so-called ‘domain objects’. The latter are descriptions of facts, including concrete and abstract facts, relating to the specialist domain.

16. Knowledge Based Systems:Knowledge base Knowledge representation is about making things explicit, is about resolving ambiguities; Knowledge representation, in the context of artificial intelligence, is about describing a class of things to a computer system. This description should not be ambiguous either lexically or structurally This description should explicate shared knowledge

17. Knowledge Based Systems:Reasoning Strategies • Reasoning may be characterised as an attempt to combine elements of old information to form new information. • Reasoning strategies refer to the rather long sequences of individual small inferences organised so as to address a main goal or problem. • Reasoning strategies involve the representation of information and knowledge, the use of inference rules for manipulating that knowledge & information, and control mechanisms for making the variety of choices necessary in the search for solutions.

18. Knowledge Based Systems:Problem Solving • Problem-solving is sometimes defined as a process that involves finding or constructing a solution to a problem. • Human problem solving can be modelled as the exploration of different paths to a solution and involves ‘information processing’ which appears unique to human beings. • Cognitive psychologists typically divide problems into two classes: ‘well defined’ or closed world problems including solutions of games or puzzles and ‘ill defined’ or open world problems.

19. Knowledge Based Systems:Heuristics or Rules of Thumb Prevent the hi-jacking of airliners Prevent hi-jackers from boarding the airliners Heuristic technique Algorithmic route • Put passengers and luggage through a metal detector & • Search only those who set off the detector • Search those passengers that match a predetermined hi-jacker profile • (inc. passengers, flight crews & mechanics) • Strip search every person with access to the airlines & • Search all luggage

20. Knowledge Based Systems & Conventional Systems Conventional Programs KBES Representation data knowledge Reasoning algorithmic & repetitive heuristic & inferential Retrieves large DB large KB Knowledge encrypted represented

21. Knowledge Based Systems & Conventional Systems Data-driven programming. Conventional if-then conditionals are executed only when the programmer directs the flow of control to them; they are if-now-then-now statements. If-then rules in data driven expert systems execute whenever patterns in the current data state warrant it, acting as if-ever-then-now agents independent of their order in the rule base. Such matching occurs on data patterns (forward chaining) and on goals identified by the system or user as reasoning process elements (backward chaining). This relieves programmers of responsibility for explicitly anticipating all eventualities that programs might encounter and when such eventualities might occur David Leinweber (1988). Knowledge-Based Systems for Financial Applications. IEEE Expert, Fall 1988, pp 18-31.

22. Knowledge Based Systems: Applications Areas • There are recognised experts • The experts are provably better than novices • The tasks takes an expert a few minutes to a few hours (if it takes days - FORGET IT) • The task is primarily cognitive • The skill is routinely taught to novices • The task requires no common sense • The task domain is important: economically, financially or socially

23. Knowledge Based Systems and Finance KNOWLEDGE-BASED technologies became a critical part of the information technology base of many financial firms, some of which are facing two issues: First, decomposable, highly specialised nature of tasks, and second the growth of legacy systems Michel Benaroch (1997). Toward the Notion of a Knowledge Repository for Financial Risk Management. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 9, NO. 1, JANUARY-FEBRUARY 1997 pp 161-167.

24. Knowledge Based Systems and Finance KNOWLEDGE-BASED technologies became a critical part of the information technology base of many financial firms, some of which are facing two issues: First, due to the ample size of most financial application tasks and their fairly decomposable nature, these firms’ portfolio of knowledge-based systems (KBS) grew to include many small systems that solve isolated subtasks using varied technologies (production rules, neural networks, fuzzy logic, etc.). This complicates the integration of systems in the course of solving entire application tasks. Michel Benaroch (1997). Toward the Notion of a Knowledge Repository for Financial Risk Management. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 9, NO. 1, JANUARY-FEBRUARY 1997 pp 161-167.

25. Knowledge Based Systems and Finance KNOWLEDGE-BASED technologies became a critical part of the information technology base of many financial firms, some of which are facing two issues: Second, most firms’ portfolios of KBSs grew to include redundant systems; in large firms, many groups are working apart to solve different tasks that have common subtasks, e.g., prediction, for which solutions often end up being reinvented and reimplemented. Michel Benaroch (1997). Toward the Notion of a Knowledge Repository for Financial Risk Management. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 9, NO. 1, JANUARY-FEBRUARY 1997 pp 161-167.

26. Knowledge Based Systems and Knowledge Engineering The accumulation, codification and application of knowledge through the use of computer systems, specifically knowledge-based systems.

27. Knowledge Based Systems and Knowledge Engineering

28. Rule-based Systems • A rule-based system helps us to codify the problem-solving knowledge of the human expert. • It appears that experts typically express their knowledge as a set of situation-action rules. • RBS research should address the need to capture, represent, store, distribute, reason about and apply human knowledge electronically. Hayes Roth, F. (1992). ‘Rule-Based Systems’ p.1426

29. Rule-based Systems:Production Systems • Production Systems are a modular knowledge representation scheme and are based on the notion of condition-actionpairs, called production rules or just productions: "If this condition occurs, then do this action". • The utility of the production system formalism comes from the fact that the conditions in which each rule is applicable are made explicit and, in theory at least, the interactions between rules are minimised in the sense that the rules do not 'call' each other.

30. Rule-based Systems:Production Systems Consider a knowledge-base containing the following rules: Rule#1:IFA&B&CTHEN D Rule#2: IFD&FTHEN G Rule#3: IFA&JTHEN G Rule#4: IFBTHEN C Rule#5: IFFTHEN B Rule#6: IFLTHEN J Rule#7: IFGTHEN H. The knowledge-base also contains the following facts Fact#1: A. (‘A’ is known to be true) Fact#2 : F. (‘F’ is known to be true)

31. Rule-based Systems:Production Systems

32. Rule-based Systems:Production Systems An Example Problem: To prove that H is true?  Cycles of Production: FIRST CYCLE

33. Rule-based Systems:Production Systems An Example Problem: To prove that H is true?  Cycles of Production: SECOND CYCLE

34. Rule-based Systems:Production Systems Consider a knowledge-base containing the following rules: Rule#1:IFA&B&CTHEN D Rule#2: IFD&FTHEN G Rule#3: IFA&JTHEN G Rule#4: IFBTHEN C Rule#5: IFFTHEN B Rule#6: IFLTHEN J Rule#7: IFGTHEN H. The knowledge-base also contains the following facts Fact#1: A. (‘A’ is known to be true) Fact#2 : F. (‘F’ is known to be true)

35. Rule-based Systems:Production Systems An Example Problem: To prove that H is true?  Cycles of Production: THIRD CYCLE

36. Rule-based Systems:Production Systems An Example Problem: To prove that H is true?  Cycles of Production: FOURTH CYCLE

37. Rule-based Systems:Production Systems Consider a knowledge-base containing the following rules: Rule#1:IFA&B&CTHEN D Rule#2: IFD&FTHEN G Rule#3: IFA&JTHEN G Rule#4: IFBTHEN C Rule#5: IFFTHEN B Rule#6: IFLTHEN J Rule#7: IFGTHEN H. The knowledge-base also contains the following facts Fact#1: A. (‘A’ is known to be true) Fact#2 : F. (‘F’ is known to be true)

38. Rule-based Systems:Production Systems An Example Problem: To prove that H is true?  Cycles of Production: FIFTH CYCLE

39. Rule-based Systems:Production Systems An Example Problem: To prove that H is true?  Cycles of Production: SIXTH CYCLE

40. Rule-based Systems:Production Systems Conflict Resolution Specificity The specificity principle states that if a number of rules are applicable to a given situation then the rule with the greatest number of condition premises should be selected to fire. Refractoriness Refractoriness is another conflict resolution strategy which states that if a rule has been applied on a previous cycle, then it should not be applied again to the same set of facts in data memories. This kind of strategy prevents a system from getting itself entwined in a loop. Recency The recency principle is a conflict resolution strategy which states that if more than one rule applies to a given situation, then choose the rule that applies to the most recently entered items in data memory.

41. Rule-based Systems:Production Systems Conflict Resolution To begin with only one rule would be applicable: the initialisation_rule, because its single pattern start matches the single element (start) in working memory: rule initialisation_rule If start Then add ‘kettle is empty’ & add ‘kettle is plugged in’ & add ‘sugar required’ & add ‘milk required’ & add ‘teacup is empty’ & add ‘kettle is switched off’. Although it is the only rule in the conflict set, this rule will be tested for specificity, refractiveness and recency.

42. Knowledge Based Systems: Deduction Systems Problem: Elicit rules from the following description Johnny is an amateur zookeeper and also keeps notes in his diary on the various animals and birds he comes in contact with. His diary contains the following entries: 'Jan 1, 1992: Mary told me that meat-eating animals with pointed teeth, forward pointing eyes and claws are called carnivores. Like other mammals they give milk.' 'Feb. 15, 1992: I saw two tawny coloured carnivores today, but one had dark spots and the other black strips. The dark spotted carnivore was called cheetah and the black stripped was a tiger.' 'March 20, 1992: We had two new animals delivered to the zoo, a zebra and a giraffe. These hoofed mammals are called ungulates. The zebra has white skin with black strips on it. The giraffe had long legs and neck, and has the same tawny colour as the tiger but with black spots'.

43. Knowledge Based Systems: Deduction Systems

44. Knowledge Based Systems: Deduction Systems Johnny has been asked to identify a hoofed, long-necked hairy animal, Freddy, which has a tawny colour and dark spots. Use the forward chaining search strategy to draw the inference network which Johnny should use to identify the unknown animal. Fired 1st Gives milk Is a mammal Fired 2nd Has hoofs Is an ungulate Has long legs Fired 3rd Has a long neck Is a giraffe Has a tawny colour Has dark spots

45. Knowledge Based Systems: Deduction Systems

46. FUZZY LOGIC & FUZZY SYSTEMS Knowledge Representation & Reasoning Some apply model-driven approaches, e.g., qualitative reasoning with economic models [6] and rule-based inferencing with experts’ heuristics [12]. Others use data-driven approaches, e.g., neural network techniques [13] and genetic algorithms [2] applied with historical data. Michel Benaroch (1997). Toward the Notion of a Knowledge Repository for Financial Risk Management. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 9, NO. 1, JANUARY-FEBRUARY 1997 pp 161-167.

47. Knowledge Based Systems & Decision Support Systems A decision support system is a computer program used by managers, planners, bureaucrats, and entrepreneurs, to support decision making by using a collection of software (and communications) systems.

48. Decision Support SystemsTerminology Decision Typology • Structured- Accounts receivable, Budget analysis, Warehouse location • Semi-structured - Production scheduling, Credit Evaluation, Design/Build • Unstructured - Software Procurement, Negotiation, Socio-economic planning Control Typology • Operational Control Accounts receivable, Production scheduling, Software Procurement • Managerial Control Budget analysis, Credit Evaluation, Negotiation • Strategic Planning Warehouse location, Design/Build, Socio- economic planning

49. Decision Support Systems- A typology EIS: Enterprise Information Systems; AI: Artificial Intelligence

50. Decision Support Systems- A potted history 1970-80’s • A model-based set of procedures for processing data and judgements to assist a manager in his [sic] decision making 1980-90’ss • An extendible system capable of supporting ad-hoc data analysis and decision modeling and used irregularly/unplanned • DSS applies to situations where a final system emerges through adaptation and learning 1990’s and 2000’s • A DSS is an interactive, flexible, and adaptable computer-based information system developed for supporting the solution of non-structured management problems for improved decision making, and can incorporate the decision makers’ own insights.