Knowledge-based Systems 2005 - 2006

1. Knowledge-based Systems2005 - 2006 Lecture 1:Introduction to KBS Lora Aroyo

2. Organizational Issues • Lecturer • Lora Aroyo • KBS@listserver.tue.nl • Lecture time • Wednesday, 8:45 - 10:30, AUD 6 • No lectures on: • 19 April • 10 May • 24 May Lecture 1

3. Course Setting • face-to-face lectures • individual literature study • Final Assignment • no exam • all correspondence via KBS@listserver.tue.nl Lecture 1

4. Final Assignment • Work in groups of 3 students • FA contains: • Work plan – task definition and distribution among group members • Design – solution description, motivation for the solution, modeling, design and architecture summary of your assignment • Implementation – implementation details, motivation for the implementation, URL of your running implementation and related documentation • Submit: • via KBS@listserver.tue.nl • in the file names indicate clearly the following info: • Group No, Course Name, Student names + numbers • by 7th July 2006, 17:00 CET Lecture 1

5. Course Materials • Knowledge Systems, Mark Stefik • Chapters 1, 2, 3, (4, 5, 6), 7 and 8 • Prolog: Programming for Artificial Intelligence, Ivan Bratko • Lecture slides & notes • Additional reference material • online tutorials & reference manuals • Web links • KBS related articles Lecture 1

6. Course Goal • Learn basics of KBS • Show how does the computer reason • Show how to model reasoning systems • Explore examples of industrial applications of KBS • Get familiar with requirements engineering for KBS applications • Brief introduction to Prolog for KBS • Brief introduction to Semantic Languages for KBS • Get an overall view on the field Lecture 1

7. Course Lectures Overview http://www.win.tue.nl/~laroyo/2L340/2005-2006/index.html • Introduction to KBS • Knowledge Representation & Reasoning • Knowledge-based Automation in Practice • Rule-based Systems • Knowledge Management in practice • Practical Navigation for the Semantic Web • User Modelling & Recommender Systems Lecture 1

8. Brief Look in the History • 60’s: Artificial Intelligence • General search methods • Little domain-specific knowledge • Fast initial success • Later: disappointment (overselling AI) • 70’s: Expert Systems • A lot of domain specific knowledge • Applicable in specialized tasks Lecture 1

9. Brief Look in the History • 80’s: Disappointment in Expert Systems • Too high expectations • Renaming to KBS • 90’s: Internet & Knowledge Management • AI integrated in business • Knowledge Management • Internet • 00’s: NLP, Telecommunications, Data Mining, Semantic Web • Ontologies and markup languages Lecture 1

10. Motivation • Facilitate computers to deal with knowledge • acquire, represent, reason • reason about problems rather than calculate a solution • Quantity of knowledge increases rapidly • Relieve humans from tedious tasks • Some knowledge-related tasks are better and easier solved by computers Lecture 1

11. KBS Applications • Medicine • diagnosis & solution • discovery & analysis • Geology • analysis of data • Computer support • configuration support for mainframe computers • Justice • Services – payments • Scheduling tasks • Education and Training Lecture 1

12. KBS Applications Examples • KLM– flight schedule construction • 112 – decision support for ambulances; Roadway Incident Management • Amsterdam KIOSK – intelligent online call center • Hitachi (500 to 600 systems for customers) • Process scheduling in chemical plants • Banking and financial diagnostic systems • Toshiba (500 systems for both internal and external use) • Fault diagnosis system - faults/restores operation to an electric power system • SMART-7 - Diagnostic system for a subway station facility • MARKETS-I - DSS for suitability of opening a convenience store at a site • ESCORT - banking operations advisor system • NEC (1,000 KBS) • COSMOS/AI - crew scheduling system for Japan Air Lines • Software debugging advisor • Mitsubishi Electric - KBS for elevator group control Lecture 1

13. Industrial interest in KBS • Consulting services • Improve corporate processes • Integration of KBS technology with conventional information technology • data processing or management information systems Lecture 1

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15. Intelligent Devices Examples • Consumer Electronics Show 2006 • http://www.cesweb.org/default_flash.asp • http://metahost.savvislive.com/microsoft/20060104/ces_billgates_keynote_20060104_300.asx • 11:50 – future scenario – ambient intelligence • 51:00 - TV and personalized ads • 59:00 - media center • 1:05:00 - recommendations and VOD • Smartkom Project • Demo video • Mobile Shop Assistant Project • Demo video Lecture 1

16. Google Local Mobile • http://www.google.com/glm/ Lecture 1

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18. HITACHI Human Interaction Lab • Display tables • Interactive displays Lecture 1

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22. Things can go wrong • http://www.adcritic.com/interactive/assets/aclu-pizza/ Lecture 1

23. KBS is … • Software system, which represents (explicit, declarative description of knowledge) and uses this knowledge to accomplish a task within the context of a certain application (aka Knowledge System) • Behaves intelligent • Automation and reuse of knowledge Lecture 1

24. KBS in general • Knowledge acquisition • transfer of knowledge from humans to computers • knowledge acquired directly from the environment • machine learning • Knowledge representation • storing and processing knowledge in computers • Inference • mechanism to generate new knowledge from existing • Explanation • Feedback to the user how & why a solution was generated Lecture 1

25. KBS concepts • Domain– subject domain of the KBS • Task– set of goals that to be fulfilled by system (e.g. diagnosis, assessment, classification, configuration, and planning). A process is collection of tasks (knowledge intensive tasks) • Agent - executor of a task (human or software) • Application - the context of the executable task within a specific domain Lecture 1

26. Knowledge-intensive Tasks Knowledge-intensive task Synthetic task Analytic task Prediction Design Monitoring Modeling Diagnosis Planning Assessment Scheduling Assignment Classification Lecture 1 Adopted from Speel, et al, 2001

27. KBS components • Goal– input knowledge  output knowledge (‘what’) • Methods- how to reach goals • inference steps in knowledge processing • control structure (methods library) • Domain Knowledge- knowledge processed by the methods Lecture 1

28. KBS Architecture • Separation • the knowledge • the inference engine • Knowledge base– knowledge representation • Inference Engine– rules to use knowledge to accomplish a task • User Interface– presentation of knowledge results • Reuse and maintenance: • Same inference over different knowledge • Same knowledge with different inference Inference Engine User Interface Knowledge base Input Data Lecture 1

29. KBS Construction feedback Use Supporting tools and methods Tools worldview takes shape in a theorythat describes the basic conceptsand models of the approach Methods Theory worldview relates to the general approach of knowledge engineering World view Adopted from Speel, et al, 2001 Lecture 1

30. KBS is NOT … • Information System • Knowledge = information + use • Use depends on task or goal • Database System • Information = data + structure • Structure provided by application domain • Expert System • old term for a knowledge system • Knowledge system is not replacing the expert but supports it Lecture 1

31. Conventional Knowledge-Based Systems Conventional  KB Systems Algorithms + Data Structures = Programs Knowledge + Inference = KBS N. Wirth Lecture 1

32. When (Not) to Use KBS • KBS are not suitable for all domains & tasks • conventional algorithms - efficient • main challenge - computation, not knowledge • knowledge - captured difficult • users may be reluctant to apply a KBS to a critical task Lecture 1

33. Meta- Knowledge– the integration of the information into a knowledge base to be effectively utilized Knowledge Knowledge Integration and Usage Information Information– the interpretation of artifacts in some context Information Interpretation in Context Data Data– collected symbols and artifacts Data Noise Knowledge Pyramid Knowledge - assigns a purpose and/or action to information Information - interpreted data“within a context set by a priori knowledge and the current environment” Data - raw digital material or the “artifacts which exist as a vehicle for conveying information” Lecture 1

34. Data • Articfacts - e.g. writing, images, sounds • Physical – marks on a sheet of paper • Electronic – bit patterns in a computer memory • Others – electro-chemical potentials in the brain • In KBS we need to support • Various media formats and data representations • Data must adhere to some structure, which allows the required information to be extracted Lecture 1

35. Information • The interpretation of data in a context set by a priori knowledge and current environment • Priori knowledge (e.g. language) • Current environment and context (e.g. why, where, how) • Identifying mappings from concepts which already exist in our knowledge base to the information captured by the data • In KBS we need to support the • extraction/storage/usage of the a priori users knowledge • creation of the data-information mappings at all levels Lecture 1

36. Knowledge • The base of personal information integratedin a way to be used in further interpretation and analysis of data • The mappings from data symbols to concepts are only useful if we are able to use them in some way further • direct usage to make a decision or carry out action • in the augmentation of out knowledge base • In KBS we need to support the • integration of information • consideration for further usage Lecture 1

37. Knowledge Based Systems The goal is to facilitate intelligent interaction with the user based on: • the identification of the appropriate information • the effective utilization of the appropriate information • the user control of the appropriate information in order to fulfill specific user goals Lecture 1

38. Knowledge in KBS • Most important & labor heavy task in KBS construction: • the experience of an expert • the transcription of this experience in KBS • methods- knowledge that describes how to perform an intellectual process • domain knowledge- that represents what is manipulated by the methods Lecture 1

39. Representation & Reasoning • Knowledge= description of the knowledge in an system independent of the symbol-level representation • Knowledge is gathered by: • observing actions • stored as facts =‘knows’ something • tell & ask functional interface, logical assertions and queries • knowledge representation language: • syntax • describes the possible configurations that can constitute sentences • semantics • determines the facts in the world to which the sentences refer • tells us what the agent believes • Symbols = Physical (written or printed) mark or pattern representing something in a selected medium • Organized in symbol structures • Symbol level = program level Lecture 1

40. Knowledge & Symbol Level Observation creates a 'what' model of system’s behaviour (set of system’s activities) Lecture 1

41. Knowledge & Symbol Level Rationalisation creates a 'why' model of system’s behaviour, in terms of world knowledge Mechanisation creates a 'how' model of system’s behaviour, in terms of system structure and interaction rules Lecture 1

42. Knowledge & Symbol levels Actor Observer Goals Knowledge behavior assign rationalize Actions Environment Observer model observe Knowledge mechanize Symbol System Symbol System Lecture 1

43. Semantics • Systematic relationship between symbols (signs), interpreter or model (signifier) and referents (signified) • Types of Semantics: • Referential– symbols refer to objects in the domain by interpretation • Denotational– maps symbols onto a description of computation Lecture 1

44. Knowledge Representation (KR) • Symbol system - encodes a body of knowledge representation knowledge access symbols concepts relationships representation formalism computational process storing and retrieving knowledge Lecture 1

45. KR Methods • Rule-based Systems capture knowledge in the form of structured if-then statements • Model-Based Reasoning uses software models to capture knowledge or to emulate real processes • Neural Nets are a network of nodes and connections used to capture knowledge, they can "learn" by using examples • Fuzzy Logic is used to represent and manipulate knowledge that is incomplete or imprecise • Decision Trees capture decision-making knowledge that can be expressed as sets of order decisions. Lecture 1

46. Rule-based Systems • use rules as knowledge representation • if-then statements • popular and intuitive knowledge representation • suitable for constraint knowledge (conditions) & pattern matching Lecture 1

47. Model based reasoning • initially developed to support industrial processes: • oil refining • chemical processes • mathematical model that mimics the real process • the model is used to predict outcomes of control actions • a powerful knowledge representation • capture processes • useful for decision support system • Challenge: ensuring the model has proper fidelity and captures important characteristics of the process modeled Lecture 1

48. Neural Nets • model the behavior of brain tissue using software • good at associative problems • given partial information, an associative problem is to find items that "fit with" (i.e. are associated with) the given information • birds are small animals with feathers that fly. Given a feathered animal that flies, we can find associated information - animal likely to be a bird and is probably small • can be trained by example • knowledge is stored as a pattern of weights distributed across all the connections between individual neurons Lecture 1

49. Graphs & Trees • Semantic network • Concept map • Flat/ no inheritance • Ontologies • inheritance • Relations • spatial, temporal, causal • Tree • hierarchy, classification • Semantic network • concepts and relations Lecture 1

50. Causal network in simple graph Iron Deficiency Weak immune system Influenza Fever Tooth inflammation Lymph nodes inflammation Bacterial nest Headache Internal bleeding Hematom Lecture 1