TP 2623 : PERWAKILAN & PENAAKULAN PENGETAHUAN

1. TP 2623 : PERWAKILAN & PENAAKULAN PENGETAHUAN By : Shereena Arif Room : T2/8, Blok H Email : shereen@ftsm.ukm.my / shereen.ma@gmail.com Topic 1: Introduction

2. Learning Outcomes At the end of this topic, students will: • Acquire good background understanding of concepts relating to KR • Understand the concepts, definitions and principles of KR TP2623, shereen@ftsm.ukm.my

3. What is knowledge-based system (KBS)? • KBS is a knowledge intensive computer program that contains aspects of human knowledge and expertise to perform tasks ordinarily done by human expert. • Emerged from the artificial intelligence (AI) domain. TP2623, shereen@ftsm.ukm.my

4. What is Artificial Intelligence (AI)? • AI is a field which deals with the design of a program that makes it possible to perceive, reason and act. TP2623, shereen@ftsm.ukm.my

5. What is knowledge? • In 1970s, it was accepted that to make machines solve intellectual problems – had to know the solutions. • A machine has to have ‘knowledge’ i.e. ‘know how’ in some specific domain. • Knowledge is a theoretical or practical understanding of a subject or a domain. TP2623, shereen@ftsm.ukm.my

6. What is knowledge? • Concepts of the same category: • the meaning of a sentence • a belief • a knowledge • an information • They are about a property in some domain • something that can be true or false in the world • Ex. All humans are male or female • Ex. Today’s lecture takes place in BK5 TP2623, shereen@ftsm.ukm.my

7. What is knowledge? • The meaning of a descriptive natural language (NL) sentence is an information • Ex. The world is flat • Ex. The world is a sphere • Not all sentences have a property as meaning: • Ex. The truck grew white smell • Ex. Are there sentences that have meaning and do not describe a property of the world? • Ex. Search two of them on this slide! • Questions, commands, .. TP2623, shereen@ftsm.ukm.my

8. What is knowledge? • Information are omnipresent in human minds and reasoning • Humans store and process massive amounts of information • Humans maintain a knowledge base of information which they consider to be true in the actual world • But humans have many other stores of “information” TP2623, shereen@ftsm.ukm.my

9. What is knowledge? • Humans store other “informations”, not considered to be true in the world: • a knowledge : Ex. The world is a sphere • a belief: Ex. Osama Laden is now dead • a goal: Ex. I will be rich in 2012 • a hope: Ex. There will be peace in Palestine in 2013 • beliefs about beliefs: Ex. In the middle ages, all people believed the earth was flat • promises, obligations, . . . • These are called propositional attitudes TP2623, shereen@ftsm.ukm.my

10. Why do we need knowledge? • Humans compute information all the time: e.g. whenever interpreting speech or text • Humans evaluate truth of information: • Ex. Next lecture is about monkey and takes place in the National Zoo • Humans update their knowledge base with information accordingly TP2623, shereen@ftsm.ukm.my

11. Why do we need knowledge? • Humans can reason with information to solve problems and tasks • Ex. From “all KR lectures take place in room BK5”, infer “the lecture of next week takes place in BK5” • whether information are believed or not: • Ex. If Bin Laden is still alive, there will be another suicide bombing, this time in Paris. • Humans have goals and they use knowledge to compute plans of actions to reach goals • Ex. To attend the KR lecture, you came to BK5 located at Block B. • Ex. To become rich, I will create spin-off company TP2623, shereen@ftsm.ukm.my

12. Other sorts of knowledge • Defeasible knowledge • Ex. Birds can fly. • Probabilistic knowledge • Ex. Chances are high that tomorrow will be sunny. • Vague knowledge • Ex. The weather is beautiful. The house too. • Declarative versus Procedural Knowledge • Important issues in AI and psychology TP2623, shereen@ftsm.ukm.my

13. Types of Knowledge • Declarative • Procedural • Semantic • Episodic • Commonsense • Heuristic TP2623, shereen@ftsm.ukm.my

14. Declarative Knowledge • Surface level knowledge • Attributes and characteristics • Example: • describe this room TP2623, shereen@ftsm.ukm.my

15. Procedural Knowledge • Processes and Procedures • Skills • Example: • How did you get to this room • How do you drive a car TP2623, shereen@ftsm.ukm.my

16. Semantic Knowledge • Relations and connections • How things fit together • Example: • What do you think about when I say “room” and “door”? • The relationship between signs, symbols and what they represent TP2623, shereen@ftsm.ukm.my

17. Episodic Knowledge • Stories, cases, examples • Based on experiences • Example: • Tell me about your birthday celebration TP2623, shereen@ftsm.ukm.my

18. Commonsense Knowledge • General knowledge about the world • Obvious to most people • Built up over time • Example: • Buildings contain rooms • Rooms are smaller than buildings TP2623, shereen@ftsm.ukm.my

19. Heuristic Knowledge • Rule-of-thumb • Directional accuracy; doesn’t guarantee outcome • IF-THEN rules • Example: • If the lights are out, no one is home. TP2623, shereen@ftsm.ukm.my

20. Knowledge in KBSs Conventional Programming Knowledge-Based Systems Algorithms + Data Structures = Programs Knowledge + Inference = KBS TP2623, shereen@ftsm.ukm.my

21. Declarative & Procedural K. • Declarative info: properties about world; “declarative” means “descriptive” • The "what" or content of learning; knowing a piece of information - a fact, a concept, or a label. • Humans have lots of procedural knowledge • We remember certain procedures that yield useful goals • When the goal rises, we execute the procedure (without reasoning) • Easy and Fast!! • Old controversy II: Which knowledge is most important for AI? Declarative AI-languages (e.g. logics) versus procedural AI-languages (e.g. production systems). TP2623, shereen@ftsm.ukm.my

22. Procedural Knowledge • Ex. Goal: clean clothes; Your laundry procedure • Ex. Goal: to eat; your restaurant procedure, your menu procedure, . . . • Ex. Goal: to have information; your text or speech interpretation procedure • Ex. Calculation: 22 x 45 = 22 + 22 +……+22 (45 times) • Our procedure 2 2 x 4 5 1 1 0 8 8 9 9 0 TP2623, shereen@ftsm.ukm.my

23. Procedural Knowledge • What is a procedure? • A known pattern of actions • Subconscious processing: • Sometimes we cannot control executions of a procedure • Ex. Count the number of words in the following sentence without interpreting the sentence: “I said do not interpret this sentence!” TP2623, shereen@ftsm.ukm.my

24. What is knowledge representation? Many definitions…. • KR is the study of: • How knowledge about the world can be represented, & • What kinds of reasoning can be done with that knowledge • It is the process of focusing and representing only the essential part of real world problem into some representation. TP2623, shereen@ftsm.ukm.my

25. What is reasoning? • A method of thinking • Process of constructing new configurations (sentences) from old ones • proper reasoning ensures that the new configurations represent facts that actually follow from the facts that the old configurations represent • this relationship is called entailment and can be expressed asKB |= alpha • knowledge base KB entails the sentence alpha TP2623, shereen@ftsm.ukm.my

26. What is knowledge engineering? • The disciplines for developing/building KBSs. • The activities – transferring & transforming aspects of problem solving expertise from knowledge sources into a program or system. • A number of methodologies to support knowledge engineering activities have been proposed. TP2623, shereen@ftsm.ukm.my

27. Principles of KR • Five basic principles about KR & their role in artificial intelligence: • A KR is a surrogate • A KR is a set of ontological commitment • A KR is a fragmentary theory of intelligence reasoning • A KR is a medium for efficient computation • A KR is a medium of human expression TP2623, shereen@ftsm.ukm.my

28. KR is a surrogate • Physical objects, events and relationships which cannot be stored directly in a computer – represented by symbols – serve as a surrogates for the external things. TP2623, shereen@ftsm.ukm.my

29. Ontological commitments • Ontology is the study of existence • For a database of knowledge base, ontology determines the things that exist in the application domain • Those categories represents the ontological commitment of the designer TP2623, shereen@ftsm.ukm.my

30. Fragmentary theory of intelligence reasoning • To support reasoning about the things in a domain, a KR must describe their behavior and interaction. TP2623, shereen@ftsm.ukm.my

31. Medium for efficient computation • Besides representing knowledge, an AI system must encode knowledge in a form that can be processed efficiently TP2623, shereen@ftsm.ukm.my

32. Medium for human expression • A good KR language should facilitate communication between the knowledge engineer who understand AI and the domain experts who understand the application TP2623, shereen@ftsm.ukm.my

33. KR Technique • A number of early knowledge representation techniques are: • Rules • Semantic net • Frames • Logic TP2623, shereen@ftsm.ukm.my

34. What will you learn next….. • Logic (propositional + description) • Semantic Net • Frame • Rules • Knowledge engineering • Ontology and ontology engineering • Knowledge representation for the web – Semantic Web • Flex TP2623, shereen@ftsm.ukm.my

35. Pendekatan • Pengendalian Kursus • Kuliah & Makmal • Penilaian • Tugasan Makmal (30%) • Kehadiran (10%) • Peperiksaan akhir (60%) TP2623, shereen@ftsm.ukm.my

36. References Brachman, R.J. & Levesque, H. J. 2004. Knowledge Representation & Reasoning. New York: Morgan Kaufmann. Russell, S. & Norvig, P. 2003. Artificial Intelligence: A Modern Approach. New Jersey: Prentice Hall. Antoniou, G. & van Harmelen, F. 2004. A Semantic Web Primer. Cambridge: MIT Press Negnevistky, M. 2002. Artificial Intelligence. Harlow: Addison-Wesley. Sowa, J. F. and Dietz D. 1999. Knowledge Representation: Logical, Philosophical, and Computational Foundations. New York: Broke/Cole Pub. Lain-lain – artikel dalam jurnal dan sumber daripada Internet TP2623, shereen@ftsm.ukm.my

37. End of Topic 1 THANK YOU..