Introduction to Artificial Intelligence: Types, Characteristics, and Development of Expert Systems

1. INFSY540Information Resources in Management Lesson 9 Chapter 10 Artificial Intelligence & Expert Systems

2. Learning Objectives • Define “artificial intelligence” (AI) • Identify the major types of AI systems & provide an example of each • List the characteristics and basic components of expert systems • Identify at least 3 factors to consider in evaluating the development of an expert system • Outline & explain the steps in developing an expert system

3. How do AI persons think? What is AI? What is I?

4. Characteristics of Intelligence • Ability to Communicate • Creativity • Internal Knowledge • Ability to Learn • World Knowledge • Goal-Directed Behavior • Self Awareness

5. + Vision + Experience + Context A Hierarchical Model of Intelligence Wisdom Knowledge Information Data

6. What is Artificial Intelligence? • Good Question. There is no generally accepted definition of Artificial Intelligence. • Why? • In practice, it is an “umbrella term” • It is multidisciplinary • Technologies regularly enter and exit the AI “umbrella”

7. Artificial Intelligence What other disciplines have been involved in AI? AI is a Multi-Disciplinary Field Historically, AI practitioners came from diverse backgrounds in both “hard” and “soft” sciences. Psychology Cognitive Science Computer Science Engineering Linguistics

8. Brief History of AI • 1943 McCulloch & Pitts paper on neurons • 1950 Age of computer simulation begins • 1956 Cognitive AI & Neural Computing fields begin (Dartmouth Summer Research Conference) • 1957 Rosenblatt’s Perceptron • 1959 Widrow & Hoff’s MADALINE • 1960’s Growth, Progress and Excessive Hype in all of AI • 1969 Minsky & Papert’s critique of Perceptrons (Results in stunted growth of Neural Networks: 1969-1984) • 1986 Re-birth of Neural Networks • 1997 Deep Blue defeats reigning chess grandmaster

9. Turing’s Test Can the human on the left tell whether the output is coming from the computer or the human on the right?

10. Features of Artificial Intelligence • The use of computers to do symbolic reasoning • A focus on problems that do not respond to algorithmic solutions • Problem solving using inexact, missing, or poorly defined information • An effort to capture and manipulate the significant qualitative features of a situation rather than relying on numerical methods

11. Features of Artificial Intelligence • An attempt to deal with issues of semantic meaning as well as syntactic form • Answers that are neither exact or optimal, but are in some sense “sufficient” • The use of large amounts of domain-specific knowledge in solving problems • The use of meta-level knowledge to effect more sophisticated control of problem-solving strategies

12. Application Categories • Interpretation Inferring situation from observations • Prediction Inferring likely consequences of situation • Diagnosis Inferring malfunctions • Design Configuring objects under constraints • Planning Developing plans to achieve goals • Monitoring Comparing observations to plans • Debugging Prescribing remedies for malfunctions • Repair Executing a plan to administer a remedy • Instruction Diagnosing and correcting performance • Control Managing system behavior • Optimization Finding “best” solutions to problems

13. Some AI Technologies • Expert Systems • Neural Networks • Genetic Algorithms • Fuzzy Logic • Robotics • Natural-Language Processing • Intelligent Tutorials • Computer Vision • Virtual Reality • Game Playing

14. Some AI Technologies • Expert Systems: Diagnose, respond & act like a human expert • Neural Networks: Use data to predict outputs or interpret inputs • Genetic Algorithms: Use data to find “optimal” solutions • Fuzzy Logic: Facilitate solutions to human vagueness problems • Robotics: Mimic physical human processes • Natural-Language Processing: Mimic human communication • Intelligent Tutorials: Facilitate human learning • Computer Vision: Mimic human sensory(visual) process • Virtual Reality: Mimic human reality inside a computer • Game Playing: Beat humans in games, e.g. chess

15. Cognitive vs Biological AI • Cognitive-based Artificial Intelligence • Top Down approach • Attempts to model psychological processes • Concentrates on what the brain gets done • Biological-based Artificial Intelligence • Bottom Up approach • Attempts to model biological processes • Concentrates on how the brain works

16. Cognitive AI Tools: Expert Systems Natural Language Fuzzy Logic Intelligent Agents Intelligent Tutorials Planning Systems Virtual Reality Biological AI Tools Neural Networks Speech Recognition Computer Vision Genetic Algorithms Evolutionary Programming Machine Learning Robotics Cognitive vs Biological AI

17. What is Artificial Intelligence? • Some definitions of AI: • Eugene Charniak, “...the study of mental faculties through the use of computational models.” • Patrick Winston, “...the study of computations that make it possible to perceive, reason, and act.” • Steven Tanimoto, “...computational techniques for performing tasks that apparently require intelligence when performed by humans.” • David Parnas, “Artificial intelligence is to artificial flowers as natural intelligence is to natural flowers.”

18. Categories of AI Definitions Systems that: Think like humans Think rationally Act like humans Act rationally

19. What is Artificial Intelligence? • Artificial Intelligence: the art of making computers that behave like the ones in movies” Bill Bulko • Computers are useless. They can only give you answers.Pablo Picasso • Computers make it easier to do a lot things, but most of the things they make easier to do, don’t need to be done. Andy Rooney • The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.Edgar W. Dijkstra

20. Questions? • Suppose we develop an AI program so that it can score 200 on a standard IQ test. Would we then have a program more intelligent than a human? • “Surely computers cannot be intelligent-they can only do what their programmers tell them.” Is the latter statement true and does it imply the former? • “Surely animals cannot be intelligent-they can only do what their genes tell them.” Is the latter statement true and does it imply the former?

21. Predicting the Future: Mission Impossible? • I think there’s a world market for about 5 computers.Thomas J. Watson, Chairman of the Board, IBM, 1948 • There is no reason for any individual to have a computer in his home. Ken Olson, President, Digital Equipment, 1977

22. Future AI Technologies • Will need to do more than just mimic humans to improve computer intelligence. • For example, examine products for defects under light and sound frequencies that human experts cannot observe. • Will need to focus on creating computer programs that can learn and teach other computer programs.

23. Future AI Technologies • Automatic Programming • Evolutionary Programming • Knowledge Based Systems • Biological Artificial Neural Networks • Real Time Planning and Re-Planning Systems • Intelligent “learning” Agents • Micro, mini and nano robots • Biometric Security Systems • Quantum computing

24. Why Should We Care about AI? • Moving from the industrial age to the information age has created a whole new world of problems. There are many very difficult problems in this new world that an AI way of thinking might help solve. • Information overload problems. • Operations in hazardous environments. • Distributing scarce corporate knowledge. • Problems requiring multidisciplinary teams.

25. Any questions?

26. Knowledge Based Systems (KBS) and Expert Systems (ES)

27. Expert System • A model and associated procedure that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. (From Introduction to Expert Systems by Ignizio) • An expert system is a computer system which emulates the decision-making ability of a human expert. (From Expert Systems: Principles and Programming by Giarratano and Riley) • Problem solving programs that usually have an explanation facility and are rich in heuristics.

28. Characteristics of an Expert System • Can explain reasoning • Can provide portable knowledge • Can display “intelligent” behavior • Can draw conclusions from complex relationships • Can deal with uncertainty

29. What distinguishes a KBS from an expert system? • Size of the knowledge base • Reuse of the knowledge • Generality of the knowledge • Large-scale integrated architectures with multiple reasoning strategies

30. Why use a KBS or ES? • Preserve knowledge--builds up the corporate memory of an organization. • Makes expertise more widely available, even if scarce or expensive. • Frees expert from repetitive, routine tasks. • Aids in imparting expertise to novices. • Improves worker productivity. • Explore alternatives -- provides a second opinion in critical situations.

31. When to use a KBS or ES? • Domain is knowledge intensive, and can be modeled with logical rules • Not a natural-language intensive problem • Neither creativity nor physical skills are required • Optimal results are not required • Subject matter experts are available for knowledge acquisition

32. When to use a KBS or ES? • High payoff • Preserve scarce expertise • Distribute expertise • Provide more consistency than humans • Faster solutions than humans • Training expertise

33. Components of KBS and ES • Essential • Knowledge base • Inference engine • Supporting • KB editor • Query interface • Explanation system

34. Fig 11.7

35. Inference Engine • Human reasoning inspires similar reasoning strategies in AI: • Classification • Rules • Heuristics • Prior cases • Expectations

36. Classification • We create and use categories to organize knowledge Animal Vertebrate Invertebrate Reptile Amphibian Mammal Fish

37. Rules • Mostly take the form IF-THEN • Rules can be cascaded, nested • "If A then B" . . . • "If B then C" • A-->B-->C • Order of evaluation may matter

38. Heuristics • “Rules of thumb” • Heuristics can be captured using rules • "If the meal includes red meat • Then choose red wine" • If the TV reception is bad • Then jiggle the antenna • Can be extremely helpful in AI applications

39. Prior Cases • Exemplified in case-based reasoning • e.g. legal precedents • Similarity of current case to previous cases provides basis for action choice • Cases stored and retrieved based on features and structure • Similarities and differences are the basis for reasoning

40. Inference Engine • Controls overall execution of the “rules”. • Descriptions of the Strategies • Forward Chaining • Derive new facts from existing facts • “Who killed the cat?” • Backward Chaining • Ask if a particular hypothesis is valid. (Goal-directed inference) • “Did curiosity kill the cat?” • Can combine the strategies

41. Knowledge Base • Uses a representation language to formalize knowledge • Context: Organizes domain into a model of entities and relationships that make up that domain. • Rules: Logical statements that govern the inference about the entities and relationships • attempt to replicate the thought process used by the expert. • Two methods of designing the rules: Rule-Based Reasoning and Case-Based Reasoning

42. Knowledge Base • Rule-based Reasoning • Uses logical rules to guide inference. • 1. If you are 150 yds. away and in the fairway, then select the 7-iron. • 2. If you are in the rough, then use the next lower-numbered club. • If you start with (150yds, rough), then by applying the above two rules you will get 6-iron as output. • The rules operate on beliefs and assumptions in the reasoning context

43. Knowledge Base • Case-based Reasoning • Look at all related facts as a “case”, seek to find similar cases to guide inference • Reason based on the similarities and differences. • Example, 1st step, using same problem: • Case 1: 170 yds., in fairway; used a 5-iron. • Case 2: 160 yds., in fairway; used a 6-iron. • Case 3: 150 yds., in fairway; used a 7-iron. • (150 yds., rough) is probably closest to Case 3.

44. Knowledge Base • Case-based Reasoning (second step): • Apply rules about what doesn’t match the case: • a. If the situation is “fairway” and the case is for “rough”, then use the next higher-numbered club. • b. If the situation is “rough” and the case is for “fairway”, then use the next lower-numbered club. • Since the situation is “rough” and Case 3 (the best matching case) is for “fairway”, we would apply the b. rule above to derive our answer of 6-iron.

45. Knowledge Base • Rule-Based and Case-Based Reasoning are equivalent: Any rule-based system can be rewritten in case-based form, and vice versa. • Using one over the other depends on how the experts do their job: • Rule-based: Do they look at one piece of data at a time? • Case-based: Do they generally reason about the data in a “big picture” way?

46. Applications of Expert Systems & KBS • Credit granting • Shipping • Information management & retrieval • Embedded systems • Help desks & assistance

47. Application Categories:Interpretation • Urban Search and Rescue robots • Interprets information about collapsed buildings • Helps identify potential locations of trapped victims. • ES is programmed into the robot exploring the inside of the building looking for “void spaces”. • Colorado School of Mines

48. Application Categories:Interpretation • Bridge Classification • The “Smart Bridge” project allows planners to classify bridges according to capacity: • Load Classification (weight, throughput,...) • Clearance Restrictions • Operates using remote imagery (photographs, satellite images)

49. Application Categories:Diagnosis & Repair • Turbine Engine Vibration Diagnosis • Takes acoustic spectrum from a running a turbine engine. • Irregular components of the signal patterns are identified. • Mechanic is pointed towards possible faults.

50. The US Army AI Center’s Favorite Photo The single locked box at the soldier’s feet replaces the stack of manuals and the tower of test equipment shown.