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INFSY540Information Resources in
ManagementLesson 9
Chapter 10Artificial Intelligence & Expert Systems
Chapter 10Slide 2
Learning ObjectivesDefine “artificial intelligence” (AI)Identify the major types of AI systems & provide an example of each List the characteristics and basic components of expert systemsIdentify at least 3 factors to consider in evaluating the development of an expert systemOutline & explain the steps in developing an expert system
Chapter 10Slide 4
Characteristics of Intelligence
Ability to Communicate
Creativity
Internal Knowledge
Ability to Learn
World Knowledge
Goal-Directed Behavior
Self Awareness
Chapter 10Slide 5
A Hierarchical Model of Intelligence
Wisdom
Knowledge
Information
Data Context+
Vision+Experience+
Chapter 10Slide 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 multidisciplinaryTechnologies regularly enter and exit the AI
“umbrella”
Chapter 10Slide 7
AI is a Multi-Disciplinary FieldHistorically, AI practitioners
came from diverse backgrounds in both
“hard” and “soft” sciences.
CognitiveScience
Linguistics Engineering
Psychology
Artificial Intelligence
Computer Science
What other disciplines have been involved in AI?
Chapter 10Slide 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
Chapter 10Slide 9
Turing’s Test
Can the human on the left tell whether the output iscoming from the computer or the human on the right?
Chapter 10Slide 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
Chapter 10Slide 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
Chapter 10Slide 12
Application CategoriesInterpretation 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
Chapter 10Slide 13
Some AI Technologies
Expert Systems
Neural Networks
Genetic Algorithms
Fuzzy Logic
Robotics
Natural-Language Processing
Intelligent Tutorials
Computer Vision
Virtual Reality
Game Playing
Chapter 10Slide 14
Some AI TechnologiesExpert 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
Chapter 10Slide 15
Cognitive vs Biological AI
Cognitive-based Artificial Intelligence Top Down approachAttempts to model psychological processesConcentrates on what the brain gets done
Biological-based Artificial IntelligenceBottom Up approachAttempts to model biological processesConcentrates on how the brain works
Chapter 10Slide 16
Cognitive vs Biological AI
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
Chapter 10Slide 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.”
Chapter 10Slide 18
Categories of AI Definitions
Systems that:
Think like humans Think rationally
Act like humans Act rationally
Chapter 10Slide 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
Chapter 10Slide 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?
Chapter 10Slide 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
Chapter 10Slide 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.
Chapter 10Slide 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
Chapter 10Slide 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.
Chapter 10Slide 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.
Chapter 10Slide 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
Chapter 10Slide 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
Chapter 10Slide 30
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.
Why use a KBS or ES?
Chapter 10Slide 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
Chapter 10Slide 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
Chapter 10Slide 40
Components of KBS and ES
EssentialKnowledge base Inference engine
SupportingKB editorQuery interface Explanation system
Chapter 10Slide 43
Inference Engine
Human reasoning inspires similar reasoning strategies in AI:ClassificationRulesHeuristicsPrior casesExpectations
Chapter 10Slide 44
Classification
We create and use categories to organize knowledge
Animal
Vertebrate Invertebrate
Fish
Reptile Amphibian Mammal
Chapter 10Slide 45
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
Chapter 10Slide 46
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
Chapter 10Slide 47
Prior Cases
Exemplified in case-based reasoninge.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
Chapter 10Slide 50
Inference Engine
Controls overall execution of the “rules”.
Descriptions of the StrategiesForward Chaining
Derive new facts from existing facts“Who killed the cat?”
Backward ChainingAsk if a particular hypothesis is valid. (Goal-directed
inference)“Did curiosity kill the cat?”
Can combine the strategies
Chapter 10Slide 53
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
Chapter 10Slide 54
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
Chapter 10Slide 55
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.
Chapter 10Slide 56
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.
Chapter 10Slide 57
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?
Chapter 10Slide 60
Applications of Expert Systems & KBS
Credit granting
Shipping
Information management & retrieval
Embedded systems
Help desks & assistance
Chapter 10Slide 61
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
Chapter 10Slide 62
Application Categories:Interpretation
Bridge ClassificationThe “Smart Bridge” project allows planners to
classify bridges according to capacity:Load Classification (weight, throughput,...)Clearance Restrictions
Operates using remote imagery (photographs, satellite images)
Chapter 10Slide 63
Application Categories:Diagnosis & Repair
Turbine Engine Vibration DiagnosisTakes acoustic spectrum from a running a
turbine engine. Irregular components of the signal patterns are
identified. Mechanic is pointed towards possible faults.
Chapter 10Slide 64
The US Army AI Center’s Favorite Photo
The single locked box at the soldier’s feet replaces the stack ofmanuals and the tower of test equipment shown.
Chapter 10Slide 71
Limitations of Knowledge Based SystemsLimited to narrow problems
Not widely used or tested
Hard to use
Cannot easily deal with “mixed” knowledge
Possibility of error
Cannot refine own knowledge base
Hard to maintain
Possible high development costs
Raise legal & ethical concerns
Chapter 10Slide 72
Advantages of Expert Systems Shells and ProductsEasy to develop & modify
Use of satisficing
Use of heuristics
Development by knowledge engineers & users
Chapter 10Slide 73
Procedural Computing
Conventional software programming paradigm relies on procedural computing over data:Program = Algorithm + Data
Algorithm is a series of tasks that the computer must perform, such as:
read a numbermultiply by 10display the resultetc…
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