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Lecture5 Expert Systems and Artificial Intelligence 1204583743868830 4

Nov 29, 2014

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Expert Systems and Artificial Intelligence

the expert is the one who has made the most mistakes in the domain of interest

Reading: Chapter 7Modified from Marakas 2003

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The Concept of Expertise Expertise: extensive knowledge in a narrow field

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Gained by making mistakes Arrive at solutions through logic Logic establishes the True/Falsity of assertions IF-THEN to arrive at conclusions Derive new information from existing to arrive at conclusions Deductions and inference are used to establish how well facts fit a scenario Experts use this information fit to arrive at their decisions

Example of information fit

Known information

John is Sams son John is the eldest child Mary is Sams daughter John and Marys mother is called Anna Sam has been married to Anna for 50 years If John is Sams son, THEN John must be a boy If Sam and Anna have been married for 50 years, THEN John and Mary are their children by either birth or adoption

Derived information

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Expert Sytems & Artificial Intelligence

Expert systems: a computer application that employs a set of rules based on human knowledge to solve problems that require human expertise

Imitates reasoning of experts on information fit A non-expert simulates a dialog with an expert to solve complex problems

Artificial Intelligence: practical mechanisms that enable computers to simulate the human reasoning process

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Interface of compute science/cognitive psychology the study of how to make computers do things which humans do better the Turing Test?

The Intelligence of Artificial Intelligence How do people reason? (the realm of cog. psych.)

Land transport personal commercial

Categorization

Can reason at levels of abstraction Rules derived from relationships among categories car Specific Rules

Heuristics

bike Motor bike

bus

taxi

Less rigorous rules of thumb Use a similar scenario to model new one (benefit of 20/20 hindsight) Frequency, pattern recognition

Past Experience

Expectations

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So can computers simulate these processes of reasoning?

How Do Computers Reason?

Rule-based reasoning: IF-THEN statements represent knowledge encoded as rules

If TRUE rule is instantiated, otherwise ignored

Pattern recognition: detecting sounds, shapes or long sequences

Analogous to Expectation, similar conditions

Frames: Object-oriented approach of creating hierarchical data structures analogous to categorisation databasey!! Case-based reasoning: adapting previous solutions to a current problem

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Types of IF Then

Inferential

If premise THEN conclusion If snowing THEN drive with caution If situation THEN action If Average grade =A THEN award 1st class degree If Antecedent THEN consequent If student has mitigating circumstances THEN award incomplete grade

Procedural

Declarative

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The CBR CycleRetrievalNew problem

New problem New solution

A Memory Knowledge Base

Retrieve from memory

Retain Confirm solution Revise Suggest solution

Reuse

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Other Forms of AI

Machine learning neural networks and genetic algorithms (learning mechanisms) Automatic programming mechanisms that generate a program to do a specific task (allows non-programmers to program)

User describes inputs and generates a program

Artificial life attempts to recreate biological phenomena within computer-based systems

As opposed to dissecting frogs! Transfer to design of engineering projects (software, spacecraft, robotics etc)

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Neural Network

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Train it? supervised Or just let it get on with it? unsupervised

Genetic Algorithm

crossover mutation

Used in scheduling (timetabling?), design, marketing

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The Concept and Structure of Expert Systems

Basic structure of an ES follows the generic structure of a DSS

User interface, Knowledge base, inference engine

The knowledge base is specific to a particular problem domain associated with the ES The main difference between an ES and DSS is that the ES contains knowledge acquired from experts in the application domain

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Common Expert System Architecture

UserUser Interface

Knowledge Engineer

KE Interface

Organization Systems Interface

Inference Engine

KE Tool Kit

Knowledge Base

User Environment

Development Environment

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The User Interface in an ES

Design of the UI focuses on human concerns such as ease of use, reliability and reduction of fatigue

Critical to its success Balance with storage capacity / hardware constraints

Design should allow for a variety of methods of interaction (input, control and query) UI should allow for a variety of interactive mechanisms:

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touch screen, keypad, light pens, voice command, hot keys

The Knowledge Base the Brains

Contains the domain-specific knowledge acquired from the domain experts Can consist of object descriptions, problemsolving behaviors, constraints, heuristics and uncertainties The success of an ES relies on the completeness and accuracy of its knowledge base

Distinguish a database (data facts) from a knowledge base (experts rules, cases, etc)

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The Inference Engine the brawn!

Here, the knowledge is put to use to produce solutions The engine is capable of performing deduction or inference based on rules or facts Also capable of using inexact or fuzzy reasoning based on probability or pattern matching Cycle consists of:1. 2. 3.

Match rules with given facts Select the rule that is to be executed Execute the rule by adding the deduced fact to the working memory

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Chaining

Simple methods used by most inference engines to produce a line of reasoning Two methods are possible depending on the direction of reasoning Forward chaining: the engine begins with the initial content of the workspace and proceeds toward a final conclusion Backward chaining: the engine starts with a goal and finds knowledge to support that goal

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Forward Chaining ExampleSuppose we have three rules: R1: If A and B then D R2: If B then C R3: If C and D then E If facts A and B are present, we infer D from R1 and infer C from R2. With D and C inferred, we now infer E from R3.

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Backward Chaining ExampleThe same three rules: R1: If A and B then D R2: If B then C R3: If C and D then E If E is known, then R3 implies C and D are true. R2 thus implies B is true (from C) and R1 implies A and B are true (from D).

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Real Chaining Example

Which method to choose? Backward or forward? Situation

You wish to fly from Warsaw to Glasgow but all direct flights are full Check which cities flights arriving in Glasgow are from and keep going backwards Check destinations from Warsaw and then check their destinations until you find a flight going to Glasgow

Backward chain

Forward chaining

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Expert Systems Shells

Expert System Shells: generic systems that contain reasoning mechanisms but not the problem-specific knowledge Early shells were cumbersome but still allowed the user to avoid having to completely program the system from scratch Modern shells contain two primary modules: a rule set builder (to construct the initial knowledge base) and an inference engine (as the vehicle for arriving at conclusions)

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Building an Expert System

An early step is to identify the type of tasks

Interpretation, predicting likely consequences Diagnosis of faults, monitoring expected outcomes User guidance E.g. to replace scarce expert resources Provide an expert assistant To free up experts onto other tasks Look for activities where experts are:

Key awareness is to recognise opportunities

Overburdened Undersupplied Expensive Unavailable

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Candidates

Diagnosis, prediction, control, evaluation, prescription of solution

Pre-design Activities

Another important step is choosing the experts who will contribute knowledge

Involved throughout the development Not be threatened, rather enhanced

Development team should have a functional understanding of the knowledge domain Unlike more general information systems design projects, the software tools and hardware platform are selected very early

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Benefits of Expert Systems

Increased timeliness in decision making

ES available on a 24 hour service By taking some of the lesser important decisions Replication at many sites Eliminates cognitive bias/limitation, memory loss

Increased productivity of experts

Improved consistency in decisions

Improved understanding and explanation Improved management of uncertainty

Fuzzy logic e.g. how tall is tall? And little amnesia! knowledge is codified.

Formalization of knowledge within organisation

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Limitations Associated With ESs

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How do you code common sense? expertise is difficult to extract and encode. Expert errors transferred to model Another is that human experts adapt naturally but an ES must be recoded. human experts better recognize when a problem is outside the knowledge domain, but an ES may just keep working ESs cant eliminate the cognitive limitations of the user An ES is functional only in a narrow domain

Key Point Summary

The Concept of Expertise Use of logic Methods of human reasoning

Categorisation etc

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