CS1001CS1001

Lecture 25Lecture 25

OverviewOverview

Homework 4Homework 4 Artificial IntelligenceArtificial Intelligence Database SystemsDatabase Systems

ReadingReading

Brookshear, 11Brookshear, 11 Brookshear, 10Brookshear, 10 Brookshear, 9Brookshear, 9

Homework 4Homework 4

Check CourseworksCheck Courseworks Problems based onProblems based on

– Handout (Smullyan, Natural Handout (Smullyan, Natural Deduction)Deduction)

– Question from Ch. 10Question from Ch. 10– Question from Ch. 11Question from Ch. 11

Artificial IntelligenceArtificial Intelligence

Reasoning (Production Systems)Reasoning (Production Systems)– Goal is to Goal is to derivederive a solution given facts and a solution given facts and

rulesrules SearchingSearching

– You are given facts and rules and You are given facts and rules and searchsearch all all possible combinations to find some desired possible combinations to find some desired solution (usually minimum/max)solution (usually minimum/max)

HeuristicsHeuristics– Operate based on guidelines you know to Operate based on guidelines you know to

be true about a problembe true about a problem

Paradigms in AIParadigms in AI

““Top Down”Top Down”– Describe intelligent behavior as rule setsDescribe intelligent behavior as rule sets– Define behavior at the highest level and Define behavior at the highest level and

refine as need berefine as need be– Examples: most production systems, Examples: most production systems,

expert systemsexpert systems ““Bottom Up”Bottom Up”

– Define very basic behavior of “agents”Define very basic behavior of “agents”– Describe simple rules of how these agents Describe simple rules of how these agents

interactinteract– Simulate interactions on a grand scaleSimulate interactions on a grand scale

A Production SystemA Production System

Genetic AlgorithmsGenetic Algorithms

A “Bottom Up” approachA “Bottom Up” approach Create simple logic and rules for Create simple logic and rules for

interactionsinteractions Essentially, a GA is a program that Essentially, a GA is a program that

writes and alterswrites and alters other programs. It other programs. It then simulates those programs and then simulates those programs and evaluates their performanceevaluates their performance

http://math.hws.edu/xJava/GA/http://math.hws.edu/xJava/GA/ http://users.ox.ac.uk/~quee0818/ts/ts.hhttp://users.ox.ac.uk/~quee0818/ts/ts.h

tmltml

Expert SystemsExpert Systems

Expert systems are a strict set of rulesExpert systems are a strict set of rules These rules allow for drawing These rules allow for drawing

conclusions from an initial set of factsconclusions from an initial set of facts Propositional logic Propositional logic couldcould be used as be used as

the mathematical system behind an the mathematical system behind an expert systemexpert system

Example: If a patient has a Hematocrit Example: If a patient has a Hematocrit reading of 8, he/she has severe reading of 8, he/she has severe AnemiaAnemia

Neural NetworksNeural Networks

Neural networks are based on the Neural networks are based on the biological function of brain biological function of brain neurons (sort of)neurons (sort of)

This is a This is a learninglearning model, whereby model, whereby the model can be the model can be trainedtrained and and updated over timeupdated over time

Neural NetworksNeural Networks

Semantic NetworksSemantic Networks

Blackboard SystemsBlackboard Systems

Blackboard systems contain all Blackboard systems contain all sorts of subsystems that we’ve sorts of subsystems that we’ve seen so farseen so far

They are a metaphorical area for They are a metaphorical area for different AI systems to exchange different AI systems to exchange predictions and vote on a predictions and vote on a consensus as a wholeconsensus as a whole