cs 561: artificial intelligence - university of southern...
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CS 561: Artificial Intelligence
Instructor: Prof Hadi MoradiInstructor: Prof. Hadi Moradi, [email protected]: M-Th 09:00-10:40, OHE136Office hours: MW 2:30 – 4:00 pm, SAL310,
O b iOr by appointmentTAs: Jeong-Yoon Lee
SAL 112Office hours: TTH 1:00-2:30Email: [email protected]
CS 561: Artificial IntelligenceCourse web page:
http://www-scf.usc.edu/~csci561aUp to date information, lecture notes Relevant dates, links, etc.Also you may check http://den.usc.edu
Class format: two sections of 45 minutesCourse material:Course material:
[AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. 2nd edition
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CS 561: Artificial Intelligence
Course overview: foundations of symbolicCourse overview: foundations of symbolic intelligent systems. Agents, search, problem solving, logic, representation, reasoning, symbolic programming, probabilistic reasoning, and robotics.Prerequisites: CS 455x, i.e.,
programming principles, discrete mathematics for computing, software design and software engineering concepts. Some knowledge of C/C++ for some programming assignments.
CS 561: Artificial Intelligence
Grading:Grading:25% for midterm 25% for final 40% for homeworks and projects10% f Q i10% for Quizzes
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Practical issues
Class list: use learn usc eduClass list: use learn.usc.edu
Login with your USC username and passwordIf CSCI561A is not listed as your courses, notify the TA.ot y t e
Submissions: See class web page under Assignmentssubmit -user csci561 -tag HW3 HW3.tar.gz
Administrative Issues
Midterm 1: 7/26/10 9:00 10:40pmMidterm 1: 7/26/10 9:00 - 10:40pm
Midterm 2: 8/10/10 9:00 - 10:40pm
See also the class web page:http://den usc edu/http://den.usc.edu/
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Why study AI?
Search engines
Science
Labor
Medicine/Diagnosis
AppliancesWhat else?
Humanoid Robots: From Honda to Sony
Walk
Turn
Stairshttp://world.honda.com/robot/
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Sony AIBO
movie1
http://www.aibo.com
Natural Language Question Answering
http://www.ai.mit.edu/projects/infolab/http://aimovie.warnerbros.com
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Robot Teams
USC robotics Lab
Modular robots self re-assembly.
What is AI?The exciting new effort to make “The study of mental facultiesThe exciting new effort to make computers thinks … machine with minds, in the full and literal sense” (Haugeland 1985)“The art of creating machines that perform functions that require intelligence when
The study of mental faculties through the use of computational models” (Charniak et al. 1985)
A field of study that seeks to explain and emulate intelligent behavior in terms ofrequire intelligence when
performed by people” (Kurzweil, 1990)
behavior in terms of computational processes” (Schalkol, 1990)
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AI – The Bigger Picture
Computer SciencePhilosophy
?
Artificial Intelligence
Cognitive Science(Psychology)
p y
Robotics(Engineering)
Neuroscience(Biology)
?
Acting Humanly: The Turing Test
Alan Turing's 1950 article Computing MachineryAlan Turing s 1950 article Computing Machinery and Intelligence discussed conditions for considering a machine to be intelligent
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Acting Humanly: The Turing Test
What tasks require AI?
“AI is the science and engineering ofAI is the science and engineering of making intelligent machines which can perform tasks that require intelligence when performed by humans …”
What tasks require AI?
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What tasks require AI?Tasks that require AI:q
Solving a differential equationBrain surgeryInventing stuffPlaying Jeopardy Playing Wheel of FortuneWhat about walking?What about grabbing stuff?What about pulling your hand away from fire?What about watching TV?What about day dreaming?
Acting Humanly: The Full Turing Test
• Problem:• Problem:
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What would a computer need to pass the Turing test?
Communication:Communication:Memory:Reasoning:Learning:
What would a computer need to pass the Turing test?
Sensing:Sensing:
M t t l (t t l t t)Motor control (total test):
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Thinking Humanly: Cognitive Science
1960 “Cognitive Revolution”:1960 Cognitive Revolution : information-processing psychology replaced behaviorism
Thinking Humanly: Cognitive Science
Cognitive science and modeling the activitiesCognitive science and modeling the activities of the brain
What level of abstraction? “Knowledge” or “Circuits”?How to validate models?
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Thinking Rationally: Laws of Thought
Aristotle (~ 450 B.C.) attempted to codifyAristotle ( 450 B.C.) attempted to codify “right thinking”
What are correct arguments/thought processes?
Thinking Rationally: Laws of Thought
Problems:
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Acting Rationally: The Rational AgentRational behavior: Doing the right thing!
Provides the most general view of AI because it includes:
Acting Rationally: The Rational Agent
Advantages:Advantages:
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How to achieve AI?How is AI research done?
TheoreticalExperimental
How to achieve AI?There are two main lines of research:There are two main lines of research:
Biological, study humans and imitate their psychology or physiology. phenomenal, study and formalize common sense facts about the world and the problems that the world presents to the achievement of goals.world presents to the achievement of goals.
The two approaches interact to some extent, and both should eventually succeed. It is a race, but both racers seem to be walking. [John McCarthy]
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Branches of AILogical AISearchNatural language processingpattern recognition Knowledge representationInference From some facts, others can be inferred. ,Automated reasoning Learning from experiencePlanning To generate a strategy for achieving some goal
AI Prehistory
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Brief History of AITh
ough
t Ancient Times 384 B.C. ----
AristotleLogic: The science of knowing.
Middle Age 1200 Ramon Lull
Ars Magnus: a rule-based device to model man's behavior and nature
Renaissance Empiricism
Next time implement links
tion
ally
:Law
s of
T Renaissance - EmpiricismExplanation of processes
17th Century ---
Gottfried Leibniz 1st system of formal logic
18th Century Rene Descartes
Dualism
19th Century 1845 -
--
Charles Babbage Analytical Engine
-
-George Boole Formalization of the Laws of Logic
Thin
king
Rat
--
Formalization of the Laws of Logic
1879-1903 -
--
Gottlob Frege First-order predicate calculus
Early 20th Century
1910-1912 ---
Russel-Whitehead Principia Mathematica
Bertrand Russel
1931 ---
Kurt Godel Incompleteness Theorem of Logic
Roots of AI in Science:Aristotle(b.384-): syllogism – formal reasoningRamon Lull (b.1235): Ars Magna – a machine capable of answering all questionsRene Descartes (1596): mind / body separation (dualism); "cogito ergo sum“Wilhelm Liebniz (1646-1716): a mechanical concept generator; "materialism"g ;Charles Babbage(1792-1871), Ada Lovelace (1815-1860): Analytical Engine – a general-purpose calculatorGeorge Boole(1815-1864): logic algebras - logical encoding and calculation of thoughtsGottlob Frege(1848-1925): predicate calculus
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Birth of Artificial Intelligence1940-1956 - 1942 - ENIAC :First digital computer
Grea
--
1943 ---
Mc Culloch and Pitts Artificial neural network
1945 ----
J. Von Neumman Modern computer architecture
1949 ---
Claude Shannon Use of heuristics to solve complex problems at E
xpectations
1950 ----
Alan M.Turing Computing Machinery and Intelligence:
Turing Test
1955 ---
Herbert Simon,Alan Newell 1st AI program:Logic Theorist
Herbert Simon
1956 ---
Dartmouth Conference
The Beginning of AIMcCulloch & Pitts
developed theory of artificial neurons (precursor to ANN's) – 1943
Alan Turing – "Can Machines Think?"the turing test (1950)the turing machine
Marvin Minsky & Dean Edmondsfirst ANN constructed, 1951
John McCarthyconvened the Dartmouth conference that coined the term artificial intelligence (AI) (1956) and set the research agenda
symbolic AIconnectionism
LISP (list processing) 1958 1st AI language
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The Rise of AI1957- 1960’s - 1958 -
--
John McCarthy. LISP
Grow
ing
1960 ---
Marvin Minsky Theory of Frames
1961 ----
Herbert Simon,Alan Newell GPS:General Problem Solver
Herbert Simon
1962 ---
Frank Rosenblatt Perceptron: Learning in Neural Networks
1965 L tfi A Z d h
g Disenchantm
ent
1965 ----
Lotfi A. ZadehFuzyy Logic Fuzzy Sets
1968 Joseph Weizenbaum ELIZA: simulates diagnosis by a psychiatrist.
1969 ---
Marvin Minsky,Seymour Papert Limitations of Perceptrons
S. Papert
An Optimistic StartIn the 50's, 60's and early 70's, much exciting progress was being made in AI:Chess
Claude Shannon, 1950The Logic Theorist
Alan Newell, Cliff Shaw, Herb Simon, 1957Checkers (Machine Learning)
Arthur Samuels, 1959Eliza - NLP
Joseph Weizenbaum, 1966DENDRAL – Knowledge-Based System
Feigenbaum, Buchanan, Lederberg, 1969SHRDLU – NLP (Blocks World)
Terry Winnograd, 1972GPS (General Problem Solver)
Alan Newell & Herb Simon, 1972
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The 70’s
Bi th d Ri f E t S tBirth and Rise of Expert Systems
1970-mid 1980’s - 1973 -
--
Alain Colmerauer PROLOG
1974 ---
Paul Werbos Neural Networks Back Propagation Law
1975 E. Feigenbaum, R. Lindsay. Dendral
E.FeigenBaum
Edward Shortliffe MYCIN
1976-1980
R. Duda, P.Hart, P. Barnett PROSPECTOR: The first commercial Expert System
P.Hart
1982 John McDermott XCON – "Expert Configurer
The PlateauIn the 70's, AI researchers began to discover that the problem wasn't as easy as it looked!
The Frame ProblemL k f C S R iLack of Common Sense ReasoningCombinatorial ExplosionThe Gap – "Toy" vs. "Real" worldsPerceptrons, by Minsky & Papert (1969) – proved limitations of perceptron networks and acted to limit significant research in the 70'ssignificant research in the 70 sLighthill Report – 1973: curtailed research funding in British Universities
AI developed a reputation as "over-hyped" and unrealistic
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The 80’s 1982 -
---
John HopfieldHopfield Networks
1982 Teuvo Kohonen
self-organising feature maps for speech recognitizion
1986 T S j kite
ms
work
s 1986 -
--
Terrence SejnowskiNETTalk
Rumerhalt,McMelland
Neural Networks Rediscovering of Back-Propagation Learning
1987 ----
Marvin Minsky The Society of Minds
atio
n of
Exp
ert
Syst
tifi
cial
Neu
ral N
etw
Fuzzy Appliances
1989 ---
Dean Pomerleau ALVINN
Com
mer
cial
iza
Rebi
rth
of A
rt
Commercial Success Despite it's reputation as "over hyped" certainDespite it s reputation as over-hyped , certain AI applications became very successful during the 70's – 80's:
• Expert SystemsIndustrial Robotics• Industrial Robotics
• Planning & Scheduling Applications
AI became a $2,000,000,000 industry by 1988
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Nowadays…Early 90’s -
-Major advances in all areas of AI, with significant demonstrations
--
significant demonstrations
1995 Birth of Intelligent Systems
1997 The Deep Blue chess program beats Garry Kasparov
Late 90’s - Web crawlers --
AI-based information extraction programs
Intelligent Room and Emotional Agents at MIT's AI Lab
2000- Interactive robot pets The Nomad robot
The Gartner Hype Curve
Interest in AI followed this patternInterest in AI followed this pattern, typical of the hype surrounding new technologies
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AI State of the artHave the following been achieved by AI?
World-class chess playingPlaying table tennisCross-country drivingSolving mathematical problemsDiscover and prove mathematical theoriesDiscover and prove mathematical theoriesEngage in a meaningful conversationUnderstand spoken languageObserve and understand human emotions…
Types of expertise (with examples)Deepcognitive
Judgmentalskills
High-levelsocial skillscognitive
skillsskills social skills
Highlycreative
Musician Seniormanager
Author, poet
Analytical Mathemat-i i
Economist, Sociali ti tician programmer scientist
Strictlyprocedural
Typist Driver Socialworker
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A driving example: Grand Challenge
Goal:
Artificial Intelligence Applications
CognitiveScience
ArtificialIntelligence
Robotics NaturalInterfaceScience
Applications Applications InterfaceApplications
•Expert Systems•Fuzzy Logic•Genetic Algorithms•Neural Networks
•Visual Perceptions•Locomotion•Navigation•Tactility
•Natural Language•Speech Recognition•Multisensory Interface•Virtual Reality
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AI Application Areas in Business
Neural Networks
Fuzzy Logic Systems
Virtual Reality
Genetic Algorithms
y
Expert Systems
AI ApplicationAreas inBusiness
Intelligent Agents
Components of Expert SystemsThe Expert System
K l d
ExpertAdvice User
I t fInferenceE i Knowledge
Base
User Workstation
InterfacePrograms
EngineProgram
Expert System Development
Workstation
KnowledgeEngineering
KnowledgeAcquisition
ProgramExpert and/or
Knowledge Engineer
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Expert System ApplicationsDecision Management
Diagnostic/TroubleshootingDiagnostic/Troubleshooting
Maintenance/Scheduling
Design/Configuration
MajorSelection/Classification
Major ApplicationCategoriesof Expert Systems
Process Monitoring/Control
General Introduction
Course Overview
General Introduction
Introduction. [AIMA Ch 1] Course Schedule. Homeworks, exams and grading. Course material, TAs and office hours. Why study AI? What is AI? The Turing test. Rationality. Branches of AI. Research disciplines connected to and at the foundation of AI. Brief history of AI. Challenges for the future. Overview of class syllabus.
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General Introduction
Course Overview
sensors
effectors
Agent
General IntroductionIntelligent Agents. [AIMA Ch 2] What is an intelligent agent? Examples. Doing the right thing (rational action). Performance measure. Autonomy. Environment and agent design. Structure of agents Agent types Reflex agentsStructure of agents. Agent types. Reflex agents.Reactive agents. Reflex agents with state. Goal-based agents. Utility-based agents. Mobile agents. Information agents.
Course Overview (cont.)9 l
Problem solving and search. [AIMA Ch 3]
measuring problem. Types of problems. More examples. Basic idea behind search algorithms.
3 l 5 l9 l
Using these 3 buckets,measure 7 liters of water.
Complexity. Combinatorial explosion and NP completeness.Polynomial hierarchy.
Traveling salesperson problem
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Course Overview (cont.)How can we solve complex problems?
Uninformed search. [AIMA Ch 3]
Depth-first. Breadth-first. Uniform-cost.
3 l 5 l9 l
Using these 3 buckets,measure 7 liters of water.
Depth-limited. Iterative deepening. Examples. Properties.
Traveling salesperson problem
Course Overview (cont.)How can we solve complex problems?
Informed search. [AIMA Ch 4] Best-first. A* search. Heuristics.
p p
Hill climbing. Problem of local extrema. Simulated annealing.
Traveling salesperson problem
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Course Overview (cont.)Practical applications
of search
Constraint Satisfaction[AIMA Ch 5]
BacktrackinggLocal search
Course Overview (cont.)Practical applications
of search
Game playing [AIMA Ch 6]
The minimax algorithm.gResource limitations. Aplha-beta pruning. Elements of chance and non-deterministic games.
tic-tac-toe
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Course Overview (cont.)Towards intelligent agents
Agents that reason logically 1
[AIMA Ch 7]Knowledge-based
Towards intelligent agents
agents. Logic and representation.Propositional (boolean) logic.
wumpus world
Course Overview (cont.)Towards intelligent agentsAgents that reason logically 2.
[AIMA Ch 7]Inference in
i i l l i
Towards intelligent agents
propositional logic. Syntax.Semantics. Examples. wumpus world
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Course Overview (cont.)Building knowledge-based u d g o edge ased
agents: 1st Order LogicFirst-order logic 1. [AIMA Ch 8]
Syntax. Semantics. Atomic sentencesAtomic sentences. Complex sentences. Quantifiers. FOL knowledge base. Situation calculus.
Course Overview (cont.)
Building knowledgeBuilding knowledge-based agents: 1st
Order Logic
First-order logic 2.[AIMA Ch 9]
Describing actions. Planning. Action sequences.
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Course Overview (cont.)
Reasoning LogicallyReasoning LogicallyInference in first-order logic.
[AIMA Ch 9]Proofs. UnificationUnification. Generalized modus ponens. Forward and backward chaining.
Example ofbackward chaining
Course Overview (cont.)Representing and OrganizingRepresenting and Organizing
KnowledgeBuilding a knowledge base.
[AIMA Ch 10]Knowledge bases.Knowledge bases. Vocabulary and rules.OntologiesOrganizing knowledge.
An ontology for the sports domain
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Course Overview (cont.)Systems that can Plan y
Future BehaviorPlanning.[AIMA Ch 11]
Definition and goals. Basic representations for l iplanning.
Situation space and plan space. Examples.
Course Overview (cont.)Learning from Observationg
Decision Trees[AIMA 18]
Introduction to decision trees. Information theory.Constructing DT.Examples.
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Course Overview (cont.)Expert Systemsp y
Probabilities + Bayesian Networks[AIMA 13 + 14]
Basics of probability theory Bayesian rule.
d lConditional reasoning. Bayesian Networks. Reasoning under uncertainty
Course Overview (cont.)Statistical Learning Methodsg
Neural Networks.[AIMA 20]
Human brain structureNeuron and activation function.Forward and backward propagations.Examples.
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Course Overview (cont.)Logical Reasoning in the g gPresence of Uncertainty
Fuzzy logic [Handout]
Introduction to fuzzy logic. Center of gravity
y gLinguistic Hedges.Fuzzy inference.Examples.
Center of largest area
Course Overview (cont.)Machine Learningg
Genetic Algorithms [Handout + AIMA 4]
Genetic algorithm approach. Mutation, Crossover, Fitness function.Examples.
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Course Overview (cont.)What challenges remain?g
Towards intelligent machines.[AIMA Ch 25]
The challenge of robots: with what we have learned, what hard problems remain to be solved?what hard problems remain to be solved? Different types of robots. Tasks that robots are for. Parts of robots. Architectures.Configuration spaces.
robotics@USC
Course Overview (cont.)
What challenges remain?What challenges remain?
Overview and summary. [all of the above]
What have we learnedWhat have we learned. Where do we go from here?
robotics@USC
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Outlook
AI is a very exciting area right nowAI is a very exciting area right now.This course will teach you the foundations.