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CSL302Artificial IntelligenceSpring 2017NARAYANAN C KRISHNANCKN@IITRPR.AC.IN
Reference MaterialqCourse TextbookoArtificial Intelligence A Modern Approach,
Stuart Russell and Peter Norvig, 3rd edition oLow price edition will suffice
qOther reference materialsohttp://aima.cs.berkeley.edu/o AI – Rich and Knight
qPre-requisiteso CSL 201 – Data Structures
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Tentative Course Schedule
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Quizzes – 20%qAlmost every weekqCovers material discussed from the previous quiz till the current weekqDuration 30-45mqTop 6 out of 8 will be considered towards the final grade
Quiz WeekQ1 2Q2 3Q3 5Q4 6Q5 11Q6 12Q7 14Q8 15
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Labs – 30%qDue every third Friday 11.55pmqProgramming Assignmentsostart early – heavy
programming component
qTA is available for any assistanceostudents are encouraged to
contact the TA for clarifications regarding the labs
qProgramming languageoC/Python
Lab DateL1 27/1L2 17/2L3 10/3L4 31/3L5 22/4L6 24/4
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Grading SchemeqTentative BreakupoQuizzes (6 out of 8) – 20%oLabs (5 out of 6) – 30%oMid-semester exam – 25%oEnd-semester exam – 25%oAttendance Bonus - 1%ØAttendance is not mandatory, however attendance will be taken
for every class and will count towards the bonus points
A student must secure an overall score of 40(out of 100) and a combined score of 60(out of 200) in the exams to pass the course.
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Honor CodeqUnless explicitly stated otherwise, for all assignments:oStrictly individual effortoGroup discussions at a high level are encouragedoYou are forbidden from trawling the web for
answers/code etc.qAny infraction will be dealt with in severest terms allowed. qI reserve the right to question you with regards to your submission, if I suspect any misconduct.
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Course Websiteqhttp://cse.iitrpr.ac.in/ckn/courses/s2017/csl302/csl302.htmlqAll class related material will be accessible from the webpageqLabs will be uploaded incrementally and will be notified through emailoLabs will be submitted only by moodle
qI will not be giving any separate handoutsqThe pdf version of the lecture slides will be available on the class website.
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General InformationqCourse Structureo3-0-2 (4 credits)
qScheduled Class TimingsoWednesday-1.30-2.20pmoThursday– 2.25-3.15pmoFriday– 3.20-4.15pm
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8:00 - 8:50 9:00 - 9:50 9:55 - 10:45 10:50 - 11:40 11:45 - 12:35 12:35 - 1:30 1:30 - 2:20 2:25 - 3:15 3:20 - 4:10 4:15 - 5:05 5:10 - 6:00 6:00 - 6:30
CYL454(L1-G1,G3,G5) HUL459(L-7) CSL343(L2) CSL607(L-5)
CYL454(L2-G2,G4,G6) HUL475(L-5) EEL345/EEL475(L-10) CSL605(L-10)
CYL458(L-3) EEL486(L-6) EEL312/EEL453(L-4)
BML601(L4) MEL203(L-5) MEL603(L6)
MAL421(L-5)
CSL607 CYL454(L1-G1,G3,G5) HUL459(L-7) CSL343(L2)
EEL312 CYL454(L2-G2,G4,G6) HUL475(L-5) EEL345/EEL475(L-10)
MEL603 CYL458(L-3) EEL486(L-6)
CSL605 BML601(L4) MEL203(L-5)
MAL421(L-5)
CSL301(L-2) CSL309(L-6) PHL452(L-4)
CSL343(L2) CYL454(L1-G1,G3,G5) CSL302(L-4) EEL490(L-4) CYL453(L-5) CSL355(L-2)
HUL459(L-7)(T) EEL345/EEL475(L-10) CYL454(L2-G2,G4,G6) EEL323/EEL463(L9) EEL484(L-5) BML451(L-6) EEL315/EEL452(L-4)
HUL475(L-5)(T) EEL486(L-6) EEL312 CYL458(L-3) EEL614(L-10) MEL602(L-10) EEL333/EEL475(L-5)
MEL203(L-5) MEL603 BML601(L4) MEL471(L-7) MEL521(L-9) MEL522(L-6)
MAL421(L-5) MEL403(L-1)
CSL301(L-2) CSL309(L-6)
CSL355(L-2) CSL302(L-4) EEL490(L-4) PHL452(L-4)
CYL458(L-3)(T) EEL315/EEL452(L-4) EEL323/EEL463(L9) EEL484(L-5) CYL453(L-5)
EEL333/EEL475(L-5) EEL614(L-10) MEL602(L-10) BML451(L-6)
MEL522(L-6) MEL471(L-7) MEL521(L-9)
MEL403(L-1)
CSL301(L-2) CSL309(L-6)
PHL452(L-4) CSL355(L-2) CSL302(L-4) EEL490(L-4)
CSL355(L-2)(T) CYL453(L-5) EEL315/EEL452(L-4) EEL323/EEL463(L9) EEL484(L-5) CYL453(L-5)(T)
BML451(L-6) EEL333/EEL475(L-5) EEL614(L-10) MEL602(L-10)
MEL522(L-6) MEL471(L-7) MEL521(L-9)
MEL403(L-1)
SLOT A SLOT B SLOT C SLOT D SLOT A1 SLOT B1 SLOT C1 SLOT D1
FRI
INDIAN INSTITUTE OF TECHNOLOGY ROPAR2014 BATCH TIMETABLE FOR 2nd SEMESTER OF ACADEMIC YEAR 2016-17
MON
TUES
WED
THUR
EEP-307-LAB
EEP-307-LAB
EEP309-LAB
MEL403(L-1)(T)
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8:00 - 8:50 9:00 - 9:50 9:55 - 10:45 10:50 - 11:40 11:45 - 12:35 12:35 - 1:30 1:30 - 2:20 2:25 - 3:15 3:20 - 4:10 4:15 - 5:05 5:10 - 6:00 6:00 - 6:30
CYL458(L-3) HUL472(L3) CSL202(L-L1) MAL213(L1-G1,G3,G5)
BML601(L4) EEL204(L-4) MAL213(L2-G2,G4,G6)
MAL421(L-5) MEL201(L-3)
MAL452(L-6)
EEL204(L-4)(T) MAL213(L1-G1,G3,G5) CYL458(L-3) HUL472(L3) CSL202(L-L1)
MEL201(L-3)(T) MAL213(L2-G2,G4,G6) BML601(L4) EEL204(L-4)
MAL421(L-5) MEL201(L-3)
MAL452(L-6)
CSL202(L-L1) MAL213(L1-G1,G3,G5) CYL458(L-3) MEL471(L-7) EEL205((L-3) PHL452(L-4) EEL208(L-7)
HUL472(L3)(T) EEL204(L-4) MAL213(L2-G2,G4,G6) BML601(L4) CYL453(L-5) MEL625(L-10)
MEL201(L-3) MAL421(L-5) BML451(L-6) MEL618(L-9)
MAL452(L-6) MAL422(L-7)
CYL458(L-3)(T) EEL208(L-7) PHL452(L-4)
MEL625(L-10) MEL471(L-7) EEL205((L-3) CYL453(L-5) EEL205((L-3)(T)
MEL618(L-9) BML451(L-6)
MAL422(L-7)
PHL452(L-4) EEL208(L-7) MEL471(L-7) CYL453(L-5)(T)
EEL208(L-7)(T) CYL453(L-5) MEL625(L-10) EEL205((L-3)
MEL625(L-10)(T) BML451(L-6) MEL618(L-9)
MAL422(L-7)
SLOT A SLOT B SLOT C SLOT D SLOT A1 SLOT B1 SLOT C1 SLOT D1
FRI
THUR
EEP203-LAB
INDIAN INSTITUTE OF TECHNOLOGY ROPAR2015 BATCH TIMETABLE FOR 2nd SEMESTER OF ACADEMIC YEAR 2016-17
MON
TUES
WED
EEP204-LAB
EEP203-LAB
EEP204-LAB
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General InformationqCourse Structureo3-0-2 (4 credits)
qScheduled Class TimingsoWednesday-1.30-2.20pmoThursday– 2.25-3.15pmoFriday– 3.20-4.15pm
qLab hoursoThursday -9.00-10.45am
qTeaching AssistantoYayati Gupta –
yayati.gupta@iitrpr.ac.inqOffice hoursoInstructor – only through prior
appointment or by email
qCourse google groupocsl302s2017@iitrpr.ac.in
qPre-registered students have already been added.qPseudonymo5 characteroJan 9th, 5.00pm
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Introduction
Motivation – Why study AI?
What comes to your mind when you hear AI?
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Kasparov said that he sometimes saw deep intelligence and creativity in the machine's moves
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HAL - Heuristic Algorithmic, capable of• Speech Recognition• Facial Recognition• Natural Language Processing• Lip Reading• Art Appreciation• Reproducing emotional
behavior• Reasoning• Playing chess
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What is AI?qWhat do you think?
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Definition of AIThinking humanly Thinking rationallyActing humanly Acting rationally
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Definition of AI
qActing Humanly –oTuring test
o Is it sufficient to imitate a human (living being)?
Thinking humanly Thinking rationallyActing humanly Acting rationally
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Definition of AI
qThinking humanlyoModel human thinking processoRequires scientific theories of internalactivities of the human brainoCognitive Science, Cognitive Neuroscience
qA machine that thinks like human while solving a problem correctly.
Thinking humanly Thinking rationallyActing humanly Acting rationally
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Definition of AI
qThinking RationallyqLaws of ThoughtoAristotle – right thinkingoBelief that “logic” governs the humanthought process
qKnowledge is not always 100% certainqWhat is the goal? What is purpose of thinking?
Thinking humanly Thinking rationallyActing humanly Acting rationally
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Definition of AI
qActing Rationallyqrational behavior = doing the right thingqEncompasses the other lines of thought.oThinking rationally will help to act rationally, but
is not the only means; Eg: ReflexqAgent: an entity that perceives and actsqGoal: building rational agents
Thinking humanly Thinking rationallyActing humanly Acting rationally
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Intelligent Agents
Definition of AI
qActing Rationallyqrational behavior = doing the right thingqEncompasses the other lines of thought.oThinking rationally will help to act rationally, but
is not the only means; Eg: Reflex
qGoal: building rational agents
Thinking humanly Thinking rationallyActing humanly Acting rationally
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AgentEnvironment
Agent
Perc
epti
on Action
What should I do next?
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Agent Functions and ProgramqAgent behavior is described by the agent function that maps percept sequences to actions.qLookup Table – An action for every possible percept sequence.qAgent Program: realization/concrete implementation of the agent function within some physical system.
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Vacuum World
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Rational AgentsqA rational agent does the right thing(action)qWithout loss of generality, “goals” specifiable by performance measure defining a numerical value for any environment historyqRational Action: that maximizes the expected value of the performance measure given the percept sequence to date and prior knowledge
qRationality ≠ OmniscienceqRationality ≠ SuccessfulqRationality ≠ ClairvoyantqRationality ≠ Intentionally no Sensing
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PEAS – Specifying the Task EnvironmentqMust specify the task environment as fully as possible
oPerformance
oEnvironment
oActuator
oSensors
Task Environment for automated taxi driver?
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PEAS – Specifying the Task EnvironmentqMust specify the task environment as fully as possible
oPerformance- safe, fast, comfortable
oEnvironment-roads, other traffic, traffic signals
oActuator-steering, accelerator, brake, horn, signal
oSensors-video camera, IR sensor, GPS, odometer
Task Environment for automated taxi driver?
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PEAS – Specifying the Task EnvironmentqHow does the following affect the complexity of the problem the rational agent faces?
oPerformance – complex goals makes performance harder to achieve?
oEnvironment
oActuator – Lack of effectors makes performance harder to achieve?
oSensors – Lack of percepts makes performance harder to achieve?
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Properties of the Task Environment
Environment
Agent
Perc
epti
on Action
What should I do next?
Static vs. Dynamic
Partially vs. Fully Observable
Deterministic vs. Stochastic
Instantaneous vs. DurativeFull vs.
Partial Satisfaction
Discrete vs. Continuous
Single vs. Multiple Agents
Episodic vs. Sequential
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Properties of the Task EnvironmentqObservable: The agent can “sense” its environmento best: fully observable worst: unobservable typical: partially observable
qDeterministic: The actions have predictable effectso best: deterministic worst: non-deterministic typical: stochastic
qStatic: The world does not change when the agent is deciding on what to do nexto best: static worst: dynamic typical: quasi-static
qEpisodic: The performance of the agent is determined episodicallyo best: episodic worst: non-episodic
qDiscrete: The environment evolves through a discrete set of stateso best: discrete worst: continuous typical: hybrid
qAgents: # of agents in the environment; are they competing or cooperating?
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Semi Sequential Discrete Multi
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Semi Sequential Discrete Multi
Poker
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Static Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Semi Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Taxi-Driving
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Semi Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Taxi-Driving Partial Stochastic Dynamic
Sequential Continuous
Multi
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Semi Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Taxi-Driving Partial Stochastic Dynamic
Sequential Continuous
Multi
Medical-Diagnosis
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Semi Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Taxi-Driving Partial Stochastic Dynamic
Sequential Continuous
Multi
Medical-Diagnosis
Partial Stochastic Dynamic
Sequential Continuous
Multi
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Semi Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Taxi-Driving Partial Stochastic Dynamic
Sequential Continuous
Multi
Medical-Diagnosis
Partial Stochastic Dynamic
Sequential Continuous
Multi
Image Analysis
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Task Environment-ExamplesEnvironment Observable Deterministic Static Episodic Discrete #
AgentsChess Fully Deterministic Semi Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Taxi-Driving Partial Stochastic Dynamic
Sequential Continuous
Multi
Medical-Diagnosis
Partial Stochastic Dynamic
Sequential Continuous
Multi
Image Analysis
Fully Deterministic Static Episodic Continuous
Single
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Task Environment-Examples
The real world is partially observable, stochastic, dynamic and continuousHow do we handle it then?
Environment Observable Deterministic Static Episodic Discrete #Agents
Chess Fully Deterministic Semi Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Taxi-Driving Partial Stochastic Dynamic
Sequential Continuous
Multi
Medical-Diagnosis
Partial Stochastic Dynamic
Sequential Continuous
Single
Image Analysis
Fully Deterministic Dynamic
Episodic Continuous
Single
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Types of AgentsqTypes of agents (increasing in generality and ability to handle complex environments)oSimple reflex agentsoModel based reflex agentsoGoal-based agentsoUtility-based agentsoLearning agents
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Simple Reflex Agents
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Vacuum World
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Model Based Reflex Agents
State Estimation
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Goal Based Agents
State Estimation
Search/Planning
Search: process of looking for a sequence of actions that reaches the goal statePlanning: can be viewed as search in a structured environment.
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Utility Based Agents
• Utility function: internalization of the performance measure• Conflicting goals• Multiple uncertain goals• Decision theoretic planning
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Learning Agents
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