CSL452 Artificial Intelligence Spring 2016cse.iitrpr.ac.in/ckn/courses/s2016/csl452/w1.pdf · oLabs...
Transcript of CSL452 Artificial Intelligence Spring 2016cse.iitrpr.ac.in/ckn/courses/s2016/csl452/w1.pdf · oLabs...
CSL452 Artificial Intelligence Spring 2016 NARAYANAN C KRISHNAN [email protected]
General Information q Course Structure o 3-0-2 (4 credits)
q Class Timings o Monday -9.00-9.50am o Tuesday – 9.55-10.45am o Wednesday – 10.50-11.40am
q Lab hours o Thursday -9.00-10.45am
q Teaching Assistant o TBA
q Office hours o Instructor – only through
prior appointment by email
q Course google group o [email protected]
q Pre-registered students have already been added. q Pseudonym o 5 character o Jan 8th, 5.00pm
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Reference Material q Course Textbook o Artificial Intelligence A Modern Approach,
Stuart Russell and Peter Norvig, 3rd edition o Low price edition will suffice
q Other reference materials o http://aima.cs.berkeley.edu/ o AI – Rich and Knight
q Pre-requisites o CSL 201 – Data Structures
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Quizzes – 30% q Almost every Thursday o 9.00-10.30am o L2
q Covers material discussed from the previous quiz till the current week q Duration 30-45m q Top 6 out of 8 will be considered towards the final grade
Quiz Date
Q1 14/1
Q2 21/1
Q3 4/2
Q4 11/2
Q5 17/3
Q6 23/3
Q7 8/4
Q8 15/4
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Labs – 20% q Due every third Friday 11.55pm q Programming Assignments o start early – heavy
programming component
q TA is available for any assistance o students are encouraged to
contact the TA for clarifications regarding the labs
q Programming language o python/C/C++/Java
Lab Date
L1 29/1
L2 19/2
L3 11/3
L4 1/4
L5 22/4
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Grading Scheme q Tentative Breakup o Quizzes (6-8) – 30% o Labs (5) – 20% o Mid-semester exam – 25% o End-semester exam – 25% o Attendance Bonus - 1% Ø Attendance is not mandatory, however attendance will be taken
for every class and will count towards the bonus points
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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.
Honor Code q Unless explicitly stated otherwise, for all assignments: o Strictly individual effort o Group discussions at a high level are encouraged o You are forbidden from trawling the web for
answers/code etc. q Any infraction will be dealt with in severest terms allowed. q I reserve the right to question you with regards to your submission, if I suspect any misconduct.
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Course Website q http://cse.iitrpr.ac.in/ckn/courses/s2016/csl452/csl452.html q All class related material will be accessible from the webpage q Labs will be uploaded incrementally and will be notified through email o Labs will be submitted only by moodle
q I will not be giving any separate handouts q The pdf version of the lecture slides will be available on the class website.
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Motivation – Why study AI?
Introduction CSL452 - ARTIFICIAL INTELLIGENCE 12
What comes to your mind when you hear AI?
Introduction CSL452 - ARTIFICIAL INTELLIGENCE 14
Kasparov said that he sometimes saw deep intelligence and creativity in the machine's moves
Introduction CSL452 - ARTIFICIAL INTELLIGENCE 15
HAL - Heuristic Algorithmic, capable of • Speech Recognition • Facial Recognition • Natural Language Processing • Lip Reading • Art Appreciation • Reproducing emotional
behavior • Reasoning • Playing chess
Definition of AI
Introduction CSL452 - ARTIFICIAL INTELLIGENCE 20
Thinking humanly Thinking rationally Acting humanly Acting rationally
Definition of AI
q Acting Humanly – o Turing test
o Is it sufficient to imitate a human (living being)?
Introduction CSL452 - ARTIFICIAL INTELLIGENCE 21
Thinking humanly Thinking rationally Acting humanly Acting rationally
Definition of AI
q Thinking humanly o Model human thinking process o Requires scientific theories of internal activities of the human brain o Cognitive Science, Cognitive Neuroscience
q A machine that thinks like human while solving a problem correctly.
Introduction CSL452 - ARTIFICIAL INTELLIGENCE 22
Thinking humanly Thinking rationally Acting humanly Acting rationally
Definition of AI
q Thinking Rationally q Laws of Thought o Aristotle – right thinking o Belief that “logic” governs the human thought process
q Knowledge is not always 100% certain q What is the goal? What is purpose of thinking?
Introduction CSL452 - ARTIFICIAL INTELLIGENCE 23
Thinking humanly Thinking rationally Acting humanly Acting rationally
Definition of AI
q Acting Rationally q rational behavior = doing the right thing q Encompasses the other lines of thought. o Thinking rationally will help to act rationally, but
is not the only means; Eg: Reflex q Agent: an entity that perceives and acts q Goal: building rational agents
Introduction CSL452 - ARTIFICIAL INTELLIGENCE 24
Thinking humanly Thinking rationally Acting humanly Acting rationally
Definition of AI
Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 26
q Acting Rationally q rational behavior = doing the right thing q Encompasses the other lines of thought. o Thinking rationally will help to act rationally, but
is not the only means; Eg: Reflex
q Goal: building rational agents
Thinking humanly Thinking rationally
Acting humanly Acting rationally
Agent Environment
Agent
Perc
epti
on
Action
What should I do next?
Intelligent Agents 27 CSL452 - ARTIFICIAL INTELLIGENCE
Agent Functions and Program q Agent behavior is described by the agent function that maps percept sequences to actions. q Lookup Table – An action for every possible percept sequence. q Agent Program: realization/concrete implementation of the agent function within some physical system.
Intelligent Agents 28 CSL452 - ARTIFICIAL INTELLIGENCE
Rational Agents q A rational agent does the right thing(action) q Without loss of generality, “goals” specifiable by performance measure defining a numerical value for any environment history q Rational Action: that maximizes the expected value of the performance measure given the percept sequence to date and prior knowledge
q Rationality ≠ Omniscience q Rationality ≠ Successful q Rationality ≠ Clairvoyant q Rationality ≠ Intentionally no Sensing
Intelligent Agents 30 CSL452 - ARTIFICIAL INTELLIGENCE
PEAS – Specifying the Task Environment q Must specify the task environment as fully as possible
o Performance
o Environment
o Actuator
o Sensors
Task Environment for automated taxi driver?
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PEAS – Specifying the Task Environment q Must specify the task environment as fully as possible
o Performance- safe, fast, comfortable
o Environment-roads, other traffic, traffic signals
o Actuator-steering, accelerator, brake, horn, signal
o Sensors-video camera, IR sensor, GPS, odometer
Task Environment for automated taxi driver?
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PEAS – Specifying the Task Environment q How does the following affect the complexity of the problem the rational agent faces? o Performance – complex goals makes performance harder to achieve?
o Environment
o Actuator – Lack of effectors makes performance harder to achieve?
o Sensors – Lack of percepts makes performance harder to achieve?
Intelligent Agents 33 CSL452 - ARTIFICIAL INTELLIGENCE
Properties of the Task Environment
Environment
Agent
Perc
epti
on A
ction
What should I do next?
Static vs. Dynamic
Partially vs. Fully Observable
Deterministic vs. Stochastic
Instantaneous vs. Durative Full vs.
Partial Satisfaction
Discrete vs. Continuous
Single vs. Multiple Agents
Episodic vs. Sequential
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Properties of the Task Environment
Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 35
q Observable: The agent can “sense” its environment o best: fully observable worst: unobservable typical: partially observable
q Deterministic: The actions have predictable effects o best: deterministic worst: non-deterministic typical: stochastic
q Static: The world does not change when the agent is deciding on what to do next o best: static worst: dynamic typical: quasi-static
q Episodic: The performance of the agent is determined episodically o best: episodic worst: non-episodic
q Discrete: The environment evolves through a discrete set of states o best: discrete worst: continuous typical: hybrid
q Agents: # of agents in the environment; are they competing or cooperating?
Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete #
Agents Chess
Intelligent Agents 36 CSL452 - ARTIFICIAL INTELLIGENCE
Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete #
Agents Chess Fully Deterministic Semi Sequential Discrete Multi
Intelligent Agents 37 CSL452 - ARTIFICIAL INTELLIGENCE
Task Environment-Examples
Intelligent Agents 38 CSL452 - ARTIFICIAL INTELLIGENCE
Environment Observable Deterministic Static Episodic Discrete # Agents
Chess Fully Deterministic Semi Sequential Discrete Multi
Poker
Task Environment-Examples
Intelligent Agents 39 CSL452 - ARTIFICIAL INTELLIGENCE
Environment Observable Deterministic Static Episodic Discrete # Agents
Chess Fully Deterministic Static Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Task Environment-Examples
Intelligent Agents 40 CSL452 - ARTIFICIAL INTELLIGENCE
Environment Observable Deterministic Static Episodic Discrete # Agents
Chess Fully Deterministic Semi Sequential Discrete Multi
Poker Partial Stochastic Static Sequential Discrete Multi
Taxi-Driving
Task Environment-Examples
Intelligent Agents 41 CSL452 - ARTIFICIAL INTELLIGENCE
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
Task Environment-Examples
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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
Task Environment-Examples
Intelligent Agents 43 CSL452 - ARTIFICIAL INTELLIGENCE
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
Multi
Task Environment-Examples
Intelligent Agents 44 CSL452 - ARTIFICIAL INTELLIGENCE
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
Multi
Image Analysis
Task Environment-Examples
Intelligent Agents 45 CSL452 - ARTIFICIAL INTELLIGENCE
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
Multi
Image Analysis
Fully Deterministic Static Episodic Continuous
Single
Task Environment-Examples
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The real world is partially observable, stochastic, dynamic and continuous How 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
Types of Agents q Types of agents (increasing in generality and ability to handle complex environments) o Simple reflex agents o Model based reflex agents o Goal-based agents o Utility-based agents o Learning agents
Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 47
Goal Based Agents
Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 51
State Estimation
Search/Planning
Search: process of looking for a sequence of actions that reaches the goal state Planning: can be viewed as search in a structured environment.
Utility Based Agents
Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 52
• Utility function: internalization of the performance measure • Conflicting goals • Multiple uncertain goals • Decision theoretic planning