isc 4322/6300 – gam 4322 artificial intelligence lecture 1 the foundations of ai and intelligent...
TRANSCRIPT
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ISC 4322/6300 – GAM 4322Artificial Intelligence
Lecture 1The Foundations of AIand Intelligent Agents
University Of Houston- VictoriaComputer Science Department
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Contacts
Instructor: Dr. Alireza Tavakkoli
Office: UC 101C
Mon. 5:00 – 6:00 pmOffice hours: Wed. 3:00 – 4:00 pm
or by appointment
Email: [email protected]: http://www2.uhv.edu/tavakkolia/teaching/ISC4322-6300/index.html
Or Blackboard
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• Introduce state-of-the-art algorithms in artificial intelligence
• Show how these algorithms apply to real-world problems
• Provide practice in applying algorithms by solving “real” problems (Games)
After this course, you should be able:
• Evaluate claims about intelligent systems in an informed way
• Have a great algorithmic toolbox to help you design adaptive systems
• Build intelligent agents for interesting agents
• Contribute to AI research
Class Goals
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• Part I: Decision-Making in Deterministic Environments– Single agent: Dynamic programming, informed search techniques, randomized
search, genetic algorithms, constraint satisfaction, planning– Multi-agent: Game theory, mini-max search and approximations
• Part II: Decision-Making in Stochastic Environments– Single agent: Bayesian Networks, Hidden Markov Models, Kalman and Particle
Filters, decision and utility theory, Markov Decision Processes– Multi-agent: Expectimax search, stochastic game theory
• Part III: Learning in Unknown Environments– Supervised learning: Decision Trees, Support Vector Machines, Neural Networks– Unsupervised learning: reinforcement learning
Structure of Course
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• Exams– One mid-term, one Final
• Assignments– 3-4 Homeworks, 2-3 Programming Assignment– Projects can be complete by pairs of students.– Late assignments: (2n)2% Penalty
• Grading Policy– Homework 15% <87 A– Projects 25% 75-87 B– Midterm 25% 62-74 C– Final 30% 50-61 D– Participation 5% 50< F
What is Expected
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What is AI?
Thinking
vs.
Acting
Humanly vs. Rationally
“The automation of activities that weassociate with human thinking,
activities such as decision-making, problem solving, learning”
(Bellman,1978)
“The study of mental faculties through the use of computational models.”
(Winston, 1992)
“The art of creating machines that perform functions that require
intelligence when performed by people.”(Kurzweil, 1990)
“AI is concerned with rational action…and studies the design of rational agents.
A rational agent acts so as to achievethe best expected outcome.”
(S.R. & P.N., 1995)
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What is AI?
Thinking
vs.
Acting
Humanly vs. Rationally
“The automation of activities that weassociate with human thinking,
activities such as decision-making, problem solving, learning”
(Bellman,1978)
“The study of mental faculties through the use of computational models.”
(Winston, 1992)
“The art of creating machines that perform functions that require
intelligence when performed by people.”(Kurzweil, 1990)
1“AI is concerned with rational action…
and studies the design of rational agents. A rational agent acts so as to achieve
the best expected outcome.”(S.R. & P.N., 1995)
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Acting Humanly
Alan Turing(1912-1954)
Turing Test (1950)
Capabilities that the computer would needto possess:
• Natural language processing• Knowledge representation• Automated reasoning• Machine learning
The total Turing test requires also computer vision and robotics
The quest for “artificial flight” succeeded when the Wright brothersand others stopped imitating birds and learned about aerodynamics.
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What is AI?
Thinking
vs.
Acting
Humanly vs. Rationally
“The automation of activities that weassociate with human thinking,
activities such as decision-making, problem solving, learning”
(Bellman,1978)
“The study of mental faculties through the use of computational models.”
(Winston, 1992)
“The art of creating machines that perform functions that require
intelligence when performed by people.”(Kurzweil, 1990)
2
“AI is concerned with rational action…and studies the design of rational agents.
A rational agent acts so as to achievethe best expected outcome.”
(S.R. & P.N., 1995)
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Thinking Humanly
Allen Newel(1927-1992)
Herbert Simon(1916-2001)
Simon and Newel came up with the General Problem Solver – GPS (1961).
GPS can solve (in principle) every problem that can be formalized
symbolically, e.g. Towers of Hanoi
Main concern by Simon and Newel: Is the trace of GPS’ reasoning the same as the trace of a human?
“Over Christmas, Allen Newell andI created a thinking machine.”
Herbert Simon, 1961
First system to separate knowledge of problems from its strategy on how to solve them
But this if the focus of…. Cognitive science:Requires experimental investigation of actual humans or animals
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What is AI?
Thinking
vs.
Acting
Humanly vs. Rationally
“The automation of activities that weassociate with human thinking,
activities such as decision-making, problem solving, learning”
(Bellman,1978)
“The study of mental faculties through the use of computational models.”
(Winston, 1992)
“The art of creating machines that perform functions that require
intelligence when performed by people.”(Kurzweil, 1990)
3
“AI is concerned with rational action…and studies the design of rational agents.
A rational agent acts so as to achievethe best expected outcome.”
(S.R. & P.N., 1995)
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Thinking Rationally
Aristotle(384-322 BC)
“Socrates is a man; all men are mortal;therefore, Socrates is mortal.”
Logic-based AILogic provides:•A syntax for describing the world and relations among objects•A way to achieve logical/correct inferences
Create intelligent systems that reason using the syntax and the laws of logic
By 1965 there were programs that could in principle, solve any solvable problem described in logical notation.
But…1.How to represent knowledge in logical notation when we are less than 100% certain?2.Computational explosion: A few hundred of facts exhaust the computational resources of any computer when it attempts logical inference without some sort of guidance
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What is AI?
Thinking
vs.
Acting
Humanly vs. Rationally
“The automation of activities that weassociate with human thinking,
activities such as decision-making, problem solving, learning”
(Bellman,1978)
“The study of mental faculties through the use of computational models.”
(Winston, 1992)
“The art of creating machines that perform functions that require
intelligence when performed by people.”(Kurzweil, 1990)
4
“AI is concerned with rational action…and studies the design of rational agents.
A rational agent acts so as to achievethe best expected outcome.”(Russell and Norvig, 1995)
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Logical inference is part of intelligence. It does not cover everything:•e.g., might be no provably correct thing to do, but still something must be done•e.g., reflex actions can be more successful than slower, carefully deliberated ones
What is a rational action?One that achieves the best outcome
(or in the case of uncertainty… the best expected outcome)
Acting Rationally
Stuart Russell
PeterNorvig
It is this objective that requires:• Natural language processing• Knowledge representation• Automated reasoning• Machine Learning• Computer Vision• Robotics
“Human behavior is well adapted forone specific environment and is the product, in part, of a complicated andlargely unknown evolutionary processthat is still far from producing perfection.”
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The Foundations of AI
• Philosophy Logic, methods of reasoning, mind as physical system foundations of learning, language,
rationality• Mathematics Formal representation and proof algorithms,
computation, (un)decidability, (in)tractability,probability
• Economics utility, decision theory • Neuroscience physical substrate for mental activity• Psychology phenomena of perception and motor control,
experimental techniques• Computer building fast computers
engineering• Control theory design systems that maximize an objective
function over time • Linguistics knowledge representation, grammar
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AI History – Modern Era
• 1943 McCulloch & Pitts: Boolean circuit model of brain• 1950 Turing's "Computing Machinery and Intelligence"• 1956 Dartmouth meeting: "Artificial Intelligence" adopted• 1952—69 Look, Ma, no hands! • 1950s Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine
• 1965 Robinson's complete algorithm for logical reasoning• 1966—73 AI discovers computational complexity
Neural network research almost disappears• 1969—79 Early development of knowledge-based systems• 1980-- AI becomes an industry • 1986-- Neural networks return to popularity• 1987-- AI becomes a science • 1995-- The emergence of intelligent agents
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• SKICAT: automatically classifies data from space telescopes and indentifies interesting objects in the sky. 94% accuracy, way better than human (decision trees)
• Deep Blue: the first computer program to defeat human champion Garry Kasparov (minimax search + alpha-beta-pruning + optimizations)
• Pegasus, Jupiter, etc.: speech recognition systems (Hidden Markov Models)
• HipNav: a robot hip-replacement surgeon (planning algorithms)
• DARPA Grand/Urban Challenge: autonomous driving 98% of the time from Pittsburgh to San Diego (filtering and planning algorithms)
• Deep Space 1: NASA spacecraft that did an autonomous flyby an asteroid (logic-based AI)
• Credit card fraud detection and loan approval (decision trees and neural networks)
• Chinook: the world checker’s champion (game theory)
• Spam Assassin and other spam detectors (naïve Bayes learning)
• Soccer playing Aibo robots (reinforcement learning)
Where are we now?
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Where are we now?
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Intelligent Agents
Agent
Environment
?
Sensors
Actuators
Percepts
Actions
How to fully describe an AI problem:
• Performance measure• Environment• Actuators• Sensors
Intelligent (rational) agent
For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and
whatever built-in knowledge of the environment the agent has
Definition encompasses both physical and virtual/software agents
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Vacuum-Cleaner World
• Performance Measure– Award One Point per Clean Cell Over 1000 Time Step
• Environment– Two Cell World
• Actions– Left, Right, Suck, NoOp
• Percepts– Location and Contents, e.g., [A,Dirty]
• Is this a rational agent?
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Rational Agents
• Rationality is distinct from omniscience (all-knowing with infinite knowledge)
• Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration)
• An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)
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PEAS
• PEAS: Performance measure, Environment, Actuators, Sensors
• Must first specify the setting for intelligent agent design
• Consider, e.g., the task of designing an automated taxi driver
Agent Type Performance Measure Environment Actuators Sensors
Taxi Driver SafeFastLegalComfortableProfitable
RoadsOther TrafficPedestriansCustomers
SteeringAcceleratorBrakeSignalHornDisplay
CamerasSonarSpeedometerGPSOdometerEngine SensorsKeyboard
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Fully observable vs. partially observable:• If the agent has always access to the complete state of the environment, then the
environment is fully observable• Partial observability due to: noisy, inaccurate sensors or sensor limitations
Deterministic vs. stochastic:• If the next state of the world is completely determined by the current state and the
agent’s action, then the environment is deterministic.• If the environment is deterministic except from the actions of other agents, then it is
called strategic.
Episodic vs. sequential:• In an episodic environment the agent’s experience does not depend on the actions
taken in previous episodes.• In sequential environments, the current decision could affect all future decisions.
Properties of Environments
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Static vs. dynamic:• If the environment changes while the agent is not acting, then the environment
is dynamic.
Discrete vs. continuous:• Continuous values can appear in:
– The state of the environment, time, the percepts or actions of the agent.
Single agent vs. multiagent:• When are the other entities in the world considered agents?
– Competitive environments:• When they maximize an performance measure that depends on the agent’s actions
– Cooperative environments:• When they communicate or follow common rules so as to satisfy a common performance
measure
Properties of Environments
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Agent Functions and Programs
• How does the inside of a rational agent work?– An agent is completely specified by the agent function mapping percept
sequences to actions– Agent = Architecture + Program
• Agent Program vs. Agent Function– Program takes the percept and returns the action– Function takes the entire percept history
• One agent function (or a small equivalence class) is rational
• Aim: find a way to implement the rational agent function concisely
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A Table Driven AgentA Table Driven Agent Program
Function TABLE-DRIVEN-AGENT (percept) returns action static percepts, a sequence, initially empty table, a table of actions, initially fully specified append percept to the end of percepts action Lookup (percepts, table) return action
• Drawbacks:– Huge table– Take a long time to build the table– No autonomy– Even with learning, needs a long time to learn the table
entries
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Reflex Vacuum Agent Program
Sensing input:[Cell Id, Dirty or Not Dirty]
Actions:[Clean, move Left, move Right, NoOp]
Reflex Vacuum Agent Program
Function REFLEX-VACUUM-AGENT ([location, status]) returns action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left
• Advantages• Small Table• Cutting Down the Table Size by Eliminating Percept History• Action Does Not Depend on the Location
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Agent
How do agents work?
Environment
?
Sensors
Actuators
Percepts
Actions
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Agent
Reflex Agents
EnvironmentSensors
Actuators
Percepts
Actions
What the world is like now?
What the world is like now?
What action should I do now?
What action should I do now?
Condition-action
(if-then)rules
Condition-action
(if-then)rules
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Agent
Model-based Reflex Agents
EnvironmentSensors
Actuators
Percepts
Actions
What the world is like now?
What the world is like now?
What action should I do now?
What action should I do now?
Condition-action
(if-then)rules
Condition-action
(if-then)rules
StateState
How the world evolves?How the world evolves?
What my actions do?What my actions do?
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Agent
Goal-based Agents
EnvironmentSensors
Actuators
Percepts
Actions
What the world is like now?
What the world is like now?
What action should I do now?
What action should I do now?GoalsGoals
StateState
How the world evolves?How the world evolves?
What my actions do?What my actions do?What it will be if I
do action A?What it will be if I
do action A?
Search & Planning
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Agent
Utility-based Agents
EnvironmentSensors
Actuators
Percepts
Actions
What the world is like now?
What the world is like now?
What action should I do now?
What action should I do now?
UtilityUtility
StateState
How the world evolves?How the world evolves?
What my actions do?What my actions do? What it will be if I do action A?
What it will be if I do action A?
How happy I will be in the state?
How happy I will be in the state?
Utility Function: X (state space)
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Agent
Learning Agents
EnvironmentSensors
Actuators
Percepts
Actions
ProblemgeneratorProblem
generator
StateState
LearningelementLearningelement
PerformanceElement
PerformanceElement
Performance standard
changes
knowledge
feedback
learninggoals
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Intelligent Agents
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Types of Agents
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• AI: A Modern Approach :– Chapters 1 & 2
Question?