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CS541 Artificial Intelligence. Lecture I: Introduction and Intelligent Agent . Self-introduction. 华刚. Prof. Gang Hua Associate Professor in Computer Science Stevens Institute of Technology Research Staff Member (07/2010—08/201 1 ) IBM T J. Watson Research Center - PowerPoint PPT Presentation
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CS541 Artificial Intelligence
Lecture I: Introduction and Intelligent Agent
Self-introduction Prof. Gang Hua
Associate Professor in Computer Science Stevens Institute of Technology
Research Staff Member (07/2010—08/2011) IBM T J. Watson Research Center
Senior Researcher (08/2009—07/2010) Nokia Research Center Hollywood
Scientist (07/2006—08/2009) Microsoft Live Labs Research
Ph.D. in ECE, Northwestern University, 06/2006
华刚
Course Information (1) CS541 Artificial Intelligence Term: Fall 2013 Instructor: Prof. Gang Hua Class time: Tuesday 2:00pm—4:30pm Location: EAS 330 Office Hour: Wednesday 4:00pm—5:00pm by
appointment Office: Lieb/Room305 Course Assistant: Yizhou Lin Course Website:
http://www.cs.stevens.edu/~ghua/ghweb/ teaching/CS541Fall2013.htm
Course Information (2) Text Book:
Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition, Prentice Hall, December 11, 2009 (Required)
Grading: Class Participation: 10% 5 Homework: 50% (including a midterm project) Final Project & Presentation: 40%
ScheduleWeek Date Topic Reading Homework**
1 08/27/2013 Introduction & Intelligent Agent Ch 1 & 2 N/A2 09/03/2013 Search: search strategy and heuristic search Ch 3 & 4s HW1 (Search)3 09/10/2013 Search: Constraint Satisfaction & Adversarial Search Ch 4s & 5 & 6 Teaming Due4 09/17/2013 Logic: Logic Agent & First Order Logic Ch 7 & 8s HW1 due, Midterm Project (Game)5 09/24/2013 Logic: Inference on First Order Logic Ch 8s & 9 6 10/01/2013 Uncertainty and Bayesian Network Ch 13 & Ch14s HW2 (Logic) 7 10/08/2013 Midterm Presentation Midterm Project Due
8 10/15/2013 No class
9 10/22/2013 Inference in Baysian Network Ch 14s HW2 Due, HW3 (Probabilistic Reasoning)10 10/29/2013 Probabilistic Reasoning over Time Ch 15 11 11/05/2013 Machine Learning Ch 18 & 20 HW3 due,12 11/12/2013 Markov Decision Process Ch 16 HW4 (Probabilistic Reasoning Over Time)
13 11/19/2013 Reinforcement learning Ch 21 HW4 due14 11/26/2013 No class Q/A for final project
15 12/03/2013 No class TA assisted Dry run
16 12/10/2013 Final Project Competition Final Project Due
Rules Need to be absent from class?
1 point per class: please send notification and justification at least 2 days before the class
Late submission of homework? The maximum grade you can get from your late
homework decreases 50% per day Zero tolerance on plagiarism!!
You receive zero grade
Introduction & Intelligent Agent
Prof. Gang Hua
Department of Computer ScienceStevens Institute of Technology
ghua@stevens.edu
Introduction to Artificial Intelligence
Chapter 1
What is AI?Systems thinking humanly Systems thinking rationally
Systems acting humanly Systems acting rationally
Acting humanly: Turing Test Turing (1950) "Computing machinery and intelligence": "Can machines think?" "Can machines behave intelligently?" Operational test for intelligent behavior: the Imitation Game
Predicted that by 2000, a machine might have a 30% chance of fooling a layperson for 5 minutes
Anticipated all major arguments against AI in following 50 years Suggested major components of AI: knowledge, reasoning, language
understanding, learning, Total Turing test: adding vision and robotics Problem: Turing test is not reproducible, constructive, or
amenable to mathematical analysis
Thinking humanly: cognitive modeling 1960 "cognitive revolution": information-processing
psychology replaced prevailing orthodoxy of behaviorism Requires scientific theories of internal activities of the brain
What levels of abstraction? "Knowledge" or "circuits"? How to validate? Requires
Predicting and testing behavior of human subjects (top-down) Direct identification from neurological data (bottom-up)
Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI
Both share one principal direction with AI: The available theories do not explain anything resembling human-
level general intelligence
Thinking rationally: "laws of thought" Aristotle: what are correct arguments/thought processes? Several Greek schools developed various forms of logic:
notation and rules of derivation for thoughts; They may or may not have proceeded to the idea of
mechanization Direct line through mathematics and philosophy to modern
AI Problems:
Not all intelligent behavior is mediated by logical deliberation What is the purpose of thinking? What thoughts should I have out of all the thoughts (logical or
otherwise) that I could have?
Acting rationally: rational agent Rational behavior: doing the right thing
The right thing: that which is expected to maximize goal achievement, given the available information
Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action
Aristotle (Nicomachean Ethics): Every art and every inquiry, and similarly every
action and pursuit, is thought to aim at some good
Rational agents An agent is an entity that perceives and acts This course is about designing rational agents Abstractly, an agent is a function from percept histories
to actions:
For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance
Caveat: computational limitations make perfect rationality unachievable
design best program for given machine resources
AI prehistory 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 engineering
Building fast computers Control theory
Design systems that maximize an objective function over time Linguistics
knowledge representation, grammar
Abridged history of AI 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—74 AI discovers computational complexity
Neural network research almost disappears 1969—79 Early development of knowledge-based systems 1980—88 Expert systems industry boom 1988—93 Expert systems industry busts: "AI Winter" 1985—95 Neural networks return to popularity 1988—Resurgence of probability; AI becomes science 1995— The emergence of intelligent agents 2003—Human-level AI back on the agenda 2010-- BIG data? Deep learning?
State of the art Deep Blue defeated the reigning world chess champion Garry Kasparov in
1997 Proved a mathematical conjecture (Robbins conjecture) unsolved for
decades No hands across America (driving autonomously 98% of the time from
Pittsburgh to San Diego) During the 1991 Gulf War, US forces deployed an AI logistics planning and
scheduling program that involved up to 50,000 vehicles, cargo, and people NASA's on-board autonomous planning program controlled the scheduling
of operations for a spacecraft Proverb solves crossword puzzles better than most humans iRobot corporated in 2000: Roomba & Scooba Google cars automatically are driving in the city to collect stree-tview
images Watson whips Brad Rutter and Ken Jennings in Jeopardy in 2011!
DeepBlue & Watson (DeepQA) DeepBlue Watson (DeepQA)
Intelligent Agent
Chapter 2
Outline Agents and environments Rationality: what is a rational agent? PEAS (Performance measure, Environment,
Actuators, Sensors) Environment types Agent types
Agents An agent is anything that can be viewed as
perceiving its environment through sensors and acting upon that environment through actuators
Human agent: eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators
Robotic agent: cameras and infrared range finders for sensors; various motors for actuators
Agents and environments
The agent function maps from percept histories to actions:
The agent program runs on the physical architecture to produce f
agent = architecture + program
Vacuum-cleaner world
Percepts: location and contents, e.g., [A,Dirty] Actions: Left, Right, Suck, NoOp
Rational agents (1) An agent should strive to "do the right thing", based on what it
can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful
Performance measure: An objective criterion for success of an agent's behavior
E.g., performance measure of a vacuum-cleaner agent could be: Amount of dirt cleaned up in time T? Amount of dirt cleaned up minus the amount of electricity consumed
in time T? Amount of time taken to clean a fixed region?
Rational agents (2) 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 the agent has.
Rational agents (3) 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)
PEAS (1) PEAS: Performance measure, Environment, Actuators,
Sensors
To design a rational agent, we must first specify the task environment
Consider, e.g., the task of designing an automated taxi driver:
Performance measure?? Environment?? Actuators?? Sensors??
PEAS (2) To design a rational agent, we must first specify the
task environment
Consider, e.g., the task of designing an automated taxi driver:
Performance measure: Safe, fast, legal, comfortable trip, maximize profits
Environment: Roads, other traffic, pedestrians, customers Actuators: Steering wheel, accelerator, brake, signal, horn
Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard
PEAS (3) Agent: Internet shopping agent
Performance measure: price, quality, appropriateness, efficiency
Environment: current and future WWW sites, vendors, shippers
Actuators: display to user, follow URL, fill in form
Sensors: HTML pages (text, graphics, scripts)
PEAS (4) Agent: Part-picking robot
Performance measure: Percentage of parts in correct bins
Environment: Conveyor belt with parts, bins
Actuators: Jointed arm and hand
Sensors: Camera, joint angle sensors
PEAS (5) Agent: Interactive English tutor
Performance measure: Maximize student's score on test
Environment: Set of students
Actuators: Screen display (exercises, suggestions, corrections)
Sensors: Keyboard
Environment types (1) Fully observable (vs. partially observable): An agent's
sensors give it access to the complete state of the environment at each point in time.
Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic)
Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.
Environment types (2) Static (vs. dynamic): The environment is unchanged
while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does)
Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions.
Single agent (vs. multiagent): An agent operating by itself in an environment.
Environment types (3)Solitaire Backgammo
nInternet
ShoppingTaxi
Observable? Yes Yes No NoDeterministic? Yes No Partly No
Episodic? No No No NoStatic? Yes Semi Semi No
Discrete? Yes Yes Yes NoSingle Agent? Yes No Yes (except
auction)No
The environment type largely determines the agent design The real world is (of course) partially observable, stochastic,
sequential, dynamic, continuous, multi-agent
Agent functions and programs An agent is completely specified by the agent
function mapping percept sequences to actions
One agent function (or a small equivalence class) is rational
Aim: find a way to implement the rational agent function concisely
Table-lookup agent
Drawbacks: Huge table Take a long time to build the table No autonomy Even with learning, need a long time to learn the table
A vacuum-cleaner agent
What is the right function? Can it be implemented in a small agent program?
Agent types Four basic types (with increasing generality):
Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents
All of them can be transformed into learning
agent
Simple reflex agents
The action to be selected only depends on the most recent percept, not a sequence These agents are stateless devices which do not have memory of past world states
Model-based reflex agents
Have internal state which is used to keep track of past states of the world Can assist an agent deal with some of the unobserved aspects of the current state
Goal-based agents
Agent can act differently depending on what the final state should look like E.g., automated taxi driver will act differently depending on where the passenger wants to go
Utility-based agents
An agent's utility function is an internalization of the external performance measure They may differ if the environment is not completely observable or deterministic
Learning agents
Learning agent cuts across all of the other types of agents: any kind of agent can learn
iRobot Roomba Demo
Summary Agents interact with environments through actuators and
sensors The agent function describes what the agent does in all
circumstances The performance measure evaluates the environment sequence A perfectly rational agent maximizes expected performance Agent programs implement (some) agent functions PEAS descriptions define task environments Environments are categorized along several dimensions:
Observable? Deterministic? Episodic? Static? Discrete? Single-agent? Several basic agent architectures exist:
Reflex, Reflex with state, goal-based, utility-based
Candidate projects Midterm Project:
Mastermind (midterm) http://en.wikipedia.org/wiki/Mastermind_%28board_game%
29 Final Projects:
Reversi (Othello) http://en.wikipedia.org/wiki/Reversi
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