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 2012 Instructor: Prof. Gang Hua Class time: Wednesday 6:15pm—8:40pm Location: Babbio Center/Room 210 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/ cs541_artificial_intelligence_fall_2012.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 08/29/2012 Introduction & Intelligent Agent Ch 1 & 2 N/A

2 09/05/2012 Search: search strategy and heuristic search Ch 3 & 4s HW1 (Search)

3 09/12/2012 Search: Constraint Satisfaction & Adversarial Search Ch 4s & 5 & 6  Teaming Due

4 09/19/2012 Logic: Logic Agent & First Order Logic Ch 7 & 8s HW1 due, Midterm Project  (Game)

5 09/26/2012 Logic: Inference on First Order Logic Ch 8s & 9  

6 10/03/2012 No class    

 7 10/10/2012 Uncertainty and Bayesian Network  Ch 13 & Ch14s  HW2 (Logic)

8 10/17/2012 Midterm Presentation 　 Midterm Project Due

9 10/24/2012 Inference in Baysian Network Ch 14s HW2 Due, HW3 (Probabilistic Reasoning)

10 10/31/2012 Probabilistic Reasoning over Time Ch 15  

11 11/07/2012 Machine Learning    HW3 due,

12 11/14/2012 Markov Decision Process Ch 18 & 20 HW4 (Probabilistic Reasoning Over Time)

 13 11/21/2012 No class Ch 16  

14 11/29/2012 Reinforcement learning Ch 21 HW4 due

15 12/05/2012 Final Project Presentation   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

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 Backgammon

Internet Shopping

Taxi

Observable? Yes Yes No No

Deterministic? Yes No Partly No

Episodic? No No No No

Static? Yes Semi Semi No

Discrete? Yes Yes Yes No

Single 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