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Introduction to AI and
Intelligent Agents
Foundations of Artificial Intelligence
Some Definitions of AI
 Building systems that think like humans
 “The exciting new effort to make computers think … machines with minds, in
the full and literal sense” -- Haugeland, 1985
 “The automation of activities that we associate with human thinking, … such
as decision-making, problem solving, learning, …” -- Bellman, 1978
 Building systems that act like humans
 “The art of creating machines that perform functions that require intelligence
when performed by people” -- Kurzweil, 1990
 “The study of how to make computers do things at which, at the moment,
people are better” -- Rich and Knight, 1991
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Some Definitions of AI
 Building systems that think rationally
 “The study of mental faculties through the use of computational models”
-- Charniak and McDermott, 1985
 “The study of the computations that make it possible to perceive, reason, and
act” -- Winston, 1992
 Building systems that act rationally
 “A filed of study that seeks to explain and emulate intelligent behavior in terms
of computational processes” -- Schalkoff, 1990
 “The branch of computer science that is concerned with the automation of
intelligent behavior” -- Luger and Stubblefield, 1993
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Thinking and Acting Humanly
 Thinking humanly: cognitive modeling
 Develop a precise theory of mind, through experimentation and introspection,
then write a computer program that implements it
 Example: GPS - General Problem Solver (Newell and Simon, 1961)
 trying to model the human process of problem solving in general
 Acting humanly
 "If it looks, walks, and quacks like a duck, then it is a duck”
 The Turing Test
 interrogator communicates by typing at a terminal with TWO other agents. The
human can say and ask whatever s/he likes, in natural language. If the human cannot
decide which of the two agents is a human and which is a computer, then the
computer has achieved AI
 this is an OPERATIONAL definition of intelligence, i.e., one that gives an
algorithm for testing objectively whether the definition is satisfied
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Thinking and Acting Rationally
Thinking Rationally
 Capture ``correct'' reasoning processes”
 A loose definition of rational thinking: Irrefutable reasoning process
 How do we do this
 Develop a formal model of reasoning (formal logic) that “always” leads to the “right” answer
 Implement this model
 How do we know when we've got it right?
 when we can prove that the results of the programmed reasoning are correct
 soundness and completeness of first-order logic
Acting Rationally
 Act so that desired goals are achieved
 The rational agent approach (this is what we’ll focus on in this course)
 Figure out how to make correct decisions, which sometimes means thinking rationally
and other times means having rational reflexes
 correct inference versus rationality
 reasoning versus acting; limited rationality
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Turing’s Goal
 Alan Turing, Computing Machinery and Intelligence, 1950:
 Can machines think?
 How could we tell?
“I propose to consider the question, ‘Can machines think?’ This should begin with
definitions of the meaning of the terms ‘machine’ and ‘think’. The definitions might
be framed so as to reflect so far as possible the normal use of the words, but this
attitude is dangerous. If the meaning of the words ‘machine’ and ‘think’ are to be
found by examining how they are commonly used it is difficult to escape the
conclusion that the meaning and the answer to the question, ‘Can machines think?’ is
to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of
attempting such a definition I shall replace the question by another, which is closely
related to it and is expressed in relatively unambiguous words.”
— Alan Turing, Computing machinery and intelligence, 1950
Turing’s “Imitation Game”
Interrogator
B (a person)
A (a machine)
Necessary versus
Sufficient Conditions
 Is ability to pass a Turing Test a necessary condition of intelligence?
 “May not machines carry out something which ought to be described as
thinking but which is very different from what a man does? This objection is a
very strong one, but at least we can say that if, nevertheless, a machine can be
constructed to play the imitation game satisfactorily, we need not be troubled
by this objection.” — Turing, 1950
 Is ability to pass a Turing Test a sufficient condition of intelligence?
The Turing Syllogism
 If an agent passes a Turing Test,
then it produces a sensible
sequence of verbal responses to a
sequence of verbal stimuli.
 If an agent produces a sensible
sequence of verbal responses to a
sequence of verbal stimuli, then it
is intelligent.
 Therefore, if an agent passes a
Turing Test, then it is intelligent.
The Capacity Conception:
If an agent has the capacity to produce a sensible sequence of verbal
responses to a sequence of verbal stimuli, whatever they may be, then it
is intelligent.
Memorizing all possible answers?
(Bertha’s Machine)
Exponential Growth
 Assume each time the judge asks a question, she picks between two
questions based on what has happened so far
Questions Asked
Possible responses
1
2
3
4
5
6
n
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4
8
16
32
64
2n
11
Storage versus Length
exponential
Polynomial vs. exponential time complexity
(one algorithm step = 1 microsecond)
n=10
n=20
n=30
n=40
n=50
n=60
n
.00001
second
.00002
second
.00003
second
.00004
second
.00005
second
.00006
second
2n
.001
second
1.0
second
17.9
minutes
12.7
days
35.7
years
336
centuries
3n
.059
second
58
minutes
6.5
years
3855
centuries
2x108
centuries
1.3x1013
centuries
(Garvey & Johnson 1979)
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The Compact Conception
If an agent has the capacity to produce a
sensible sequence of verbal responses to an
arbitrary sequence of verbal stimuli without
requiring exponential storage, then it is
intelligent.
Size of the Universe
Time
Here, now
15*109 light-years
Big bang
Storage Capacity of the Universe
Volume: (15*109 light-years)3 = (15*109*1016 meters)3
Density: 1 bit per (10-35 meters)3
Total storage capacity: 10184 bits < 10200 bits < 2670 bits
Critical Turing Test length: 670 bits < 670 characters
< 140 words < 1 minute
The universe is not big enough to
hold a bertha machine
Some Sub-fields of AI
 Problem solving
 Lots of early success here
 Solving puzzles
 Playing chess
 Mathematics (integration)
 Uses techniques like search and problem reduction
 Logical reasoning
 Prove things by manipulating database of facts
 Theorem proving
 Automatic Programming
 Writing computer programs given some sort of description
 Some success with semi-automated methods
 Some error detection systems
 Automatic program verification
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Some Sub-fields of AI
 Language understanding and semantic modeling
 One of the earliest problems
 Some success within limited domains
 How can we “understand” written/spoken language?
 Includes answering questions, translating between languages, learning from
written text, and speech recognition
 Some aspects of language understanding:
 Associating spoken words with “actual” word
 Understanding language forms, such as prefixes/suffixes/roots
 Syntax; how to form grammatically correct sentences
 Semantics; understanding meaning of words, phrases, sentences
 Context
 Conversation
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Some Sub-fields of AI
 Pattern Recognition
 Computer-aided identification of objects/shapes/sounds
 Needed for speech and picture understanding
 Requires signal acquisition, feature extraction, ...
 Data mining and Information Retrieval
 Expert Systems and Knowledge-based Systems
 Designers often called knowledge engineers
 Translate things that an expert knows and rules that an expert uses to make
decisions into a computer program
 Problems include
 Knowledge acquisition (or how do we get the information)
 Explanation (of the answers)
 Knowledge models (what do we do with info)
 Handling uncertainty
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Some Sub-fields of AI
 Planning, Robotics and Vision
 Planning how to perform actions
 Manipulating devices
 Recognizing objects in pictures
 Machine Learning and Neural Networks
 Can we “remember” solutions, rather than recalculating them?
 Can we learn additional facts from present data?
 Can we model the physical aspects of the brain?
 Classification and clustering
 Non-monotonic Reasoning
 Truth maintenance systems
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Fundamental Techniques of AI
 Knowledge Representation
 Intelligence/intelligent behavior requires knowledge, which is:
 Voluminous
 Hard to characterize
 Constantly changing
 How can one capture formally (i.e., computerize) everything needed for
intelligent behavior? Some questions...
 How do you store all of that data in a useful way?
 Can you get rid of some?
 How can you store decision making steps?
 Characteristics of good data representation techniques:
 Captures general situation rather than being overly specific
 Understandable by the people who provide it
 Easily modified to handle errors, changes in data, and changes in perception
 Of general use
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Fundamental Techniques of AI
Search
 How can we model the problem search space
 How can we move between steps in a decision making process?
How can you find the info you need in a large data set?
Given a choice of possible decision sequences, how do you pick a
good one?
Heuristic functions
 Given a goal, how do you figure out what to do (planning)?
 Base-level versus meta-level reasoning
How can we reason about what step to take next (in reaching the
goal)?
How much do we reason before acting?
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AI in Everyday Life?
 AI techniques are used in many common applications
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Intelligent user interfaces
Search Engines
Spell/grammar checkers
Context sensitive help systems
Medical diagnosis systems
Regulating/Controlling hardware devices and processes (e.g, in automobiles)
Voice/image recognition (more generally, pattern recognition)
Scheduling systems (airlines, hotels, manufacturing)
Error detection/correction in electronic communication
Program verification / compiler and programming language design
Web search engines / Web spiders
Web personalization and Recommender systems (collaborative/content filtering)
Personal agents
Customer relationship management
Credit card verification in e-commerce / fraud detection
Data mining and knowledge discovery in databases
Computer games
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AI Spin-Offs
 Many technologies widely used today were the direct or indirect
results of research in AI:
 The mouse
 Time-sharing
 Graphical user interfaces
 Object-oriented programming
 Computer games
 Hypertext
 Information Retrieval
 The World Wide Web
 Symbolic mathematical systems (e.g., Mathematica, Maple, etc.)
 Very high-level programming languages
 Web agents
 Data Mining
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What is an Intelligent Agent
 An agent is anything that can
 perceive its environment through sensors, and
 act upon that environment through actuators (or effectors)
actuators
 Goal: Design rational agents that do a “good job” of acting in
their environments
 success determined based on some objective performance measure
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Example: Vacuum Cleaner Agent
 Percepts: location and contents, e.g., [A, Dirty]
 Actions: Left, Right, Suck, NoOp
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What is an Intelligent Agent
 Rational Agents
 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,
amount of time taken, amount of electricity consumed, amount of noise generated, etc.
Definition of 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.
 Omniscience, learning, autonomy
 Rationality is distinct from omniscience (all-knowing with infinite knowledge)
 Choose action that maximizes expected value of perf. measure given percept to date
 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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What is an Intelligent Agent
 Rationality depends on
 the performance measure that defines degree of success
 the percept sequence - everything the agent has perceived so far
 what the agent know about its environment
 the actions that the agent can perform
 Agent Function (percepts ==> actions)
 Maps from percept histories to actions f: P*  A
 The agent program runs on the physical architecture to produce the function f
 agent = architecture + program
Action := Function(Percept Sequence)
If (Percept Sequence) then do Action
 Example: A Simple Agent Function for Vacuum World
If (current square is dirty) then suck
Else move to adjacent square
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What is an Intelligent Agent
 Limited Rationality
 Optimal (i.e. best possible) rationality is NOT perfect success: limited sensors,
actuators, and computing power may make this impossible
 Theory of NP-completeness: some problems are likely impossible to solve
quickly on ANY computer
 Both natural and artificial intelligence are always limited
 Degree of Rationality: the degree to which the agent’s internal "thinking"
maximizes its performance measure, given
 the available sensors
 the available actuators
 the available computing power
 the available built-in knowledge
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PEAS Analysis
 To design a rational agent, we must specify the task environment
 PEAS Analysis:
 Specify Performance Measure, Environment, Actuators, Sensors
 Example: Consider the task of designing an automated taxi driver
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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
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PEAS Analysis – More Examples
 Agent: Medical diagnosis system
 Performance measure: Healthy patient, minimize costs, lawsuits
 Environment: Patient, hospital, staff
 Actuators: Screen display (questions, tests, diagnoses, treatments, referrals)
 Sensors: Keyboard (entry of symptoms, findings, patient's answers)
 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
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PEAS Analysis – More Examples
 Agent: Internet Shopping Agent
 Performance measure??
 Environment??
 Actuators??
 Sensors??
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Environment Types
 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.
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Environment Types (cont.)
 Static (vs. dynamic):
 The environment is unchanged while an agent is deliberating (the environment
is semi-dynamic 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. multi-agent):
 An agent operating by itself in an environment.
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Environment Types (cont.)
The environment type largely determines the agent design.
The real world is (of course) partially observable, stochastic, sequential,
dynamic, continuous, multi-agent
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Structure of an Intelligent Agent
 All agents have the same basic structure:
 accept percepts from environment
 generate actions
 A Skeleton Agent:
function Skeleton-Agent(percept) returns action
static: memory, the agent's memory of the world
memory  Update-Memory(memory, percept)
action  Choose-Best-Action(memory)
memory  Update-Memory(memory, action)
return action
 Observations:
 agent may or may not build percept sequence in memory (depends on domain)
 performance measure is not part of the agent; it is applied externally to judge
the success of the agent
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Looking Up the Answer?
 A Template for a Table-Driven Agent:
function Table-Driven-Agent(percept) returns action
static: percepts, a sequence, initially empty
table, a table indexed by percept sequences, initially fully specified
append percept to the end of percepts
action  LookUp(percepts, table)
return action
 Why can't we just look up the answers?
 The disadvantages of this architecture
 infeasibility (excessive size)
 lack of adaptiveness
 How big would the table have to be?
 Could the agent ever learn from its mistakes?
 Where should the table come from in the first place?
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Agent Types
 Simple reflex agents
 are based on condition-action rules and implemented with an appropriate
production system. They are stateless devices which do not have memory of
past world states.
 Reflex Agents with memory (Model-Based)
 have internal state which is used to keep track of past states of the world.
 Agents with goals
 are agents which in addition to state information have a kind of goal
information which describes desirable situations. Agents of this kind take
future events into consideration.
 Utility-based agents
 base their decision on classic axiomatic utility-theory in order to act rationally.
Note: All of these can be turned into “learning” agents
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A Simple Reflex Agent
 We can summarize part of
the table by formulating
commonly occurring patterns
as condition-action rules:
 Example:
if car-in-front-brakes
then initiate braking
 Agent works by finding a
rule whose condition matches
the current situation
 rule-based systems
 But, this only works if the
current percept is sufficient
for making the correct
decision
Foundations of Artificial Intelligence
function Simple-Reflex-Agent(percept) returns action
static: rules, a set of condition-action rules
state  Interpret-Input(percept)
rule  Rule-Match(state, rules)
action  Rule-Action[rule]
return action
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Example: Simple Reflex Vacuum Agent
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Agents that Keep Track of the World
 Updating internal state
requires two kinds of
encoded knowledge
 knowledge about how the world
changes (independent of the
agents’ actions)
 knowledge about how the
agents’ actions affect the world
 But, knowledge of the
internal state is not always
enough
 how to choose among
alternative decision paths (e.g.,
where should the car go at an
intersection)?
 Requires knowledge of the goal
to be achieved
Foundations of Artificial Intelligence
function Reflex-Agent-With-State(percept) returns action
static: rules, a set of condition-action rules
state, a description of the current world
state  Update-State(state, percept)
rule  Rule-Match(state, rules)
action  Rule-Action[rule]
state  Update-State(state, action)
return action
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Agents with Explicit Goals
 Reasoning about actions
 reflex agents only act based on pre-computed knowledge (rules)
 goal-based (planning) act by reasoning about which actions achieve the goal
 less efficient, but more adaptive and flexible
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Agents with Explicit Goals
 Knowing current state is not always enough.
 State allows an agent to keep track of unseen parts of the world, but the agent
must update state based on knowledge of changes in the world and of effects of
own actions.
 Goal = description of desired situation
 Examples:
 Decision to change lanes depends on a goal to go somewhere (and other factors);
 Decision to put an item in shopping basket depends on a shopping list, map of
store, knowledge of menu
 Notes:
 Search (Russell Chapters 3-5) and Planning (Chapters 11-13) are concerned with
finding sequences of actions to satisfy a goal.
 Reflexive agent concerned with one action at a time.
 Classical Planning: finding a sequence of actions that achieves a goal.
 Contrast with condition-action rules: involves consideration of future "what will
happen if I do ..." (fundamental difference).
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A Complete Utility-Based Agent
 Utility Function
 a mapping of states onto real numbers
 allows rational decisions in two kinds of situations
 evaluation of the tradeoffs among conflicting goals
 evaluation of competing goals
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Utility-Based Agents (Cont.)
 Preferred world state has higher utility for agent = quality of
being useful
 Examples
 quicker, safer, more reliable ways to get where going;
 price comparison shopping
 bidding on items in an auction
 evaluating bids in an auction
 Utility function: state ==> U(state) = measure of happiness
 Search (goal-based) vs. games (utilities).
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Shopping Agent Example
 Navigating: Move around store; avoid obstacles
 Reflex agent: store map precompiled.
 Goal-based agent: create an internal map, reason explicitly about it, use signs
and adapt to changes (e.g., specials at the ends of aisles).
 Gathering: Find and put into cart groceries it wants, need to
induce objects from percepts.
 Reflex agent: wander and grab items that look good.
 Goal-based agent: shopping list.
 Menu-planning: Generate shopping list, modify list if store is
out of some item.
 Goal-based agent: required; what happens when a needed item is not there?
Achieve the goal some other way. e.g., no milk cartons: get canned milk or
powdered milk.
 Choosing among alternative brands
 utility-based agent: trade off quality for price.
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General Architecture for Goal-Based Agents
Input percept
state  Update-State(state, percept)
goal  Formulate-Goal(state, perf-measure)
search-space  Formulate-Problem (state, goal)
plan  Search(search-space , goal)
while (plan not empty) do
action  Recommendation(plan, state)
plan  Remainder(plan, state)
output action
end
 Simple agents do not have access to their own performance measure
 In this case the designer will "hard wire" a goal for the agent, i.e. the designer will choose
the goal and build it into the agent
 Similarly, unintelligent agents cannot formulate their own problem
 this formulation must be built-in also
 The while loop above is the "execution phase" of this agent's behavior
 Note that this architecture assumes that the execution phase does not require
monitoring of the environment
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Learning Agents
 Four main components:
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

Performance element: the agent function
Learning element: responsible for making improvements by observing performance
Critic: gives feedback to learning element by measuring agent’s performance
Problem generator: suggest other possible courses of actions (exploration)
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Search and Knowledge Representation
 Goal-based and utility-based agents require representation of:
 states within the environment
 actions and effects (effect of an action is transition from the current state to
another state)
 goals
 utilities
 Problems can often be formulated as a search problem
 to satisfy a goal, agent must find a sequence of actions (a path in the state-space
graph) from the starting state to a goal state.
 To do this efficiently, agents must have the ability to reason with
their knowledge about the world and the problem domain
 which path to follow (which action to choose from) next
 how to determine if a goal state is reached OR how decide if a satisfactory state
has been reached.
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Intelligent Agent Summary
 An agent perceives and acts in an environment. It has an
architecture and is implemented by a program.
 An ideal agent always chooses the action which maximizes its
expected performance, given the percept sequence received so
far.
 An autonomous agent uses its own experience rather than
built-in knowledge of the environment by the designer.
 An agent program maps from a percept to an action and
updates its internal state.
 Reflex agents respond immediately to percepts.
 Goal-based agents act in order to achieve their goal(s).
 Utility-based agents maximize their own utility function.
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Exercise
 Do Exercise 1.3, on Page 30
 You can find out about the Loebner Prize at:
http://www.loebner.net/Prizef/loebner-prize.html
 Also (for discussion) look at exercise 1.2 and read the material on the Turing
Test at:
http://plato.stanford.edu/entries/turing-test/
 Read the article by Jennings and Wooldridge (“Applications of
Intelligent Agents”). Compare and contrast the definitions of agents and
intelligent agents as given by Russell and Norvig (in the text book) and
and in the article.
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Exercise
 News Filtering Internet Agent
 uses a static user profile (e.g., a set of keywords specified by the user)
 on a regular basis, searches a specified news site (e.g., Reuters or AP) for news
stories that match the user profile
 can search through the site by following links from page to page
 presents a set of links to the matching stories that have not been read before
(matching based on the number of words from the profile occurring in the news
story)
 (1) Give a detailed PEAS description for the news filtering agent
 (2) Characterize the environment type (as being observable,
deterministic, episodic, static, etc).
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