1. Unit-1
INTRODUCTION

2. – Agents and environments Good behavior – The nature of environments
Intelligent Agents
– Agents and environments
Good behavior
– The nature of environments
Structure of agents
Problem solving agents
Example problems
Searching for solutions
Uninformed search strategies
Avoiding repeated states – searching with partial information.
Foundations of Artificial Intelligence

3. Introduction to AI and Intelligent Agents

4. 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
Foundations of Artificial Intelligence

5. 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
Foundations of Artificial Intelligence

6. Acting humanly: The Turing test approach requirement for Turing test.
Rationality
Acting humanly: The Turing test approach requirement for Turing test.
Two persons and one machine.
1. Interrogator (questioner)
2. B (a person)
3. A (a machine)
Foundations of Artificial Intelligence

7. Turing’s “Imitation Game”
Interrogator
B (a person)
A (a machine)
not new with Turing: Descartes implicitly proposed a test for distinguishing bête and homme based on distinguishability of their verbal behaviors.
Descarte’s view:
Animals are automata; animal behaviors are mechanical.
People, as reveled in their flexible verbal behaviors, are not mechanical.
Machines can’t talk, and therefore can’t think.
“But the principal argument...which may convince us that the brutes are devoid of reason, is that...it has never yet been observed that any animal has arrived at such a degree of perfection as to make use of a true language; that is to say, as to be able to indicate to us by the voice, or by other signs, anything which could be referred to by thought alone, rather than to a mere movement of nature...; which may be taken for the true distinction between man and brute.”
— René Descartes, Letter to Henry More, 1647
“The new problem has the advantage of drawing fairly sharp line s between the physical and intellectual capacities of a man.
The question and answer method seems to be suitable for introducing almost any one of the fields of human endeavor that we wish to include.”
— Alan Turing, Computing Machinery and Intelligence, 1950

8. Capabilities of computer
Natural language processing
To enable it to communicate successfully in english
Knowledge representation
To store what it knows or receives
Automated reasoning
To use the stored information to answer questions and to draw new conclusions.
Machine learning
To adapt to new circumstances.
Foundations of Artificial Intelligence

9. Total Turing test
It avoid physical interaction between questioner and computer because physical stimulation is unnecessary for intelligence.
To pass total turing test the computer will need:
a. Computer vision : To perceive objects.
b. Robotics : To manipulate and move them.
Foundations of Artificial Intelligence

10. 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
Foundations of Artificial Intelligence

11. 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
Foundations of Artificial Intelligence

12. AI in Everyday Life?
AI techniques are used in many common applications
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
Foundations of Artificial Intelligence

13. 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
Foundations of Artificial Intelligence

14. 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)
Goal: Design rational agents that do a “good job” of acting in their environments
success determined based on some objective performance measure
actuators
Foundations of Artificial Intelligence

15. Example: Vacuum Cleaner Agent
Percepts: location and contents, e.g., [A, Dirty]
Actions: Left, Right, Suck, NoOp
Foundations of Artificial Intelligence

16. 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)
Foundations of Artificial Intelligence

17. 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
Foundations of Artificial Intelligence

18. 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
Foundations of Artificial Intelligence

19. 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
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
Foundations of Artificial Intelligence

20. 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
Foundations of Artificial Intelligence

21. PEAS Analysis – More Examples
Agent: Internet Shopping Agent
Performance measure??
Environment??
Actuators??
Sensors??
Foundations of Artificial Intelligence

22. 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.
Foundations of Artificial Intelligence

23. 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.
Foundations of Artificial Intelligence

24. 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
Foundations of Artificial Intelligence

25. Structure of an Intelligent Agent
All agents have the same basic structure:
accept percepts from environment
generate actions
A Skeleton Agent:
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
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
Foundations of Artificial Intelligence

26. Looking Up the Answer? A Template for a Table-Driven Agent:
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?
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
Foundations of Artificial Intelligence

27. 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
Foundations of Artificial Intelligence

28. 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
There are three phases inside the loop here: figure out how
the environment has changed, figure out what is the best action,
figure out how this action changes the environment.
The key advantage of this architecture is that the "interpret"
function identifies "equivalence classes" of percepts: many
different percepts correspond to the SAME environmental situation,
from the point of view of what the agent should DO.
Therefore the table of rules can be much smaller than the lookup table
above.
It is not rational for an agent to pay attention to EVERY aspect
of the environment.
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
Foundations of Artificial Intelligence

29. Example: Simple Reflex Vacuum Agent
There are three phases inside the loop here: figure out how
the environment has changed, figure out what is the best action,
figure out how this action changes the environment.
The key advantage of this architecture is that the "interpret"
function identifies "equivalence classes" of percepts: many
different percepts correspond to the SAME environmental situation,
from the point of view of what the agent should DO.
Therefore the table of rules can be much smaller than the lookup table
above.
It is not rational for an agent to pay attention to EVERY aspect
of the environment.
Foundations of Artificial Intelligence

30. 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
LEARNING IN INTELLIGENT AGENTS
With the reflex architecture, if the table of rules prescribes the
wrong action, and the agent discovers this and changes the table,
it has automatically generalized from its specific experience.
Generalization is a key phenomenon in learning. Generalization
always requires previous "background" knowledge to direct it.
All complex intelligent agents will have a lot of background knowledge
preprogrammed, because they do not have the time to receive enough
experience and feedback from the environment to allow them to learn to
behave correctly starting from scratch.
In linguistics this is called the "poverty of stimulus" argument. If
you calculate how many sentences a young child hears before it starts
to speak correct English, the number is too few to allow it to "guess"
the grammar of English. Therefore the baby must have a so-called
universal natural language grammar preprogrammed into it by its genes.
This argument is controversial, but there is scientific agreement
that background knowledge of some sort (often very hidden and implicit)
is necessary for learning in humans and AI systems.
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
Foundations of Artificial Intelligence

31. 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
Foundations of Artificial Intelligence

32. 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).
Foundations of Artificial Intelligence

33. 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
Foundations of Artificial Intelligence

34. 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).
Foundations of Artificial Intelligence

35. 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.
Foundations of Artificial Intelligence

36. 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
GOALS AND GOAL FORMULATION
Often the first step in problem-solving is to simplify the
performance measure that the agent is trying to maximize.
Formally, a "goal" is a set of desirable world-states.
"Goal formulation" means ignoring all other aspects of the
current state and the performance measure, and choosing a goal.
Example: if you are in Arad (Romania) and your visa will expire
tomorrow, your goal is to reach Bucharest airport.
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
Foundations of Artificial Intelligence

37. Learning Agents Four main components:
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)
Foundations of Artificial Intelligence

38. 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.
Foundations of Artificial Intelligence

39. 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.
Foundations of Artificial Intelligence

40. Exercise Do Exercise 1.3, on Page 30
You can find out about the Loebner Prize at:
Also (for discussion) look at exercise 1.2 and read the material on the Turing Test at:
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.
Foundations of Artificial Intelligence

41. 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).
Foundations of Artificial Intelligence