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Artificial Intelligence
Lecture 1
Objectives
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Definition
Foundation of AI
History of AI
Agent
Application of AI
Definition
• It is the science and engineering of making
intelligent machines, especially intelligent
computer programs
• It is related to the similar task of using
computers to understand human intelligence
Definition of scientists
Foundation
• Strong AI
 Introduced by John Searle in 1980 Minds,Brains and
Programs(Article)
 Deals with the creation of computer-based artificial
intelligence truly reason and solve problems
 Types Human-like AI(computer program think and
reason just like human mind), Non-Human-like AI(
computer programs develops non-human way of thinking
and reasoning)
Foundation(Cont..)
• Weak AI
Deals with the creation of some form of artificial
intelligence that can reason and solve problems in
a limited
History of AI
• To program computers think like human, at first we
have to resolve how humans think
• Studied on human-thinking and mind go back nearly
2000 years
• Arostotle was the first philosopher who formalize
human thinking
• Warren McCulloch and Walter Pitts(1943) Models of
Artificial Neurons  earliest work in AI
• Marvin Minsky and Dean Edmond(1951)first Neural
computer(SNARC)
• McCarthy wrote LISP(1958)
Application of AI
• Chinook(1994)man-machine champion in
checker
• Deep Blue Chess playing computer beat Garry
Kasparov in 1996
• Fuzzy Logic is a technique for reasoning under
uncertainty
• Expert systems
• Neural Network
Agent
• An agent is anything that can be viewed as perceiving
its environment through sensors and acting upon that
environment through effectors
• human agent has eyes, ears as sensor and hands,legs
as effectors
• a hardware or software based computer system that
has the following properties
• Autonomy  operates without direct intervention of
human
• Social ability Agents can communicate with other
agents via an agent communication language
• Reactivity  Agents perceive their environment
Architecture of Agent
Types of Agent
• Rational agentdoes the right thing
• Omniscient agent knows the actual
outcome of its actions
Some terms
• Performance measure: a subjective measure
to characterize how successful an agent is
(e.g., speed, power usage, accuracy, money,
etc.)
• (degree of) Autonomy: to what extent is the
agent able to make decisions and take actions
on its own?
Structure of Intelligent Agents
Types of agent program
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simple reflex agents
model-based reflex agents
goal-based agents
utility-based agents
Simple reflex agents
• act only on the basis of
the current percept,
ignoring the rest of the
percept history.
• The agent function is
based on the conditionaction rule: if condition
then action.
Model-based reflex agents
• It works by finding a
rule whose condition
matches the current
situation (as defined by
the percept and the
stored internal state)
and then doing the
action associated with
that rule
Goal-based agents
• Goal-based agents further
expand on the capabilities
of the model-based
agents, by using "goal"
information.
• Goal information
describes situations that
are desirable.
• allows the agent a way to
choose among multiple
possibilities, selecting the
one which reaches a goal
state
Utility-based agents
• Goal-based agents only
distinguish between goal
states and non-goal states
• to define a measure of
how desirable a
particular state is can be
obtained through the use
of a utility function which
maps a state to a
measure of the utility of
the state
Mobile agents
• Programs that can migrate from one machine to
another.
• Execute in a platform-independent execution
environment
• Two types:
– One-hop mobile agents (migrate to one other place)
– Multi-hop mobile agents (roam the network from
place to place)
• Applications:
– Distributed information retrieval.
– Telecommunication network routing.
Properties of environments
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Accessible
Deterministic
Episodic
Static
Discrete
Accessible vs. inaccessible
• If an agent's sensory apparatus gives it access
to the complete state of the
environmentaccessable
Deterministic vs. nondeterministic
• If the next state of the environment is
completely determined by the current state
and the actions selected by the agents, then
we say the environment is deterministic
Episodic vs. nonepisodic
• In an episodic environment, the agent's
experience is divided into "episodes."
• Each episode consists of the agent perceiving and
then acting.
• The quality of its action depends just on the
episode itself, because subsequent episodes do
not depend on what actions occur in previous
episodes.
• Episodic environments are much simpler because
the agent does not need to think ahead.
Static vs. dynamic
• If the environment can change while an agent
is deliberating, then we say the environment is
dynamic for that agent; otherwise it is static.
Discrete vs. continuous
• If there are a limited number of distinct,
clearly defined percepts and actions we say
that the environment is discrete.
• Chess is discrete—there are a fixed number of
possible moves on each turn.
• Taxi driving is continuous—the speed and
location of the taxi and the other vehicles
sweep through a range of continuous values
Questions
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Define AI according to different scientists
Distinguish between strong AI and weak AI
Define agent
State properties of agent
Explain structure of agent with proper example
Mention differences between rational and
omniscient agent
• How is an Agent different from other software?
• Explain different types of agent program
• Mention properties of environment