Download - 1. Jimma University,JiT Depatment of Computing Introduction To Artificial Intelligence Zelalem H. 2
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Jimma University,JiTDepatment of Computing
Introduction To Artificial Intelligence
Zelalem H.
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Outline
(1)Introduction• What is AI?• Foundations of AI• State of the art in AI• History of AI (Reading assignment)
(2) Intelligent Agents• Agents and environments• Rationality• The Nature of environments• The structure of agents
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1. Introduction
For thousands of years, we have tried to understand how we think!
AI, goes further still; it attempts not just to understand but also to build intelligent entities.
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Introduction…
What is AI?Some possible definitions
• Thinking humanly Thinking rationally• Acting humanly Acting rationally
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Introduction…
Thinking humanly• Cognitive science: the brain as an
information processing machine• Requires scientific theories of how the brain works
• How to understand cognition as a computational process? • Introspection: try to think about how we think
• Predict and test behavior of human subjects • Image the brain, examine neurological data
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Acting humanly• The Turing Test
• What capabilities would a computer need to have to pass the Turing Test?• Natural language processing• Knowledge representation• Automated reasoning• Machine learning
Introduction…
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Turing Test: Criticism• What are some potential problems with the Turing Test?• Some human behavior is not intelligent• Some intelligent behavior may not be human• Human observers may be easy to fool
• Chinese room argument: one may simulate intelligence without having true intelligence
• Is passing the Turing test a good scientific goal?
Introduction…
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Introduction…
Thinking rationally• Idealized or “right” way of thinking• Logic: patterns of argument that always yield correct conclusions when supplied with correct premises “Socrates is a man; All men are mortal; Therefore Socrates is mortal.”
• Beginning with Aristotle, philosophers and mathematicians have attempted to formalize the rules of logical thought
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Introduction…
Thinking rationally …• Logicist approach to AI: describe problem in formal logical notation and apply general deduction procedures to solve it
• Problems with the logicist approach• Computational complexity of finding the solution
• Describing real-world problems and knowledge in logical notation
• A lot of intelligent or “rational” behavior has nothing to do with logic
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Introduction…
Acting rationally• A rational agent is one that acts to achieve the best outcome• Goals are application-dependent and are expressed in terms of the utility of outcomes
• Being rational means maximizing your expected utility
• This definition of rationality only concerns the decisions/actions that are made, not the cognitive process behind them
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Introduction…
Acting rationally…• Advantages
• Generality: goes beyond explicit reasoning, and even human cognition altogether
• Practicality: can be adapted to many real-world problems
• Amenable to good scientific and engineering methodology
• Avoids philosophy and psychology
• Any disadvantages?• Not feasible in complicated envt’s • Computational demands are just too high
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AI FoundationsPhilosophy
• Can formal rules be used to draw valid conclusions?
• How does the mind arise from a physical brain?
• Where does knowledge come from?• How does knowledge lead to action?
Mathematics• What are the formal rules to draw valid conclusions?
• What can be computed?• How do we reason with uncertain information?
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AI Foundations…
Economics• How should we make decisions so as to maximize payoff?
• How should we do this when others may not go along?
Neuroscience How do brains process information?
Psychology How do humans and animals think and act?
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AI Foundations…
Computer Engineering How can we build an efficient computer?
Control Theory and Cybernetics How can artifacts operate under their own control?
Linguistics How does language relate to thought?
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The state-of-the-art
Robotic Vehicles Speech recognition Autonomous planning and scheduling Game playing Spam fighting, fraud detection Robotics Machine translation Vision
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The History of Artificial intelligence
Reading Assignment From Page 16 to 28 Russell and Norvig, 3rd edition
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2. Intelligent Agents
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
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Agent function
The agent function maps from percept histories to actions
The agent program runs on the physical architecture to produce the agent function
agent = architecture + program
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Vacuum-cleaner world
Percepts: Location and status, e.g., [A,Dirty]
Actions: Left, Right, Suck, NoOp
function Vacuum-Agent([location,status]) returns an action
if status = Dirty then return Suckelse if location = A then return Rightelse if location = B then return Left
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Rational agents
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 the agent’s built-in knowledge
Performance measure An objective criterion for success of an agent's behavior
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Specifying the task environment
Problem specification: Performance measure, Environment, Actuators, Sensors (PEAS)
Example: 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
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Agent: Part-sorting robot
Performance measure• Percentage of parts in correct bins
Environment• Conveyor belt with parts, bins
Actuators• Robotic arm
Sensors• Camera, joint angle sensors
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Agent: Spam filter
Performance measure• Minimizing false positives, false negatives
Environment• A user’s email account
Actuators• Mark as spam, delete, etc.
Sensors• Incoming messages, other information about user’s account
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Environment typesFully observable (vs. partially observable): The 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 agent’s action
Episodic (vs. sequential): The agent's experience is divided into atomic “episodes,” and the choice of action in each episode depends only on the episode itself
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Environment types
Static (vs. dynamic): The environment is unchanged while an agent is deliberating• Semidynamic: the environment does not change with the passage of time, but the agent's performance score does
Discrete (vs. continuous): The environment provides a fixed number of distinct percepts, actions, and environment states• Time can also evolve in a discrete or continuous fashion
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Environment types
Single agent (vs. multi-agent): An agent operating by itself in an environment
Known (vs. unknown): The agent knows the rules of the environment
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Examples
Chess w Clock without clock Taxi
Fully observableDeterministic Episodic StaticDiscreteSingle Agent
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Yes Yes No
Strategic Strategic No
No No No
Semi Yes No
Yes Yes No
No No No
Hierarchy of agent types
Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents
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Simple reflex agent
Select action on the basis of current percept, ignoring all past percepts
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Model-based reflex agent
Maintains internal state that keeps track of aspects of the environment that cannot be currently observed
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Goal-based agent
The agent uses goal information to select between possible actions in the current state
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Utility-based agent
The agent uses a utility function to evaluate the desirability of states that could result from each possible action
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A learning agent
to build learning machines and then to teach
them.
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A learning Agent
learning element, which is responsible for making improvements, performance element, which is responsible for selecting external actions. The learning element uses feedback from the critic on how the agent is doing and determines how the performance element should be modified to do better in the future. problem generator: responsible
for suggesting actions that will lead to new and informative experiences
Questions?