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Fundamentals of AIIntroduction
COSC 159 - Fundamentals of AI 2
Overview
SyllabusGradingTopics
What is AI?Four competing views
AgentsCourse GoalsSummary
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Syllabus
Instructor information
PrerequisitesProgramming Languages
TextbookRussell and Norvig, AIMA, 2nd Edition
Attendance
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Grading
Qualities of good workCommunicationCorrectnessValidationComparisonEfficiency
Your work will be graded on all aspects
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Topics CoveredDefinitions of AIAgentsProblem representation and solving Searching, heuristics, optimization
Knowledge representation and reasoning Logic
Planning problemsUncertaintyLearningMore topics if we have time
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What is AI?
Understand and build intelligent entitiesArtificial refers to building entities
What is intelligence?Understand and build an entity emulating a
human?Understand and build an entity that is
rational?
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Rationality
An ideal concept of intelligenceDoing the right thing given available
informationHow do we define the right thing?
Suppose put your hand down on a hot stove. What is the rational response?Rationality does not always mean doing the best possible thing
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Rationality
Given the situation, was the boss’ action irrational?
What would make the boss’ action irrational?
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Competing Views of AI
Many definitions that can be classified as follows (Russell and Norvig, 2003)
Like Humans Rationally
Thinking
Acting
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Acting Humanly
Turing test (1950)
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Acting Humanly
Goal: Make computers/entities act like humans Natural language processing Knowledge representation Automated reasoning Machine learning
It is not important how the actions are chosen, as long as results in behavior indistinguishable from a human
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Thinking Humanly
Understand cognition Defined as the mental process of knowing,
including aspects such as awareness, perception, reasoning, and judgment.
Simulate cognition on computers
Cognitive science Experimental investigation of humans and
animals
The how is important
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Thinking Rationally
Attempt to codify “right thinking” Aristotle’s syllogisms (reasonings or patterns of
argument)
Logical approach Formal methods of representing knowledge Formal methods of reasoning
Again, how a conclusion is reached is important
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Acting Rationally
Entities that do the right thing
The how isn’t necessarily importantCouple rational thinking with other methodsWhat if there is no provably correct action?
Consider the hot stove againDid the action require rational thought?
Are reflex actions intelligent?
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ApplicationsAutonomous planning and scheduling NASA’s Remote Agent
Game playing IBM’s Deep Blue
Autonomous control ALVINN, drove 98% of the time across the country
Diagnosis Medical diagnosis
Pattern recognition Data mining and bioinformatics
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Characteristics of AI Problems
Frequently hard NP-hard, which implies there is no known efficient
general solution
Frequently complex Messy data, such as images, pressure, locations,
natural language, etc.
Frequently imprecise Uncertain situations
Autonomy Cannot require human intervention, must adapt
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Agents
An agent is something that actsIn this class, we will build software agents Agents that act rationally
How are agents different from other programs? Autonomous Perceptive Persistent Adaptable Assume the goals of other agents
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Agents
AgentSensors
Actuators
?
Environm
entPercepts
Actions
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Definitions
Percept sequenceHistory of everything agent has perceive
Agent functionMap from percept sequence to action
Agent program Implementation of agent function
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Example
Consider a world that has a starving monkey and a banana. Whenever the monkey is in the same location as the banana, the monkey will eat it. After eating the banana, the monkey falls asleep.
We would like to build a simulation for the environment with a software agent representing the monkey.
Consider a world with two locations.
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ExampleU
D
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Example
AssumptionsMonkey can see the bananas and knows
its locationDefines percepts: (Location, Contents)
ActionsUp, down, eat, sleep
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Example
Agent function should move monkey to the bananas, eat the bananas, then sleep
One possible agent program is to create a table mapping a percept sequence to appropriate actionTable-driven agent
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Table
Percept Sequence Action
(U, Empty) Down
(U, Bananas) Eat
(D, Empty) Up
…
(U, Empty),(D, Bananas) Eat
(U, Bananas),(U,Empty) Sleep
…
(D,Empty),(U,Bananas),(U,Empty) ???
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Questions to ponder
Is a table driven agent a good way to implement rational behavior?Are all sequences of percepts possible in the environment?What if the monkey didn’t know its location, could you still devise a solution to the problem? How would the percepts change?
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Measuring Rational Behavior
What does it mean for an agent to do the right thing?The right action is the one causing the
agent to be most successful.
A performance measure embodies the criterion for an agent’s success.
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Performance Measures
Simple performance measure for monkey and bananasThe monkey has eaten and fallen asleep.
Suppose you have two monkeys, one that sleeps right after eating and one that wanders around and then falls asleep. Which one is better? Why?
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Performance Measures
Consider more complex environments What performance measure is appropriate for the
economy? What about for stocks? How about medical diagnoses? What about driving a car?
Performance measures are not easy to determine, but you must design one for each environment
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Rationality
Rational behavior at any given time depends on four thingsPerformance measureAgent’s prior knowledgeActions agent can performAgent’s percept sequence
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Course Goals
Understand and build intelligent entities Rational agents
Formulate search problems Solve using uninformed and informed algorithms
Represent and reason about knowledge Logic
Formulate and solve planning problems STRIPS, partial order planners
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Course Goals (cont.)
Reason in uncertain situationsProbability, Bayesian networks
Introduce machine learning Inductive learning, decision trees
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For Next Time
Read through chapters 1 and 2.
Think about how you would implement a simulation for the two location monkey and banana world.
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Summary
AI is the study and implementation of intelligent entitiesSeveral perspectives on AIWe will take the rational action perspective
The agent framework provides a unifying approach to AIApplications of AI are widespread and complex
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