Fundamentals of AI Introduction. COSC 159 - Fundamentals of AI2 Overview Syllabus Grading Topics...

Fundamentals of AIIntroduction

COSC 159 - Fundamentals of AI 2

Overview

SyllabusGradingTopics

What is AI?Four competing views

AgentsCourse GoalsSummary


Syllabus

Instructor information

PrerequisitesProgramming Languages

TextbookRussell and Norvig, AIMA, 2nd Edition

Attendance


Grading

Qualities of good workCommunicationCorrectnessValidationComparisonEfficiency

Your work will be graded on all aspects


Topics CoveredDefinitions of AIAgentsProblem representation and solving Searching, heuristics, optimization

Knowledge representation and reasoning Logic

Planning problemsUncertaintyLearningMore topics if we have time


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?


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


Rationality

Given the situation, was the boss’ action irrational?

What would make the boss’ action irrational?


Competing Views of AI

Many definitions that can be classified as follows (Russell and Norvig, 2003)

Like Humans Rationally

Thinking

Acting


Acting Humanly

Turing test (1950)


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


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


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


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?


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


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


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


Agents

AgentSensors

Actuators

?

Environm

entPercepts

Actions


Definitions

Percept sequenceHistory of everything agent has perceive

Agent functionMap from percept sequence to action

Agent program Implementation of agent function


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.


ExampleU

D


Example

AssumptionsMonkey can see the bananas and knows

its locationDefines percepts: (Location, Contents)

ActionsUp, down, eat, sleep


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


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) ???


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?


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.


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?


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


Rationality

Rational behavior at any given time depends on four thingsPerformance measureAgent’s prior knowledgeActions agent can performAgent’s percept sequence


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


Course Goals (cont.)

Reason in uncertain situationsProbability, Bayesian networks

Introduce machine learning Inductive learning, decision trees


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.


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

Fundamentals of AI Introduction. COSC 159 - Fundamentals of AI2 Overview Syllabus Grading Topics...

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