COSC 159: Fundamentals of AI Introduction

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COSC 159 Fundamentals of AIIntroduction

• Craig A. Struble, Ph.D.
• Department of Mathematics, Statistics, and
  Computer Science
• Marquette University

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Overview

• Syllabus
• Grading
• Topics
• What is AI?
• Four competing views
• Agents
• Course Goals
• Summary

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Syllabus

• Instructor information
• Prerequisites
• COSC 152 - Programming Languages
• Textbook
• Russell and Norvig, AIMA, 2nd Edition
• Check pages 389-420
• Attendance

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Grading

• Qualities of good work
• Communication
• Correctness
• Validation
• Comparison
• Efficiency
• Your work will be graded on all aspects

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Topics Covered

• Definitions of AI
• Agents
• Problem representation and solving
• Searching, heuristics, optimization
• Knowledge representation and reasoning
• Logic
• Planning problems
• Uncertainty
• Learning
• More topics if we have time

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What is AI?

• Understand and build intelligent entities
• Artificial 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 intelligence
• Doing the right thing given available information
• How 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)

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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
• Aristotles 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 isnt necessarily important
• Couple rational thinking with other methods
• What if there is no provably correct action?
• Consider the hot stove again
• Did the action require rational thought?

Are reflex actions intelligent?
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Applications

• Autonomous planning and scheduling
• NASAs Remote Agent
• Game playing
• IBMs 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 acts
• In 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
Agent
Percepts
Sensors
?
Environment
Actions
Actuators
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Definitions

• Percept sequence
• History of everything agent has perceive
• Agent function
• Map 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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Example
U
D
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Example

• Assumptions
• Monkey can see the bananas and knows its location
• Defines percepts (Location, Contents)
• Actions
• Up, 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 action
• Table-driven agent

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Table
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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 didnt 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 agents success.

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Performance Measures

• Simple performance measure for monkey and bananas
• The 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 things
• Performance measure
• Agents prior knowledge
• Actions agent can perform
• Agents 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 situations
• Probability, 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
  entities
• Several perspectives on AI
• We will take the rational action perspective
• The agent framework provides a unifying approach
  to AI
• Applications of AI are widespread and complex