Artificial Intelligence

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Artificial Intelligence

• CS 165A
• Fall 2004
• Lecture Notes 3

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(No Transcript)
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Main Approaches to AI

• Definition of intelligence
• 1. Acting humanly Turing test approach
  (1950)
• idea to define intelligence by
  comparison with
• (acceptedly) intelligent entities
• 2. Thinking humanly cognitive modeling approach
• 3. Thinking rationally laws of thought approach
• typically based on logical
  representation
• focus on making correct
  inferences
• 4. Acting rationally rational agent approach
• rational agent acts to achieve best
  (expected)
• outcome
• Viewed in early years (e.g., Turing) as ability
  to think

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AI as ideal behavior
Human
Ideal
Systems that think like humans
Systems that think rationally
Thought processes and reasoning
Systems that act like humans
Systems that act rationally
Behavior
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Approach of text/course to AI

• Will briefly overview Turing Test approach
• because of its historical importance
• Will adopt rational agent approach
• can define standard of rationality more
  easily
• than if use comparison with humans
• more general than approaches based on rational
  
• thought
• Rational approach as ideal model
• Compare with ideal (frictionless) models of
  physics

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Turings seminal AI paper
Computing Machinery and Intelligence (1950)

• Considers the question, Can Machines Think?
• Too subjective, meaningless rather, replace
  this question with an operational definition of
  thinking/intelligence
• The Imitation Game

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Turing paper (cont.)

• The Turing Test
• Are there imaginable digital computers which
  would do well in the imitation game?
• i.e., Can a computer fool an interrogator into
  thinking it is a person?
• Properties of the Turing Test
• Operational/functional/behavioral definition of
  intelligence
• Distinguishes between physical and intellectual
  capacities
• Question and answer method
• language comprehension and generation
• Might there be other kinds of Turing Tests?
• Emotional, physical, visual

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Digital Computers

• In 1950, computers were not household items!
• Turing had to define digital computers
• Distinguishes from human computers
• States basic Theory of Computation results
  regarding universality
• All digital computers are essentially equivalent
• Dont need different machines for different tasks
• Main technical issues
• Adequate storage (109), Speed, Programming
• Key for Turing was learning machines
• Probabilistic (not completely determined)
• Simulate a childs mind, then educate

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Quotes

• I should be surprised if a storage of more
  than l09 was required for satisfactory playing of
  the imitation game. It is probably not
  necessary to increase the speed of operations of
  the machines at all. Our problem then is to
  find out how to program these machines to play
  the game.
• Instead of trying to produce a program to
  simulate the adult mind, why not rather try to
  produce one which simulates the child's? If this
  were then subjected to an appropriate course of
  education one would obtain the adult brain. Our
  hope is that there is so little mechanism in the
  child-brain that something like it can be easily
  programmed.

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Objections to intelligent computers (Turing)

• The Theological Objection
• Thinking is part of the soul, which is particular
  to man
• The 'Heads in the Sand' Objection 
• I dont want it to be true
• The Mathematical Objection  
• Godels Incompleteness Theorem
• The Argument from Consciousness
• How would we really know?
• Arguments from Various Disabilities
• Computers will never be able to do X

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Main Objections (Turing)

• Lady Lovelace's Objection
• Computers can only do what we instruct them to do
• Argument from Continuity in the Nervous System 
• The nervous system is analog
• The Argument from Informality of Behaviour 
• Rules cannot capture behavior
• The Argument from Extra-Sensory Perception  
• What if ESP is real?

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Insight from 1950

• We may hope that machines will eventually
  compete with men in all purely intellectual
  fields. But which are the best ones to start
  with?
• Chess
• Understanding and speaking language
• I believe that in about fifty years time it will
  be possible to program computers with a storage
  capacity of about 109 to make them play the
  imitation game so well that an average
  interrogator will not have more than 70 per cent
  chance of making the right identification after
  five minutes of questioning.
• I believe that at the end of the century the use
  of words and general educated opinion will have
  altered so much that one will be able to speak of
  machines thinking without expecting to be
  contradicted.

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The Loebner Prize
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AI and Intelligent Agents
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AI as ideal behavior
Human
Ideal
Systems that think like humans
Systems that think rationally
Thought processes and reasoning
Systems that act like humans
Systems that act rationally
Behavior
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Our view of AI

• AIMA view AI is building intelligent (rational)
  agents
• Principles of rational agents
• Models for constructing them
• Their components
• RationalDoes the right thing in a particular
  situation
• Maximize expected performance (not actual
  performance)
• So a rational agent does the right thing (at
  least tries to)
• Maximizes the likelihood of success, given its
  information
• How is the right thing chosen?
• Possible actions (from which to choose)
• Percept sequence (current and past)
• Knowledge (static or modifiable)
• Performance measure (wrt goals defines success)

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What's an Agent?
"An intelligent agent is an entity capable of
combining cognition, perception and action in
behaving autonomously, purposively and flexibly
in some environment." (agents_at_USC)

• Possible properties of agents
• Agents are autonomous they act on behalf of the
  user
• Agents can adapt to changes in the environment
• Agents don't only act reactively, but sometimes
  also proactively
• Agents have social ability they communicate
  with the user, the system, and other agents as
  required
• Agents also cooperate with other agents to carry
  out more complex tasks than they themselves can
  handle
• Agents migrate from one system to another to
  access remote resources or even to meet other
  agents

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AgentWeb

• http//agents.umbc.edu
• Agent portal
• News
• Organizations
• Labs
• Courses
• Companies
• Software
• Topics
• Etc.

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Our model of an agent

• An agent
• perceives its environment,
• reasons about its goals,
• acts upon the environment
• Abstractly, a function from percept histories to
  actions
• f P ? A
• Main components of an agent
• Perception (sensors)
• Reasoning/cognition
• Action (actuators)
• Supported by
• knowledge representation, search, inference,
  planning, uncertainty, learning, communication

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Our view of AI (cont.)

• So this course is about designing rational agents
• Constructing f
• For a given class of environments and tasks, we
  seek the agent (or class of agents) with the
  best performance
• Note computational limitations make complete
  rationality unachievable in most cases
• In practice, we will focus on problem-solving
  techniques for agents
• Cognition (not perception or action)
• View as ways of constructing f

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Ideal Rational Agent

• Basic definition
• For each possible percept sequence, an ideal
  rational agent should do whatever action is
  expected to maximize its performance measure, on
  the basis of the evidence provided by the percept
  sequence and whatever built-in knowledge the
  agent has.
• Potential problems?

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Do the Right Thing

• Task Get to the top
• Whats the right action?

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Describing an agent

• PEAS description of an agent Performance
  measure, Environment, Actuators, Sensors
• Goals may be explicit or implicit (built into
  performance measure)
• Not limited to physical agents (robots)
• Any AI program

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The Vacuum World
Performance measure, Environment, Actuators,
Sensors
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Vacuum world

• Environment (E)
• Location
• Cleanliness
• Three actions (A)
• Move right
• Move left
• Suck
• Sensed information (percepts) of environment (S)
• Two locations
• Left
• Right
• Two states
• Dirty
• Clean
• Performance (P)
• Keep world clean (?)

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PEAS Descriptions
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Agent Program

• Implementing f P ? A or f (P) A
• Lookup table?
• Learning?

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Basic types of agent programs

• Simple reflex agent
• Model-based reflex agent
• Goal-based agent
• Utility-based agent
• Learning agent

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Simple Reflex Agent

• Input/output associations
• Condition-action rule If-then rule (production
  rule)
• If condition then action (if in a certain state,
  do this)
• If antecedent then consequent

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Simple Reflex Agent

• Simple state-based agent Classify the current
  percept into a known state, then apply the rule
  for that state

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Examples

• Function REFLEX-VACUUM-AGENT (location, status)
• Returns an action
• If statusDirty, then return Suck
• Else if location A then return Right
• Else if location B then return Left

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Examples

• Early expert systems
• Production system architecture
• Short term memory (STM) state of world
• Long term memory (LTM) IF-THEN rules
• Matching
• Must make correct decision on basis of current
  percept
• Environment must be fully observable

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Alternatives to simple reflex agent model

• Maintain view of part of world cant see
• Construct and use models of the world
• Construct and use goals for agent
• Simple goals
• Current/past states of environment not sufficient
  for action
• Utility-based model of agent
• Goals may be too simple a representation
• Constitutes special case of utility function
• Construct agents with learning capabilities

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Model-Based Reflex Agent

• Internal state keeps track of the world, models
  the world

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Model-Based Reflex Agent

• State-based agent Given the current state,
  classify the current percept into a known state,
  then apply the rule for that state

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Goal-Based Agent

• Goal immediate, or long sequence of actions?
• Search and planning finding action sequences
  that achieve the agents goals

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Utility-Based Agent

• There are many ways to skin a cat
• Utility function Specifies degree of usefulness
  (happiness)
• Maps a state onto a real number

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Learning Agent
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Environments

• Properties of environments
• Fully vs. partially observable
• Deterministic vs. stochastic
• Episodic vs. sequential
• Friendly vs. hostile
• Static vs. dynamic
• Discrete vs. continuous
• Single agent vs. multiagent
• The environment types largely determine the agent
  design
• The real world is inaccessible, stochastic,
  nonepisodic, hostile, dynamic, and continuous

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Coming next

• Chapter 3, Problem solving and search (blind
  search)
• Chapter 4, Heuristic search