Artificial intelligence

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Artificial intelligence
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Information

• Instructor Johnson Thomas
• 325 North Hall
• Tel [phone]
• e-mail jpt_at_cs.okstate.edu
• Office hours
• Monday 5-7pm
• Tuesday 2-4pm
• Alternates between Tulsa and Stillwater

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Information

• TA zang, yihong
• E-mailzangyihong_at_hotmail.com
• Office hours to be decided

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Information

• Book
• artificial intelligence a modern approach
• Russell and Norvig
• Prentice Hall

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Information

• Practical x 3 45 marks (3 x 15)
• Homework x 3 30 marks (3 x 10)
• Mid-Term 30 marks
• Final Examination 35 marks

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Artificial intelligence
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introduction

• What is AI?
• Many definitions
• Thought processes and reasoning
• Behavior
• Thought and behavior like humans
• Ideal thought and behavior rationality
• A system is rational if It does the right thing
  given what it knows

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Introduction

• Systems that think like humans
• Computers with minds
• Automation of activities that we associate with
  human thinking e.g. decision-making, problem
  solving, learning ,
• Systems that act like humans
• Machines that perform the functions that require
  intelligence when performed by people
• Make computers do things at which, at the moment,
  people are better

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Introduction

• Systems that think rationally
• The study of mental faculties through the use of
  computational models
• The study of the computations that make it
  possible to perceive, reason, and act
• Systems that act rationally
• Study of the design of intelligent agents
• Concerned with intelligent behavior in artifacts

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Introduction

• Acting humanly Turing test
• The computer passes the test if a human
  interrogator, after posing some written
  questions, cannot tell whether the written
  responses come from a person or not.

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Introduction

• Therefore, the computer would need to possess the
  following
• Natural language processing
• Communication
• Knowledge representation
• Store knowledge
• Automated reasoning
• Use stored information to answer questions and
  draw new conclusions
• Machine learning
• Adaptation and pattern recognition/extrapolation

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Introduction

• Turing test no physical interaction
• Total Turing test
• Interrogator can test subjects perceptual
  abilities
• Need computer vision to perceive objects
• Interrogator can pass physical objects
• Need Robotics to manipulate objects and move
  about

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Introduction

• Thinking humanly cognitive modeling
• Understand the workings of human minds
• Once we have a theory of the mind, we can express
  the theory as a computer program
• Cognitive science brings together computer
  models from AI and experimental techniques from
  psychology

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Introduction

• Thinking rationally laws of thought
• Logic
• Difficult in practice
• Knowledge can be informal

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Introduction

• Acting rationally rational agents
• agent something that acts
• More than just a program
• Can perceive their environment
• Can adapt to change
• Operate under autonomous control
• Persists over a prolonged time.
• Can take on anothers goals
• Rational agent acts to achieve best outcome
  possible

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History

• Subjects which have contributed to AI
• Philosophy 428 BC present
• Mathematics 800-present a
• Economics 1776-present
• Neuroscience 1861 present
• Psychology 1879-present
• Computer engineering of 1940-present
• Control theory and cybernetics 1948-present
• linguistics 1957-present

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History

• First AI work - McCulloch and Pits (1943)
• Model of artificial neurons
• Based on
• Physiology and functions of the brain
• Analysis of propositional logic
• Turings theory of computation
• Each neuron is on or off
• Neuron switches on in response to stimulation
  by a sufficient number of neighboring neurons

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History

• Hebb demonstrated an updating rule for modifying
  the connection strengths between neurons
• Minsky and Edwards built the first neural network
  computer 1950
• Turings article in 1950 computing machinery
  and intelligence introduced Turing test,
  machine learning, genetic algorithms, enforcement
  learning

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History

• Birthplace of AI meeting organized by McCarthy
  at Dartmouth college in 1956.
• Some early programs
• Logic theorist (LT) for proving theorems
• General Problem Solver (GPS) imitates human
  problem solving protocols
• Physical symbol system hypothesis any system
  exhibiting intelligence must operate by
  manipulating data structures composed of symbols

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History

• Geometric theorem prover (1959)
• Program for checkers
• McCarthy (1958)
• Defined Lisp
• Invented time sharing
• Advice taker program first program to embody
  general knowledge of the world
• Minsky a number of micro world projects that
  require intelligence
• Blocks world rearrange blocks In a certain way,
  using a robot hand that can pick up one block at
  a time

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History

• vision project
• Vision and constraint propagation
• Learning theory
• Natural language understanding
• Planning
• Perceptrons

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History

• Use of domain specific knowledge that allows
  larger reasoning steps
• Dendral program Inferring molecular structure
  from the information provided by a mass
  spectrometer
• Expert systems for medical diagnosis mycin
• No theoretical framework
• Knowledge acquired by Interviewing experts
• Rules reflected uncertainty associated with
  medical knowledge

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History

• First commercial expert system R1 DEC
• For configuring computers
• Every major U.S. corporation had expert systems
• Japanese fifth generation project
• US microelectronics and computer technology
  corporation (MCC)
• Britains Alveys program

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History

• Hopfield statistical approaches for neural
  networks
• Hinton neural net models of memory
• so-called connectionist models (as opposed to
  symbolic models promoted earlier)

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History

• AI is now a science based on rigorous theorems
  or hard experimental evidence

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Applications

• Autonomous planning and scheduling
• NASAs remote agent program
• controls scheduling of operations for a
  spacecraft.
• Plans are generated from high level goals
  specified from the ground.
• Monitors operation of the spacecraft as the plans
  are executed
• Game playing
• IBMs deep Blue defeated world champion

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Applications

• Autonomous control
• ALVINN computer vision system to steer a car
• Diagnosis
• Medical diagnosis programs based on probabilistic
  analysis
• Logistics planning
• Logistics planning and scheduling for
  transportation in gulf war

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Applications

• Robotics
• Language understanding and problem solving

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Intelligent agents
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Agents

• Agent anything that can be viewed as perceiving
  its environment through sensors and acting upon
  that environment through actuators
• An agent is a function from percept histories to
  an action
• f P ? A

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Agents

• Human agent
• Ears, eyes, for sensors
• Hands, legs, for actuators
• Robotic agent
• Cameras for sensors
• Motors for actuators
• Software agents
• Keystrokes, file contents, network packets as
  sensory inputs
• Display on the screen, writing files, sending
  network packets

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sensors
Environment
Agent
percepts
?
actions
actuators
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Agents

• Percept refers to the agents perceptual inputs
  at any given instant
• Percept sequence complete history of everything
  the agent has perceived
• An agents choice of action at any given instant
  can depend on the entire percept sequence
  observed to date
• Agent function - maps any given percept sequence
  to an action. This function describes the
  behavior of an agent

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Agents

• Agent function - an abstract mathematical
  description
• Agents program an implementation

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Agents

• Vacuum cleaner world
• Two locations squares A and B
• Vacuum agents perceives which square it is in and
  whether there is dirt in the square
• Vacuum agent can choose to move left, move right,
  Suck dirt, do nothing
• Agent function if current square is dirty, then
  suck , otherwise move to the other square
• Tabulation of agent function

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B
A
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Agents

• Partial tabulation of simple agent function
• Percept sequence action
• A, clean right
• A, dirty suck
• B, clean left
• B, dirty suck
• A, clean, A, clean right
• A, clean, A, dirty suck
• A, clean, A, clean, A, clean right
• A, clean, A, clean, A, dirty suck

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Agents

• Rational agent does the right thing, that is,
  every entry in the table for the agent function
  is filled out correctly
• Performance measure criterion for success of an
  agents behavior
• Sequence of actions based on percepts it received
  
• Sequence of actions causes the environment to go
  through a sequence of states
• If the sequence Is desirable, agent has performed
  well

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Agents

• Vacuum cleaner agent measure performance by the
  amount of dirt cleaned up in a single eight hour
  shift
• Clean, dump dirt, clean, dump dirt ?
• More suitable performance measure -reward agent
  for having a clean floor
• Design performance measures according to what one
  wants in the environment - not according to how
  one thinks the agent should behave

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Agents

• Rationality
• What is rational at any given time depends on
• performance measure that defines the criterion of
  success
• Agents prior knowledge of the environment
• Actions that the agent can perform
• Agents percept sequence to date

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Agents

• Definition
• 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
  whatever built-in knowledge the agent has

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Agents

• Vacuum cleaner agent cleans the square if it is
  dirty and moves to the other square if it is not
• Is this a rational agent?

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Agents

• What is its performance measure
• Performance measure awards one point for each
  clean square at each time step over a lifetime of
  1000 times steps
• What is known about the environment
• Geography of environment is known a priori. Dirt
  distribution and initial location of agent are
  not known. Clean squares stay clean. Sucking
  cleans the current square. Left and right move
  the agent

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Agents

• What sensors and actuators does it have
• Available actions left, right, suck, NoOp
• Agent perceives Its location and whether that
  location contains dirt
• Under these conditions, the agent is rational

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Agents

• Note
• rationality maximizes expected performance
• Perfection maximizes actual performance
• Rational agent
• Gathers information
• Learns as much as possible from what it perceives

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Agents

• Task of computing the agent function
• When the agent is being designed, some of the
  computation is done by its designers
• When it is deliberating on its next action, the
  agent does more computation
• The agent learns from experience it does more
  computation as it learns

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Agents

• Autonomous the agent should learn what It can
  to compensate for partial or incorrect prior
  knowledge
• Vacuum cleaning agent that learns to foresee
  where and when additional dirt will appear will
  do better than one that does not
• Agent will have some initial knowledge as well as
  an ability to learn. Initially it will make
  mistakes.

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Agents

• Specifying the task environment
• Vacuum cleaning agent
• Performance measure, and environment, sensors and
  actuators (PEAS) this is the task environment
• Automated taxi driver
• Performance measure getting to correct
  destination minimizing fuel consumption , wear
  and tear, trip time, cost, traffic law
  violations maximizing safety, comfort, profits
• Environment variety of roads, traffic,
  pedestrians, road works, etc. Interact with the
  passengers, snow

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Agents

• Actuators accelerator, steering, braking
  output to a display screen or voice synthesizer
  communicate with other vehicles
• Sensors controllable cameras, speedometer
  odometer, accelerometer, engine and other system
  sensors , GPS, sensors to detect distances to
  other cars and obstacles, keyboard or microphone
  for the passenger to request a destination

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Agents

• Properties of task environments
• Fully observable vs partially observable
• Fully observable If agents sensors give it
  access to the complete state of the environment
  at each point in time
• Partially observable because of noisy and
  inaccurate sensors or because parts of the state
  are missing from the sensor data

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Agents

• Deterministic vs stochastic
• Deterministic if the next state of the
  environment is completely determined by the
  current state and the action executed by the
  agent
• Otherwise stochastic taxi driving cannot
  predict traffic
• Episodic vs sequential
• Episodic agents experience is divided into
  atomic episodes. Episode consists of agent
  perceiving and performing a single action. Next
  episode does not depend on the actions taken in
  previous episodes
• Sequential current decision can affect future
  decisions

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Agents

• static vs dynamic
• dynamic if environment can change while an
  agent is deliberating
• Static otherwise
• Discrete vs continuous
• Time
• Percepts
• Actions
• Single agent vs multiagent
• Aim is to maximize performance
• Competitive multiagent environment
• Cooperative multiagent environment
• Communication in multiagent environment

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Agents

• Crossword puzzle
• Fully observable, deterministic, sequential,
  static, discreet, single agent
• Medical diagnosis
• Partially observable, stochastic, sequential,
  dynamic, continuous, single agent
• Can be multiagent deal with patients etc.

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Agents

• Structure
• So far talked about behavior
• Agent program implements the agent function
  mapping percepts to actions
• This program will run on some sort of computing
  device with physical sensors and actuators
  (architecture)
• agent architecture program

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Agents

• Skeleton of program
• Input current percept from sensors
• Output action to actuators
• Agent function takes entire percept history as
  input

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Agents

• Four basic kinds of agent programs
• simple reflex agents
• Model based reflex agents
• Goal based agents
• Utility based agents
• Can use a table look up approach but not
  practical

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Agents

• Function TABLE-DRIVEN_AGENT(percept) returns an
  action
• static percepts, a sequence initially empty
• table, a table of actions, indexed by percept
  sequence
• append percept to the end of percepts
• action ? LOOKUP(percepts, table)
• return action
• This approach is doomed to failure

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Agents

• Simple reflex agents
• simplest kind of agent
• Select actions on the basis of the current
  percept, ignoring the rest of the percept history
  
• Example vacuum cleaning agent
• Decision based on current location and whether
  location contains dirt

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Agents

• Code for vacuum cleaning agent
• function reflex-vacuum-agent (location,
  status) returns an action
• if status dirty then return suck
• else if location A then return right
• else if location B then return left

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Agents

• Condition-action rule
• Example
• if car-in-front-is-braking then initiate-braking

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Agents
sensors
Environment
Agent
percepts
What the world is like now
What action I should do now
actions
actuators
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Agents

• More general and flexible approach
• Build a general purpose Interpreter for condition
  action rules
• Create rule sets for specific task environments

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Agents

• function SIMPLE-REFLEX-AGENT(percept) returns an
  action
• static rules, a set of condition-action rules
• state ? INTERPRET-INPUT(percept)
• rule ? RULE-MATCH(state, rule)
• action ? RULE-ACTIONrule
• return action

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Agents

• state ? INTERPRET-INPUT(percept)
• This function generates an abstracted description
  of the current state from the percept
• rule ? RULE-MATCH(state, rule)
• This function returns the first row in the set of
  rules that matches the given state description
• action ? RULE-ACTIONrule
• Action defined

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Agents

• Current decision can be made on the basis of only
  the current percept and environment must be
  fully observable
• Suppose vacuum agent is deprived of its location
  sensor and has only a dirt sensor
• 2 possible percepts Dirty, Clean
• It can Suck in response to Dirty
• What about Clean?
• Moving Left fails (forever) if it happens to
  start in square A
• Moving Right fails (forever) if it happens to
  start in square B

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Agents

• Will only work if the environment is fully
  observable otherwise infinite loops may occur.
• Can exit from infinite loops if the agent can
  randomize its actions

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Agents

• Model based reflex agents
• Partial observability agent must keep track of
  the part of the word it cannot see now
• that is, agent maintains internal state that
  depends on the percept history and thereby
  reflects at least some of the unobserved aspects
  of the current state

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Agents

• Example changing lanes agent needs to keep
  track of where the other cars are If it cannot
  see them all at once

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Agents

• Updating internal state Information as time goes
  by
• Need some information about how the world
  involves independently of the agent
• overtaking car generally will be closer than it
  was a moment ago
• Need some information about how the agents own
  actions affect the world
• When agent turns the steering wheel clockwise,
  the car turns to the right
• this knowledge about how the world works a
  model of the world
• agent that uses such a model model based agent

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Agents
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Agents

• function REFLEX-AGENT-WITH-STATE(percept) returns
  an action
• static rules, a set of condition-action rules
• state, a description of the current world state
• action, the most recent action.
• state ? UPDATE-STATE(state, action, percept)
• rule ? RULE-MATCH(state, rule)
• action ? RULE-ACTIONrule
• return action

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Agents

• state ? UPDATE-STATE(state, action, percept)
• responsible for creating the new internal state
  description
• Interprets the new percept in the light of the
  existing knowledge about the state
• Uses information about how the world evolves to
  keep track of the unseen parts of the world
• Knows about what the agents actions do to the
  state of the world

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Agents

• Knowing about the current state of the
  environment is not always enough to decide what
  to do
• Agent needs some sort of goal information also
• Agent a program combines of this with information
  about the results of possible actions (the same
  information as was used to update internal state
  in the reflex agent) to choose the action

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Agents
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Agents

• Searching and planning are needed to find actions
  sequences that achieve the agents goals

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Agents

• Certain goals can be reached in different ways.
• Some are better, have a higher utility.
• Utility function maps a (sequence of) state(s)
  onto a real number.
• Describes the associated degree of happiness

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Agents

• Improves on goals
• Selecting between conflicting goals
• Speed and safety - utilities function specifies
  the appropriate tradeoff
• Select appropriately between several goals based
  on likelihood of success.

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Agents
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Agents

• All previous agent-programs describe methods for
  selecting actions.
• Yet it does not explain the origin of these
  programs.
• Learning mechanisms can be used to perform this
  task.
• Teach them instead of instructing them.
• Advantage is the robustness of the program toward
  initially unknown environments.

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Agents
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Agents

• Learning agent 4 components
• Learning element introduce improvements in
  performance element.
• Critic provides feedback on agents performance
  based on fixed performance standard.
• Performance element selecting actions based on
  percepts.
• Corresponds to the previous agent programs
• Problem generator suggests actions that will
  lead to new and informative experiences.
• Exploration vs. exploitation

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Agents

• Automated taxi agent
• Performance element collection of knowledge and
  procedures that taxi has for selecting its
  driving actions
• Taxi drives using this performance element
• Critic observes the world and passes information
  to the learning element
• Taxi makes quick left turn across three lanes of
  traffic critic observes abuse by other drivers.
• Learning element formulates a rule saying this is
  a bad action
• Performance element is modified by installing
  the new rule
• Problem generator identifies certain areas of
  behavior in need of improvement and suggest
  experiments