CMPUT 366 Intelligent Systems:

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CMPUT 366 Intelligent Systems

• Introduction to Artificial Intelligence

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Instruction Team

• Prof Dekang Lin
• Office hours Tue, Thur 330-430, or by
  appointment
• Phone [phone]
• TAs Yaling Pei, Mark Schmidt, Gang Wu
• E-mail c366_at_cs.ualberta.ca
• Home Page http//www.cs.ualberta.ca/lindek/366
• Announcements
• Slides
• Assignments

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Textbooks

• Required 
• S Russell and P Norvig, Artificial Intelligence
  A Modern Approach, Prentice Hall, 1995.
• Recommended
• D Poole, A Mackworth and R Goebel, Computational
  Intelligence A Logical Approach , Oxford, 1998.
• Nilsson, Artificial Intelligence A New
  Synthesis, Morgan Kaufmann, 1998.

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Evaluation

• 4 Assignments
• 16 each. Solo! (see code of conducts)
• Paper/Pencil
• Submit hard copy on due date before class, write
  ligibly
• Implementations (C/Java)
• Submit using try. The deadline is 1159pm on
  the due date.
• The implementations must run on the lab machines
  (in CSC 219)
• Final Exam
• 36

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Other Issues

• Prerequisites
• Programming skills (C, Java)
• Elementary probability theory
• AI Seminar
• http//www.cs.ualberta.ca/ai/seminars
• Friday noons, CSC333
• Neat topics, great speakers, FREE PIZZA!

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Course Overview

• Introduction intelligent agent
• Search and constraint satisfaction
• Logical agent and planning
• Probabilistic reasoning
• Natural language and speech
• Perception (if there is time)

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What is Artificial Intelligence (AI)?

• Discipline that systematizes and automates
  intellectual tasks to create machines that

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Act Like Humans

• AI is the art of creating machines that perform
  functions that require intelligence when
  performed by humans
• Methodology Take an intellectual task at which
  people are better and make a computer do it

• Prove a theorem
• Play chess
• Plan a surgical operation
• Diagnose a disease
• Navigate in a building

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Turing Test

• Alan Turing, a mathematician who not only cracked
  the German code making machine, Enigma during the
  Second World War, but invented the concept of
  computers as we know them.
• Turing asserted that if you can fool a human into
  believing that he/she is receiving answers from
  another human when in fact it is a computer, this
  proves that computers are doing essentially what
  human brains do.

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• Can machines think -gt Can machines behave
  intelligently?
• Operational test of intelligence Imitation Game
• Problem
• Turing Test is not reproducible, constructive, or
  amenable to mathematical analysis.

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Think Like Humans

• How the computer performs functions does matter
• Comparison of the traces of the reasoning steps
• Cognitive science ? testable theories of the
  workings of the human mind

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Examples

• Garden-Path Sentence
• The horse raced past the barn fell.
• Center-embedding
• The cat that the dog that the mouse that the
  elephant admired bit chased died.
• The elephant admired the mouse that bit the dog
  that chased the cat that died.

But, do we want to duplicate human imperfections?
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Think Rationally Laws of Thought

• Normative (or prescriptive) rather than
  descriptive
• Aristotle what are correct arguments/thought
  processes?
• Several Greek schools developed forms of logic
  notation and rules of derivation for thoughts.
• Problems
• Not all intelligent behavior is mediated by
  logical deliberation
• What is the purpose of thinking? What thoughts
  should I have?

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Act Rationally

• Rational behavior doing the right thing
• The right thing
• that which is expected to maximize goal
  achievement, given the available information
• Limited resource, imperfect knowledge
• Rationality ? Omniscience, Rationality ?
  Clairvoyance, Rationality ? Successes
• Doesn't necessarily (but often) involve thinking
• Ignores the role of consciousness, emotions, fear
  of dying,
• Doesnt necessarily have anything to do with how
  humans solve the same problem.

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Example Semantic Orientation

• In many tasks, it is necessary to determine the
  semantic orientation of words
• Mining movie reviews
• Routing custermer e-mail
• Turney 2002
• Determine the semantic orientation of words using
  internet search engines.

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AI History
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Trends Since 90s

• Relying less on logic and more on probability
  theory and statistics.
• More emphasis on objective performance
  evaluation.
• Intelligent Agents
• Accomplishments in
• Game playing Deep blue, Chinook,
• Space Probe
• Biological sequence analysis
• OCR
• Consumer electronics

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Notion of an Agent
Source robotics.stanford.edu/latombe/cs121/2003/
home.htm
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Notion of an Agent

• Locality of sensors/actuators
• Imperfect modeling
• Time/resource constraints
• Sequential interaction
• Multi-agent worlds

Source robotics.stanford.edu/latombe/cs121/2003/
home.htm
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Example Tracking a Target

• The robot must keep the target in view
• The targets trajectory is not known in
  advance
• The robot may not know all the obstacles in
  advance
• Fast decision is required

Source robotics.stanford.edu/latombe/cs121/2003/
home.htm
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What is Artificial Intelligence? (revised)

• Study of design of rational agents
• agent thing that acts in environment
• Rational agent agent that acts rationally
• actions are appropriate for goals and
  circumstances to changing environments and goals
• learns from experience

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Goals of Artificial Intelligence

• Scientific goal
• understand principles that make rational
  (intelligent) behavior possible, in natural or
  artificial systems.
• Engineering goal
• specify methods for design of useful, intelligent
  artifacts.
• Psychological goal
• understanding/modeling people
• cognitive science (not this course)

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Goals of This Course

• Introduce key methods techniques from AI
• searching,
• reasoning and decision making (logical and
  probabilistic)
• learning (covered in detail in CMPUT466)
• language understanding,
• . . .
• Understand applicability and limitations of these
  methods

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Goals of This Course

• Our approach
• Characterize Environments
• Identify agent that is most effective for each
  environment
• Study increasingly complicated agent
  architectures requiring
• increasingly sophisticated representations,
• increasingly powerful reasoning strategies

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Intelligent Agents

• Definition An Intelligent Agent perceives its
  environment via sensors and acts rationally upon
  that environment with its acutators.
• Hence, an agent gets percepts one at a time, and
  maps this percept sequence to actions.
• Properties
• Autonomous
• Interacts with other agents
• plus the environment
• Adaptive to the environment
• Pro-active (goal-directed)

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Applications of Agents

• Autonomous delivery/cleaning robot
• roams around home/office environment, delivering
  coffee, parcels,. . . vacuuming, dusting,. . .
• Diagnostic assistant helps a human troubleshoot
  problems and suggest repairs or treatments.
• E.g., electrical problems, medical diagnosis.
• Infobot searches for information on computer
  system or network.
• Autonomous Space Probes
• . . .

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Task Environments PEAS

• Performance Measure
• Criterion of success
• Environment
• Actuators
• Mechanisms for the agent to affect the
  environment
• Sensors
• Channels for the agent to perceive the environment

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Example Taxi Driving

• Performance Measure
• Safe, fast, legal, comfortable trip, maximize
  profit
• Environment
• Roads, other traffic, pedestrians, customers
• Actuators
• Steering, accelerator, break, signal, horn,
• Sensors
• Cameras, sonar, speedometer, GPS,

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Types of Environments

• Fully observable (accessible) or not
• Deterministic vs. stochastic
• Episodic vs. sequential
• Static vs. dynamic
• Discrete vs. continuous
• Single agent vs. multiagent
• competitive vs. cooperative

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Example Cleaning Agent
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• Performance Measure
• ??
• Environment
• ??
• Actuators
• ??
• Sensors
• ??

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SurfBot

• Automated web surfing
• A SurfBot operates in the environment of the web.
• takes in high-level, perhaps informal, queries
• finds relevant information
• presents information in meaningful way

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• Performance Measure
• ??
• Environment
• ??
• Actuators
• ??
• Sensors
• ??

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Agent Function and Program

• Agent specified by agent function
• mapping percept sequences to actions
• Aim Concisely implement rational agent
  function
• Agent program
• input a single percept-vector
• (keeps/updates internal state)
• returns action

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Skeleton Agent Program

• function SkeletonAgent(percept) returns action
• static memory, agent's memory of the world
• memory ? UpdateMemory(memory,percept)
• action ? ChooseBestAction(memory)
• memory ? UpdateMemory(memory, action)
• return action

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Types of Agents

• Simple reflex agents
• Actions are determined by sensory input only
• Model-based reflex agents
• Has internal states
• Goal-based agents
• Action may be driven by a goal
• Utility-based agents
• Maximizes a utility function

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Simple Reflex Agent
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Example

• A LEGO MindStormTM program
• if (isDark(leftLightSensor))
• turnLeft()
• else if (isDark(rightLightSensor)) turnRight()
• else goStraight()
• Whats the agent function?

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Model-Based Agent
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Goal-based Agent
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Utility-based Agent
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Summary

• What is AI?
• Rationality
• A bit of History
• Intelligent Agent
• PEAS
• Types of Agents