Introduction: What is AI

1
Introduction What is AI?

• CMSC 25000
• Introduction to Artificial Intelligence
• January 3, 2002

2
Agenda

• Course goals
• Course description and syllabus
• What is Artificial Intelligence?

3
Course Goals

• Understand reasoning, knowledge representation
  and learning techniques of artificial
  intelligence
• Evaluate the strengths and weaknesses of these
  techniques and their applicability to different
  tasks
• Understand their roles in complex systems
• Assess the role of AI in gaining insight into
  intelligence and perception

4
Instructional Approach

• Readings
• Provide background and detail
• Class sessions
• Provide conceptual structure
• Homework
• Provide hands-on experience
• Explore and compare techniques

5
Course Organization

• Knowledge representation manipulation
• Reasoning, Planning,..
• Acquisition of new knowledge
• Machine learning techniques
• AI at the interfaces
• Perception - Language, Speech, and Vision

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Course Materials

• Textbook
• Artificial Intelligence, 3rd edition
• by Patrick H. Winston
• Lecture Notes
• Available on-line for reference

7
Homework Assignments

• Weekly
• due Tuesdays in class
• Implementation and analysis
• Programming assignments in Scheme
• Tested under Dr Scheme
• Available in Regentstein Linux MAC labs
• Advanced or higher
• Simply Scheme - good reference
• TA Discussion List for help
• http//mailman.cs.uchicago.edu -- cs25000

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Homework Comments

• Homework will be accepted late
• 10 off per day
• Collaboration is permitted on homework
• Write up your own submission
• Give credit where credit is due

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Grading

• Homework 40
• Midterm 30
• Final Exam30

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Course Resources

• Web page
• classes.cs.uchicago.edu/classes/archive/2002/
  winter/cs25000/
• Lecture notes, syllabus, homework assignments,..
• Staff
• Instructor Gina-Anne Levow, levow_at_cs
• Office Hours Thursday 230-430 pm, Ry162C
• TA Nandakumar Raghunathan, nanda_at_cs
• Office Hours Monday 4-6 pm, Ry 178

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Questions of Intelligence

• How can a limited brain respond to the incredible
  variety of world experience?
• How can a system learn to respond to new events?
• How can a computational system model or simulate
  perception? Reasoning? Action?

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What is AI?

• Perspectives (Russel Norvig)
• The study and development of systems that
• Think and reason like humans
• Cognitive science perspective
• Think and reason rationally
• Act like humans
• Turing test perspective
• Act rationally
• Rational agent perspective

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Focus

• Study computational models that enable systems to
• reason
• perceive, and
• act
• Solve real-world (not toy) problems
• Formal systems enable assessment of psychological
  and linguistic theories

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Solving Real-World Problems

• Airport gate scheduling
• Satisfy constraints on gate size, passenger
  transfers, traffic flow
• Uses AI techniques of constraint propagation,
  rule-based reasoning, and spatial planning
• Disease diagnosis (Quinlans ID3)
• Database of patient information disease state
• Learns set of 3 simple rules, using 5 features to
  diagnose thyroid disease

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Evaluation Linguistic Theories

• Principles and Parameters theory proposes small
  set of parameters to account for grammatical
  variation across languages
• E.g. S-V-O vs S-O-V order, null subject
• PAPPI (Fong 1991) implements theory
• Converts English parser to Japanese by switch of
  parameter and dictionary

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Challenges

• Limited resources
• Artificial intelligence computationally demanding
• Many tasks NP-complete
• Find reasonable solution, in reasonable time
• Find good fit of data and process models
• Exploit recent immense expansion in storage,
  memory, and processing

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Studying AI

• Computations that enable reasoning, action, and
  perception
• Knowledge Representation and Reasoning
• What do we know, how do we model it, how we
  manipulate it
• Rule systems, search, constraint propagation
• Machine learning
• Applications to perception and action
• Language, speech, vision, robotics.