Transcript Introduction: What is AI?

Introduction: What is AI?
CMSC 25000
Introduction to Artificial Intelligence
January 3, 2002
Agenda
• Course goals
• Course description and syllabus
• What is Artificial Intelligence?
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
Instructional Approach
• Readings
– Provide background and detail
• Class sessions
– Provide conceptual structure
• Homework
– Provide hands-on experience
– Explore and compare techniques
Course Organization
• Knowledge representation & manipulation
– Reasoning, Planning,..
• Acquisition of new knowledge
– Machine learning techniques
• AI at the interfaces
– Perception - Language, Speech, and Vision
Course Materials
• Textbook
– Artificial Intelligence, 3rd edition
• by Patrick H. Winston
• Lecture Notes
– Available on-line for reference
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
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
Grading
• Homework: 40%
• Midterm:
30%
• Final Exam:30%
Course Resources
• Web page:
– classes.cs.uchicago.edu/classes/archive/2002/
winter/cs25000/
• Lecture notes, syllabus, homework assignments,..
• Staff:
– Instructor: Gina-Anne Levow, levow@cs
• Office Hours: Thursday 2:30-4:30 pm, Ry162C
– TA: Nandakumar Raghunathan, nanda@cs
• Office Hours: Monday 4-6 pm, Ry 178
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?
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
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
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 (Quinlan’s ID3)
– Database of patient information + disease state
– Learns set of 3 simple rules, using 5 features to
diagnose thyroid disease
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
Challenges
• Limited resources:
– Artificial intelligence computationally
demanding
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•
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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
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.