Refinement Planning: Status and Prospectus

1
1946 ENIAC heralds the dawn of Computing
2
1950 Turing asks the question.
I propose to consider the question
Can machines think?
--Alan Turing, 1950
3
1995 RALPH takes a trip from coast to coast
CMUs RALPH program drove a van for all but 52
miles of a trip from D.C. to San Diego
4
1996 EQP proves that Robbins Algebras are all
boolean
An Argonne lab program has come up with a major
mathematical proof that would have been called
creative if a human had thought of it.
-New
York Times, December, 1996
5
Jan 12, 1997 HAL 9000 becomes operational in
fictional Urbana, Illinois
by now, every intelligent person knew that
H-A-L is derived from Heuristic ALgorithmic
-Dr. Chandra, 2010 Odyssey Two

6
May, 1997 Deep Blue beats the World Chess
Champion
vs.
I could feel human-level intelligence across the
room -Gary Kasparov, World Chess
Champion (human)
7
May, 1999 Remote Agent takes Deep Space 1 on a
galactic ride
8
May 2000 SCIFINANCEsynthesizes programsfor
financial modeling

• Develop pricing models for complex derivative
  structures
• Involves the solution of a set of PDEs (partial
  differential equations)
• Integration of object-oriented design, symbolic
  algebra, and plan-based scheduling

9
Sept. 2002 Cindy Smart marketed

• Vision can read, tell the time
• Speech recognition can recognize 700 words and
  77 phrases
• Voice synthesis speaks with a soft voice

10
What else?

• Real-time response
• robustness
• autonomous intelligent interaction with the
  environment
• planning
• communication with natural language
• commonsense reasoning
• creativity
• learning
• ???

11
Administrivia

• Textbook Lugers Artificial Intelligence, 2002,
  Addison Wesley
• Grading
• Assignments 40
• Midterm Exam 1 20
• Midterm Exam 2 20
• Final Exam 20
• Academic honesty

12
Contents

• PART I Artificial Intelligence Its Roots and
  Scope
• Chapter 1 AI History and Applications
• PART II Artificial Intelligence as
  Representation and Search
• Chapter 2 The Predicate Calculus
• Chapter 3 Structures and Strategies for State
  Space Search
• Chapter 4 Heuristic Search
• Chapter 5 Control and Implementation of
  State-Space Search

13
Contents (contd)

• Part III Representation and Intelligence The AI
  Challenge
• Chapter 6 Knowledge Representation
• Chapter 7 Strong Method Problem Solving
• Chapter 8 Reasoning in Uncertain Situations

14
Contents (contd)

• Part IV Machine Learning
• Chapter 9 Machine Learning Symbol-based
• Chapter 10 Machine Learning Connectionist
• Chapter 11 Machine Learning Social and Emergent

15
Contents (contd)

• Part V Advanced Topics for AI Problem Solving
• Chapter 12 Automated Reasoning
• Chapter 13 Understanding Natural Language

16
Contents (contd)

• Part VI Languages and Programming Techniques for
  AI
• Chapter 14 An Introduction to Prolog
• Chapter 15 An Introduction to Lisp
• Part VII Epilogue
• Chapter 16 Artificial Intelligence as Empirical
  Enquiry

17
What is AI?
18
Figure 1.1 The Turing test.
19
Definitions of AI

• Systems that think like humans
• Systems that act like humans
• Systems that think rationally
• Systems that act rationally

20
Question

• What would impress you as an
• intelligent system?

21
Important Research and Application Areas

• Game playing
• Automated Reasoning and Theorem Proving
• Expert Systems
• Natural Language Understanding and Semantic
  Modeling
• Modeling Human Performance
• Planning and Robotics
• Languages and Environments for AI
• Machine Learning
• Alternative Representations Neural Nets and
  Genetic Algorithms
• AI and Philosophy

22
Important Features of AI

• The use of computers to do reasoning, pattern
  recognition, learning, or some other form of
  inference.
• A focus on problems that do not respond to
  algorithmic solutions. This underlies the
  reliance on heuristic search as an AI
  problem-solving technique.
• A concern with problem solving using inexact,
  missing, or poorly defined information and the
  use of representational formalisms that enable
  the programmer to compensate for these problems.

23
Important Features of AI (contd)

• Reasoning about the significant qualitative
  features of a situation.
• An attempt to deal with issues of semantic
  meaning as well as syntactic form.
• Answers that are neither exact nor optimal, but
  are in some sense sufficient. This is a result
  of the essential reliance on heuristic
  problem-solving methods in situations where
  optimal or exact results are either too expensive
  or not possible.

24
Important Features of AI (contd)

• The use of large amounts of domain-specific
  knowledge in solving problems. This is the basis
  of expert systems.
• The use of meta-level knowledge to effect more
  sophisticated control of problem solving
  strategies. Although this is a very difficult
  problem, addressed in relatively few current
  systems, it is emerging as an essential area of
  research.