Download Refinement Planning: Status and Prospectus

1946: ENIAC heralds the dawn of Computing
1950: Turing asks the question….
I propose to consider the question:
“Can machines think?”
--Alan Turing, 1950
1995: RALPH takes a trip from
coast to coast
CMU’s RALPH program drove a van for all but 52 miles
of a trip from D.C. to San Diego
1996: EQP proves that
Robbin’s Algebras are all boolean
----- EQP 0.9, June 1996 ----The job began on eyas09.mcs.anl.gov, Wed Oct 2 12:25:37 1996
UNIT CONFLICT from 17666 and 2 at 678232.20 seconds.
---------------- PROOF ---------------2 (wt=7) [] -(n(x + y) = n(x)).
3 (wt=13) [] n(n(n(x) + y) + n(x + y)) = y.
5 (wt=18) [para(3,3)] n(n(n(x + y) + n(x) + y) + y) = n(x + y).
6 (wt=19) [para(3,3)] n(n(n(n(x) + y) + x + y) + y) = n(n(x) + y).
…….
17666 (wt=33) [para(24,16426),demod([17547])] n(n(n(x) + x) ….
[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
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
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)
May, 1999: Remote Agent takes
Deep Space 1 on a galactic ride
Goals
Scripts
Scripted
Executive
ESL
Mission-level
actions &
resources
Generative
Planner &
Scheduler
Generative
Mode Identification
& Recovery
component models
Monitors
Real-time Execution
Adaptive Control
Hardware
For two days in May, 1999, an AI Program called Remote Agent
autonomously ran Deep Space 1 (some 60,000,000 miles from earth)
May 2000: SCIFINANCE
synthesizes programs
for financial modeling
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Develop pricing
models for complex
derivative structures
Involves the solution of
a set of PDEs (partial
differential equations)
Integration of objectoriented design,
symbolic algebra, and
plan-based scheduling
Sept. 2002:
Cindy Smart marketed
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Vision: can read, tell
the time
Speech recognition:
can recognize 700
words and 77 phrases
Voice synthesis:
speaks with a soft
voice
What else?
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Real-time response
robustness
autonomous intelligent interaction with the
environment
planning
communication with natural language
commonsense reasoning
creativity
learning
???
Administrivia
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Textbook: Luger’s Artificial Intelligence,
2002, Addison Wesley
Grading:
– Assignments
40%
– Midterm Exam 1
20%
– Midterm Exam 2
20%
– Final Exam
20%
Academic honesty
Contents
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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
Contents (cont’d)
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Part III: Representation and Intelligence:
The AI Challenge
– Chapter 6: Knowledge Representation
– Chapter 7: Strong Method Problem
Solving
– Chapter 8: Reasoning in Uncertain
Situations
Contents (cont’d)
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Part IV: Machine Learning
– Chapter 9: Machine Learning: Symbolbased
– Chapter 10: Machine Learning:
Connectionist
– Chapter 11: Machine Learning: Social
and Emergent
Contents (cont’d)
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Part V: Advanced Topics for AI Problem
Solving
– Chapter 12: Automated Reasoning
– Chapter 13: Understanding Natural
Language
Contents (cont’d)
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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
What is AI?
Figure 1.1: The Turing test.
Definitions of AI
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Systems that think like humans
Systems that act like humans
Systems that think rationally
Systems that act rationally
Question:
What would impress you as an
“intelligent system?”
Important Research and
Application Areas
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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
Important Features of AI
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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.
Important Features of AI
(cont’d)
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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.
Important Features of AI
(cont’d)
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The use of large amounts of domainspecific 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.