Download Intro part 1 - Department of Computer Science

CS 475:
Uncertainty and Multi-Agent Systems
Prof. Bart Selman
selman@cs.cornell.edu
Introduction
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Bart Selman
CS 475
Overview of this Lecture
• Motivation behind new course
• Course administration
• Role of uncertainty and multi-agent systems in AI
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Bart Selman
CS 475
Motivation
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CS 475
A new course
The field of AI has grown tremendously over the last two decades, both
in terms of range of topics and technical depth.
A single introductory course no longer works.
So, this course complements CS 472.
CS 472: Search, Adversarial Search, Planning, Knowledge Represention
and Reasoning, and Learning. (Parts I, II, III, IV, & VI R&N.)
CS 475: Uncertainty (Part V, R&N) and Multi-Agent Systems (t.b.d.)
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CS 475
Course Administration
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Bart Selman
CS 475
CS 475
Lectures: Wedns & Fridays – 2:55pm – 4:10pm
Location: OH 216
Lecturer: Prof. Selman
Office: 4148 Upson Hall
Email: selman@cs.cornell.edu
Administrative Assistant: Beth Howard
(bhoward@cs.cornell.edu)
5136 Upson Hall, 255-4188
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CS 475
Grading (tentative)
Midterm
(15%)
Homework
(40%)
Participation
(10%)
Final
(35%)
Note: The lowest homework grade will be dropped before the
final grade is computed.
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CS 475
Textbook
Artificial Intelligence: A Modern Approach (AIMA)
(Second Edition) by Stuart Russell and Peter Norvig
Probabilistic Reasoning in Intelligent Systems : Networks of
Plausible Inference by Judea Pearl
Learning Bayesian Networks by Richard Neapolitan
Multi-Agent Systems: A Modern Approach to Distributed AI
by Gerhard Weiss
Bart Selman
CS 475
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Emergence of Uncertainty in AI
Historical Perspective
AI: Obtaining an understanding of the human mind is one of the
final frontiers of modern science.
Founders:
Aristotle, George Boole, Gottlob Frege, and Alfred Tarski
formalizing the laws of human thought
Alan Turing, John von Neumann, and Claude Shannon
thinking as computation
John McCarthy, Marvin Minsky, Herbert Simon, and Allen Newell
the start of the field of AI (1959)
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CS 475
•
1959 – 1985 Logical representations and symbolic processing were at the core
of AI.
Precise. Models much of mathematical thought. Also, human artifacts
such as circuits and complex machinery. Well-understood syntax and
semantics. Computational principles well-understood (inference).
Moreover: “symbolic processing and representations” were actually novel
for computing. Handcrafted knowledge works well in specialized
domains in e.g. expert systems and automated diagnosis systems.
“knowledge driven”
[Aside: probabilistic methods, such as Markov models, were quite familiar in the
50s and 60s from work in EE on information and signal processing.]
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CS 475
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1985 – 2005 emergence of probabilistic and statistical methods in AI.
Can deal with noisy input (sensor) data, and many forms of uncertain
information. Can exploit statistical regularities in large data-sets, leading to
statistical machine learning (less need for hand-crafted encoding of
knowledge). Computational techniques were developed. Probabilistic
framework (e.g. Bayesian nets, Markov random fields) bridge different areas
such as reasoning, nlp, vision, machine learning, and bio-info.
“data-driven”
[Aside: value of probabilistic models was also discovered “later” in
the other fields --- e.g. statistical physics (1870+) & quantum physics
(1910+) and in computation (randomized algorithms 1970+).]
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CS 475
• 2006+ ? Possibly a merger of probabilistic information (“soft
constraints / preferences”) and logical information (“hard constraints”).
Consider a robot moving around: a probabilistic (continuous) model of its most
likely location seems appropriate given noisy sensory inputs. However, in
reasoning and making plans about its environment an appropriate
discretization may be needed (e.g. “door is either closed or open”; “words” in
language; component-wise designs).
“Hierarchical models: lower-level statistical (from sensory input) & higherlevels symbolic (from cognition).”
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CS 475
Emergence of Multi-Agent Systems
1959—1985 Mostly single agent, problem solving / task-oriented
perspective
Examples: medical diagnosis systems or Deep Blue.
1985---2005 Shift to autonomous, interacting AI systems (“agents”).
Examples: shopping and bidding “agents” (e.g., TAC competition) and
distributed sensor networks.
Brings in ideas from economics, game theory, auctions, coordination, and
distributed computing.
Open issue: do truly new aspects of intelligence “emerge” in a distributed setting?
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