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CS 475:Uncertainty and Multi-Agent Systems

Prof. Bart Selmanselman@cs.cornell.edu

Introduction

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Overview of this Lecture

• Motivation behind new course

• Course administration

• Role of uncertainty and multi-agent systems in AI

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Motivation

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The field of AI has grown tremendously over the last two decades, bothin 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 Representionand 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.)

A new course

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Course Administration

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Lectures: Wedns & Fridays – 2:55pm – 4:10pmLocation: OH 216

Lecturer: Prof. SelmanOffice: 4148 Upson HallEmail: selman@cs.cornell.edu

Administrative Assistant: Beth Howard (bhoward@cs.cornell.edu)    5136 Upson Hall, 255-4188

CS 475

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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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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

Multi-Agent Systems: A Modern Approach to Distributed AIby Gerhard Weiss

Learning Bayesian Networks by Richard Neapolitan

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Emergence of Uncertainty in AIHistorical Perspective

Emergence of Uncertainty in AIHistorical 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 Tarskiformalizing 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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• 1959 – 1985 Logical representations and symbolic processing were at the core of AI.

Precise. Models much of mathematical thought. Also, human artifactssuch 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 specializeddomains in e.g. expert systems and automated diagnosis systems.

“knowledge driven”

[Aside: probabilistic methods, such as Markov models, were quite familiar in the50s and 60s from work in EE on information and signal processing.]

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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 tostatistical 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 areassuch as reasoning, nlp, vision, machine learning, and bio-info.

“data-driven”

[Aside: value of probabilistic models was also discovered “later” inthe other fields --- e.g. statistical physics (1870+) & quantum physics(1910+) and in computation (randomized algorithms 1970+).]

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• 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) & higher-levels symbolic (from cognition).”

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Emergence of Multi-Agent SystemsEmergence 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) anddistributed sensor networks.

Brings in ideas from economics, game theory, auctions, coordination, anddistributed computing.

Open issue: do truly new aspects of intelligence “emerge” in a distributed setting?

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