ai applications - ubc computer sciencekevinlb/teaching/cs322 - 2008-9/lectures/intro3.pdf ·...
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lAI Applications
CPSC 322 – Intro 3
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AI Applications CPSC 322 Lecture 3, Slide 2
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AI Applications CPSC 322 Lecture 3, Slide 3
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Search: Checkers
• Arthur Samuel at IBM t t lwrote programs to play
checkers (1950s)initially they played at a– initially, they played at a strong amateur level
– however, they used some (simple) machine learning techniques, and soon outperformed Samueloutperformed Samuel
AI Applications CPSC 322 Lecture 3, Slide 4
Source: IBM Research
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Search: ChessI 1996 d 1997 G K h ld h• In 1996 and 1997, Gary Kasparov, the world chess grandmaster played two tournaments against Deep Blue a program written by researchers at IBMBlue, a program written by researchers at IBM
AI Applications CPSC 322 Lecture 3, Slide 5
Source: IBM Research
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Search: ChessD Bl ’ R l i h fi• Deep Blue’s Results in the first tournament: – won 1 game, lost 3 and tied 1
• first time a reigning world champion lost to a computer• first time a reigning world champion lost to a computer• although Kasparov didn’t see it that way…
AI Applications CPSC 322 Lecture 3, Slide 6
Source: CNN
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Search: ChessD Bl ’ R l i h d• Deep Blue’s Results in the second tournament: – second tournament: won 3 games, lost 2, tied 1
AI Applications CPSC 322 Lecture 3, Slide 7
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CSPs: Crossword Puzzles
AI Applications CPSC 322 Lecture 3, Slide 8
Source: Michael Littman
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CSPs/Local Search: Dance Evolution
http://videolectures.net/aaai07_balogh_daevo
AI Applications CPSC 322 Lecture 3, Slide 9
“Best Student Video”, AAAI-07 video competition
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Planning: LogisticsD i A l i d R l i T l• Dynamic Analysis and Replanning Tool (Cross & Walker)
– logistics planning and scheduling for military transportsed in the 1991 G lf War b the US– used in the 1991 Gulf War by the US
– problems had 50,000 vehicles; different starting points and destinations
AI Applications CPSC 322 Lecture 3, Slide 10
Source: DARPA
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Planning: Spacecraft ControlNASA D S O fNASA: Deep Space One spacecraft• operated autonomously for two days in May, 1999:
– determined its precise position using stars and asteriods• despite a malfunctioning ultraviolet detector
– planned the necessary course adjustmentplanned the necessary course adjustment– fired the ion propulsion system to make this adjustment
AI Applications CPSC 322 Lecture 3, Slide 11
Source:NASA
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AI Applications CPSC 322 Lecture 3, Slide 12
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Logic: Cyc“Day after day since 1984 teams of programmers linguists“Day after day since 1984, teams of programmers, linguists, theologians, mathematicians and philosophers have plugged away at a $60-million project they hope will transform human existence: teaching a computer common sense.teaching a computer common sense.
“They have been feeding a database named Cyc over a million truths and generalities about daily life so it can automatically make assumptions humans make such as:assumptions humans make, such as:– Creatures that die stay dead.– Dogs have spines.
Scaling a cliff requires intense physical effort– Scaling a cliff requires intense physical effort.
“Though some critics question the potential of this painstaking effort, the inventors believe Cyc will form the brains of computers with supercharged reasoning abilities - which could help us work more efficiently, make us understand each other better and even help us predict the previously unforeseeable.”
AI Applications CPSC 322 Lecture 3, Slide 13
Quoted from: Independent Online
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Logic: Cyc
AI Applications CPSC 322 Lecture 3, Slide 14
Source: Cycorp
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Logic: CycSecure“C S t t k t b ild f l• “CycSecure scans a computer network to build a formal representation of the network, based on Cyc’s pre-existing ontology of networking, security, and computing concepts:gy g y p g p– information about what
computers are on the network, what programs are installed or p grunning on those computers, what privileges the running programs have, what users are logged into the computers, etc.
• This formal representation also allows users to interactalso allows users to interact directly with the model of the network, allowing testing of proposed changes ”
AI Applications CPSC 322 Lecture 3, Slide 15
proposed changes.Excerpted from: Shepard et al., 2005
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Logic: Visualizing Derivations
http://videolectures.net/aaai07_trac_idv
AI Applications CPSC 322 Lecture 3, Slide 16
from the AAAI-07 video competition
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AI Applications CPSC 322 Lecture 3, Slide 17
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Reasoning under Uncertainty: Diagnosis
AI Applications CPSC 322 Lecture 3, Slide 18
Source: Onisko et al., 99
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Reasoning Under UncertaintyT t l ifi ti i SVM• Texture classification using SVMs– foliage, building, sky, water
AI Applications CPSC 322 Lecture 3, Slide 19
Source: Mike Cora, UBC
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Reasoning Under UncertaintyC l B d Obj R i iColour-Based Object Recognition
“Most Innovative Vid ”Video”
AAAI-07 Video
AI Applications CPSC 322 Lecture 3, Slide 20
0 deoCompetition
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AI Applications CPSC 322 Lecture 3, Slide 21
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Planning Under Uncertainty: Aibo RL
AAAI-07 AIVC - 27. Aibo ingenuity (Littmann).mov
winner of “best short video” at AAAI-07 video competition
AI Applications CPSC 322 Lecture 3, Slide 22
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Planning Under UncertaintyL li i ffi b i h l fi dLocalization: office robot with laser rangefinder
Source:
AI Applications CPSC 322 Lecture 3, Slide 23
Source: Sebastian
Thrun
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Planning Under UncertaintySi l L li i & M i Mi iSimultaneous Localization & Mapping: Mine mapping
Source:
AI Applications CPSC 322 Lecture 3, Slide 24
Source: Sebastian
Thrun
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Planning Under UncertaintyH li l MDP i f l iHelicopter control: MDP, reinforcement learning
Source:
AI Applications CPSC 322 Lecture 3, Slide 25
Source: Andrew
Ng
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Planning Under UncertaintyA d i i DARPA G d Ch llAutonomous driving: DARPA Grand Challenge
Source:
AI Applications CPSC 322 Lecture 3, Slide 26
Source: Sebastian
Thrun
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AI Applications CPSC 322 Lecture 3, Slide 27