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Artificial Intelligence 15381

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ELSE Append(Succ(Scurr), S-queue) IF Null(S-queue), FAILURE ... THEN return APPEND(Path(treef), Path-1(treeb)) ELSE Replace Sb by Succ-1(Sb) & UPDATE (treeb) ... – PowerPoint PPT presentation

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Title: Artificial Intelligence 15381


1
Artificial Intelligence 15-381
  • 15-381 Artificial Intelligence
  • Today Introduction to AI Search Methods
  • (and Aministrivia)
  • Jaime Carbonell
  • jgc_at_cs.cmu.edu
  • 14-January-2003

2
Todays Topics
  • What exactly is AI, anyway?
  • AI searchknowledgelearning
  • AI application areas
  • Course Outline
  • Administration and grading ?
  • PROJECT Free-text Q/A
  • Basic search methods (Most of this lecture)

3
What is AI Some Quick Answers
  • From the Media AI is
  • What socially-inept superhackers do
  • The opposite of natural stupidity
  • Building useful idiot-savant programs
  • Deep Blue (IBMs chess program)
  • Robots with feelings (Spielberg)

4
What is AI Some Quick Answers (cont.)
  • From Academia AI is
  • modeling aspects of human cognition by computer
  • the study of ill-formed problems
  • "nothing more" than advanced algorithms research
  • cool stuff! Machine learning, data mining,
    speech, language, vision, web agentsand you can
    actually get paid a lot for having fun!
  • what other CS folks dont yet know how to do,
    and we AIers arent always too sure either

5
Operationally Speaking, AI is
  • Applied Cognitive Science
  • Computational models of human reasoning
  • Problem solving
  • Scientific thinking
  • Models of non-introspective mental processes
  • Language comprehension, language learning
  • Human memory organization (STM, LTM)

6
Operationally Speaking, AI is
  • Knowledge Engineering
  • Codify human knowledge for specific tasks
  • E.g. Medical diagnosis, Machine Translation
  • Central in 1970s 80s ? just one lecture here
  • Problem-Solving Methods
  • How to encode and use knowledge to find answer
  • E.g. HS, MEA, A, Logic resolution
  • Always at the very core of AI ? many lectures

7
Operationally Speaking, AI is
  • Machine Learning
  • Learning as the hallmark of intelligencebut it
    is already practical in multiple applications
  • E.g. D-trees, rule-induction, reinforcement,
    NNets
  • Discredited in 1960s ? Vibrant core 90s 00s
  • Applications data text mining, speech,
    robotics
  • Most active research area in AI ? many lectures

8
AI Application Areas
  • Rule-Based Expert Systems
  • Medical Diagnosis MYCIN, INTERNIST, PUFF
  • CSP Scheduling ISIS, Airline scheduling
  • Data Mining
  • Financial Fraud detection, credit scoring
  • Sales Customer preferences, inventory
  • Science NASA galaxy DB, genome analysis

9
AI Application Areas (cont.)
  • Language Processing
  • Speech dictation, HCI
  • Language Machine Translation
  • ML NLP Fact Extraction
  • ML words Information Retrieval
  • Robotics
  • Machine Vision
  • Mobile Robots agents
  • Manipulation

10
  • 15-381 AMINISTRIVA
  • Lectures on Web ( Occasional Recitations)
  • Text Artificial Intelligence A Modern
    Approach by Russell Norvig, Prentice Hall,
    Second Edition
  • Tasks Grading
  • Problem sets 1 2 10 each
  • Midterm 15 (closed book)
  • Final 25 (open book)
  • Project 40 (2 or 3
    person teams)

11
  • TAs
  • Andy Yang ayang_at_andrew
  • Maxim Likhachev likhachev_at_cs
  • Jing Xiao xiaoj_at_cs
  • Joao Pedro Suosa jpsousa_at_cs
  • Course Admin
  • Eleanor Cambridge eleanorc_at_cs
  • 4517 NSH x8-4788
  • Late Policy 10 lower grade if up to a week late

12
AI-Based Problem Solving
  • BASIC SEARCH METHODS
  • State-Space ltS, S0, SGj, Oigt
  • S0 Initial State
  • SG Goal State(s) to be reached
  • Oi Operators O S gt S

13
AI-Based Problem Solving (cont.)
  • State-Space Navigation
  • Forward Search BFS, DFS, HS,
  • Backward Search BFS-1, Backchaining,
  • Bi-Directional Search BFS2,
  • Goal Reduction Island-S, MEA
  • Transformation S ? S
  • Abstraction S ? SA MEA (SA)
  • Analogy If Sim(P,P) then Sol(P)? Sol(P)

14
More on the State Space
  • Useful Functions
  • Succ(si) sk oj(si) sk
  • Reachable(si) Usk Succ (si)
  • Succ-1(si) sk oj(sk) si)
  • Reachable-1(si) Usk (Succ-1)(si)
  • s-Path(sa0, san) (sa0, sa1,, san)
  • such that for all sa1 exists oj(sai) sai1
  • o-Path(sa0, san) (oj0, oj1,, ojn-1)
  • such that for all sa1 exists oj(sai) sai1

15
More on the State Space (cont.)
  • Useful Concepts
  • Solution o-Path(s0, sG) or s-Path
  • Cost(Solution) ? cost(oj) often cost(oj) 1
  • P is solvable if at least one o-Path(s0, sG)
    exists
  • Solutions may be constructed forward, backward or
    any which way
  • State spaces may be finite, infinite, implicit or
    explicit

16
Zero-Knowledge Search
  • Simple Depth-First Search
  • DFS(Scurr, Sgoal, S-queue)
  • IF Scurr Sgoal, SUCCESS
  • ELSE Append(Succ(Scurr), S-queue)
  • IF Null(S-queue), FAILURE
  • ELSE DFS(First(S-queue), Sgoal, Trail(S-queue))

17
Depth First Search
1
SI
2
7
3
8
5
4

SG
6
18
DFS (cont.)
  • Problems with DFS
  • Deep (possibly infinite) rat holes
  • ? depth-bounded DFS, D max depth
  • Loops Succ(Succ(..Succ(S))) S
  • ? Keep s-Path and always check Scurr
  • Non-Optimality Other paths may be less costly
  • ? No fix here for DFS
  • Worst-case time complexity (O(bmax(D,d))

19
DFS (cont.)
  • When is DFS useful?
  • Very-high solution density
  • Satisficing vs. optimizing
  • Memory-limited search O(d) space
  • Solution at Known-depth (then Dd)

20
Zero Knowledge Search (cont.)
  • Simple Breadth-First Search
  • BFS(Scurr, Sgoal, S-queue)
  • IF Scurr Sgoal, SUCCESS
  • ELSE Append(Succ(Scurr), S-queue)
  • IF Null(S-queue), FAILURE
  • ELSE BFS(Last(S-queue), Sgoal,
  • All-But-Last(S-queue))

21
Breadth-First Search
1
4
3
2
11
10
9
8
7
6
5
12

SG
22
Simple BFS cont.
  • Problems with BFS
  • Loops Succ(Succ(Succ(S)))S
  • Pseudo-loops Revisiting old states off-path
  • ? Keep full visited prefix tree
  • Worst case time complexity O(bd)
  • Worst case space complexity O(bd)
  • When is BFS Useful?
  • Guarantee shortest path
  • Very sparse solution space
  • (better if some solution is close to SI)

23
Zero Knowledge Search (cont.)
  • Backwards Breadth-First Search
  • BFS(Scurr, Sinit, S-queue)
  • IF Scurr Sinit, SUCCESS
  • ELSE Append(Succ-1(Scurr), S-queue)
  • IF Null(S-queue), FAILURE
  • ELSE BFS(Last(S-queue), Sinit,
  • All-But-Last(S-queue))

24
Backwards Breadth-First Search
9
SI

8
7
6
5
4
3
2
1
SG
25
Backward-BFS (cont.)
  • Problems with Backward-BFS
  • All the ones for BFS
  • Succ(Scurr) must be invertible Succ-1(Scurr)
  • When is Backward-BFS useful?
  • In general, same as BFS
  • If backward branching lt forward branching

26
Bi-Directional Search
  • Algorithm
  • Initialize Fboundary Sinit
  • Initialize Bboundary Sgoal
  • Initialize treef Sinit
  • Initialize treeb Sgoal
  • For every Sf in Fboundary
  • IF Succ(Sf) intersects Bboundary
  • THEN return APPEND(Path(treef), Path-1(treeb))
  • ELSE Replace Sf by Succ(Sf) UPDATE (treef)
  • 6. For every Sb in Bboundary
  • IF Succ(Sb) intersects Fboundary
  • THEN return APPEND(Path(treef),
    Path-1(treeb))
  • ELSE Replace Sb by Succ-1(Sb) UPDATE
    (treeb)
  • 7. Go to 5.
  • Note wheres the bug?

27
Bi-Directional Breadth-First Search
1
SI
3
4
8
9
10
11
12
13

7
5
6
2
SG
28
Bi-Directional Search (cont.)
  • Problems with Bi-BFS
  • Loops Succ(Succ(Succ(S))) S
  • Loops Succ-1(Succ-1( Succ-1(S)))) S
  • Pseudo-loops Revisiting old states off-path
  • ? Keep full visited prefix treef, trees
  • Succ(Scurr)must be invertible Succ-1(Scurr)
  • When is Bi-BFS useful?
  • Space and time complexity
  • O(bfd/2) O(bbd/2) O(bd/2) if bf bb

29
Island-Driven BFS
  • Definition
  • An island is a state known a-priori to be on the
    solution path between Sinit and Sgoal.
  • If there are k sequential islands
  • BFS(Sinit, S-(goal)
  • APPEND(BFS(Sinit, Sk1), BFS(Sk1, Sk2),BFS(SIk,
    Sgoal))
  • Upper bound complexity O(kmaxi0kbdki,ki1)
  • Complexity if islands are evenly spaced
  • O((k1)bd/(k1))

30
Island-Driven Search
1
SI

SIsland
SG
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