Intelligent planning: a decomposition and abstraction based approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
1997
|
| Schriftenreihe: | Artificial intelligence
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXII, 252 S. graph. Darst. |
| ISBN: | 3540619011 |
Internformat
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| 100 | 1 | |a Yang, Qiang |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Intelligent planning |b a decomposition and abstraction based approach |c Qiang Yang |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 1997 | |
| 300 | |a XXII, 252 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Artificial intelligence | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Artificial intelligence -- Congresses | |
| 650 | 4 | |a Planning -- Data processing -- Congresses | |
| 650 | 0 | 7 | |a Automatische Handlungsplanung |0 (DE-588)4211191-2 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)1071861417 |a Konferenzschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Automatische Handlungsplanung |0 (DE-588)4211191-2 |D s |
| 689 | 0 | |5 DE-604 | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-007450050 | |
Datensatz im Suchindex
| _version_ | 1807683743115116544 |
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| adam_text |
TABLE
OF
CONTENTS
1.
INTRODUCTION
.
1
1.1
THE
PROBLEM
.
1
1.2
KEY
ISSUES
.
1
1.3
PLANNING
VERSUS
SCHEDULING
.
4
1.4
CONTRIBUTIONS
AND
ORGANIZATION
.
6
1.5
BACKGROUND
.
8
PART
I.
REPRESENTATION,
BASIC
ALGORITHMS,
AND
ANALYTICAL
TECHNIQUES
2.
REPRESENTATION
AND
BASIC
ALGORITHMS
.
15
2.1
BASIC
REPRESENTATION
.
15
2.1.1
DOMAIN
DESCRIPTION
.
15
2.1.2
PARTIAL-ORDER
AND
PARTIAL-INSTANTIATION
PLANS
.
17
2.2
BASIC
PLANNING
ALGORITHMS
.
19
2.3
PARTIAL-ORDER,
BACKWARD-CHAINING
ALGORITHMS
.
20
2.3.1
CORRECTNESS
.
21
2.3.2
THREAT
DETECTION
AND
RESOLUTION
.
23
2.3.3
ESTABLISHING
PRECONDITIONS
.
23
2.3.4
A
HOUSE-PAINTING
EXAMPLE
.
25
2.4
TOTAL-ORDER,
FORWARD-CHAINING
ALGORITHMS
.
27
2.5
MORE
ADVANCED
OPERATOR
REPRESENTATIONS
.
29
2.6
SEARCH
CONTROL
.
31
2.7
SATISFACTION
BASED
METHODS
.
32
2.7.1
OVERVIEW
.
32
2.7.2
MODEL
DEFINITION
.
33
2.7.3
MODEL
SATISFACTION
AND
PLAN
CONSTRUCTION
.
35
2.8
BACKGROUND
.
37
3.
ANALYTICAL
TECHNIQUES
.
39
3.1
BASIC
ANALYTICAL
TECHNIQUES
.
39
3.2
ANALYZING
FORWARD-CHAINING,
TOTAL-ORDER
PLANNERS
.
40
3.3
ANALYZING
PARTIAL-ORDER
PLANNERS
.
41
XII
TABLE
OF
CONTENTS
3.4
CASE
STUDY:
AN
ANALYSIS
OF
T
WEAK
.
44
3.4.1
AN
IMPLEMENTATION
OF
T
WEAK
.
44
3.4.2
ANALYZING
T
WEAK
.
46
3.5
CRITICS
OF
BASIC
PLANNERS
.
46
3.6
BACKGROUND
.
47
4.
USEFUL
SUPPORTING
ALGORITHMS
.
49
4.1
PROPOSITIONAL
UNIFICATION
ALGORITHM
.
49
4.2
GRAPH
ALGORITHMS
.
51
4.3
DYNAMIC
PROGRAMMING
.
54
4.4
BRANCH
AND
BOUND
.
56
4.5
CONSTRAINT
SATISFACTION
.
58
4.5.1
LOCAL-CONSISTENCY
BASED
METHODS
.
59
4.5.2
BACKTRACK-FREE
ALGORITHM
.
60
4.5.3
BACKTRACK-BASED
ALGORITHMS
.
61
4.6
THE
G
SAT
ALGORITHM
.
64
4.7
BACKGROUND
.
66
5.
CASE
STUDY:
COLLECTIVE
RESOURCE
REASONING
.
67
5.1
EAGER
VARIABLE-BINDING
COMMITMENTS
.
67
5.2
EXTENDING
THE
REPRESENTATION
.
69
5.3
PLAN
GENERATION
.
71
5.3.1
CORRECTNESS
CHECK
.
71
5.3.2
THREAT
DETECTION
.
71
5.3.3
PRECONDITION
ESTABLISHMENT
.
72
5.4
A
HOUSE-PAINTING
EXAMPLE
.
72
5.5
A
VARIABLE-SIZED
BLOCKS
WORLD
DOMAIN
.
76
5.6
SUMMARY
.
78
5.7
BACKGROUND
.
78
PART
II.
PROBLEM
DECOMPOSITION
AND
SOLUTION
COMBINATION
6.
PLANNING
BY
DECOMPOSITION
.
85
6.1
DECOMPOSITION
PLANNING
.
85
6.1.1
TWO
EXAMPLES
.
85
6.1.2
GLOBAL
PLANNING-DOMAIN
DECOMPOSITION
.
86
6.2
EFFICIENCY
BENEFITS
OF
DECOMPOSITION
.
87
6.2.1
FORWARD-CHAINING,
TOTAL-ORDER
PLANNERS
.
87
6.2.2
BACKWARD-CHAINING,
PARTIAL-ORDER
PLANNERS
.
90
6.2.3
CRITERIA
FOR
GOOD
DECOMPOSITION
.
92
6.3
GOAL-DIRECTED
DECOMPOSITION:
THE
GDECOMP
ALGORITHM
.
92
6.4
OTHER
BENEFITS
OF
DECOMPOSITION
.
95
6.5
ALTERNATIVE
APPROACHES
TO
DECOMPOSITION
PLANNING
.
95
TABLE
OF
CONTENTS
XIII
6.6
SUMMARY
.
99
6.7
BACKGROUND
.
100
7.
GLOBAL
CONFLICT
RESOLUTION
.
101
7.1
GLOBAL
CONFLICT
RESOLUTION
-
AN
OVERVIEW
.
101
7.2
CONFLICT
RESOLUTION
CONSTRAINTS
.
102
7.2.1
CONFLICTS
AND
CONFLICT
RESOLUTION
METHODS
.
102
7.2.2
RELATIONS
AMONG
CONFLICT
RESOLUTION
METHODS
.
104
7.3
CONFLICT
RESOLUTION
AS
CONSTRAINT
SATISFACTION
.
106
7.3.1
REPRESENTATION
.
106
7.3.2
PROPAGATING
CONSTRAINTS
AMONG
CONFLICTS
.
107
7.3.3
REDUNDANCY
REMOVAL
VIA
SUBSUMPTION
RELATIONS
.
107
7.4
THE
PAINTING
EXAMPLE
.
110
7.5
WHEN
IS
THE
CONSTRAINT-BASED
METHOD
USEFUL?
.
112
7.6
THE
C
OMBINE
ALGORITHM
.
112
7.7
RELATED
WORK
.
115
7.7.1
OVERVIEW
OF
PREVIOUS
WORK
.
115
7.7.2
RELATED
WORK
BY
SMITH
AND
PEOT
.
116
7.7.3
RELATED
WORK
BY
ETZIONI
.
118
7.8
OPEN
PROBLEMS
.
118
7.9
SUMMARY
.
119
7.10
BACKGROUND
.
120
8.
PLAN
MERGING
.
121
8.1
THE
VALUE
OF
PLAN
MERGING
.
121
8.2
FORMAL
DESCRIPTION
.
123
8.2.1
A
FORMAL
DEFINITION
FOR
PLAN
MERGING
.
123
8.2.2
AN
EXAMPLE
.
125
8.2.3
IMPACT
ON
CORRECTNESS
.
126
8.3
COMPLEXITY
OF
PLAN
MERGING
.
128
8.3.1
DECIDING
WHAT
TO
MERGE
.
128
8.3.2
DECIDING
HOW
TO
MERGE
.
129
8.4
OPTIMAL
PLAN
MERGING
.
130
8.5
APPROXIMATE
PLAN
MERGING
.
133
8.5.1
ALGORITHM
A
PPROX
M
ERGE
.
134
8.5.2
EXAMPLE
.
134
8.5.3
COMPLEXITY
.
135
8.6
RELATED
WORK
.
136
8.6.1
CRITICS
IN
N
OAH
,
S
IPE
,
AND
N
ONLIN
.
136
8.6.2
M
ACHINIST
.
137
8.6.3
OPERATOR
OVERLOADING
.
138
8.7
OPEN
PROBLEMS
.
138
8.7.1
ORDER-DEPENDENT
COST
FUNCTIONS
.
138
8.7.2
HIERARCHICAL
PLAN
MERGING
.
138
8.7.3
ENHANCING
CONFLICT
RESOLUTION
.
139
XIV
TABLE
OF
CONTENTS
8.8
SUMMARY
.
139
8.9
BACKGROUND
.
139
9.
MULTIPLE-GOAL
PLAN
SELECTION
.
141
9.1
CONSISTENCY-BASED
PLAN
SELECTION
.
141
9.1.1
THE
MULTIPLE-GOAL
PLAN-SELECTION
PROBLEM
.
141
9.1.2 A
CSP
REPRESENTATION
.
142
9.1.3
A
CONSTRAINT-BASED
SOLUTION
.
142
9.1.4
EVALUATION
-
A
BLOCKS-WORLD
EXAMPLE
.
144
9.2
OPTIMIZATION-BASED
PLAN
SELECTION
.
146
9.2.1
EXAMPLES
.
147
9.2.2
COMPLEXITY
.
148
9.2.3
A
HEURISTIC
ALGORITHM
FOR
PLAN
SELECTION
.
149
9.2.4
EXAMPLES
.
153
9.2.5
EMPIRICAL
RESULTS
IN
A
MANUFACTURING
PLANNING
DOMAIN
.
155
9.3
OPEN
PROBLEMS
.
156
9.4
SUMMARY
.
157
9.5
BACKGROUND
.
157
PART
III.
HIERARCHICAL
ABSTRACTION
10.
HIERARCHICAL
PLANNING
.
163
10.1
A
HIERARCHICAL
PLANNER
.
164
10.1.1
ALGORITHM
.
164
10.1.2
PRECONDITION-ELIMINATION
ABSTRACTION
.
165
10.2
SPECIFYING
REFINEMENT
.
171
10.2.1
FORWARD-CHAINING,
TOTAL-ORDER
REFINEMENT
.
171
10.2.2
BACKWARD-CHAINING,
PARTIAL-ORDER
REFINEMENT
.
172
10.3
PROPERTIES
OF
AN
ABSTRACTION
HIERARCHY
.
174
10.3.1
EXISTENCE-BASED
PROPERTIES
.
174
10.3.2
REFINEMENT-BASED
PROPERTIES
.
175
10.4
AN
ANALYTICAL
MODEL
.
179
10.4.1
ASSUMPTIONS
.
180
10.4.2
THE
PROBABILITY
OF
REFINEMENT
.
183
10.4.3
ANALYTICAL
RESULT
.
184
10.5
OPEN
PROBLEMS
.
187
10.6
SUMMARY
.
187
10.7
BACKGROUND
.
188
11.
GENERATING
ABSTRACTION
HIERARCHIES
.
189
11.1
SYNTACTIC
CONNECTIVITY
CONDITIONS
.
189
11.2
H
IPOINT
.
192
11.2.1
A
LPINE
.
194
TABLE
OF
CONTENTS
XV
11.2.2
PROBABILITY
ESTIMATES
.
196
11.2.3
COLLAPSING
NODES
WITH
LOW
REFINEMENT
PROBABILITIES
.
199
11.2.4
AUGMENTED
TOPOLOGICAL
SORT
OF
ABSTRACTION
GRAPH
.
200
11.3
EMPIRICAL
RESULTS
IN
THE
BOX
DOMAIN
.
200
11.4
RELATED
WORK
ON
ABSTRACTION
GENERATION
.
203
11.5
OPEN
PROBLEMS
.
204
11.6
SUMMARY
.
206
11.7
BACKGROUND
.
206
12.
PROPERTIES
OF
TASK
REDUCTION
HIERARCHIES
.
207
12.1
DEFINING
OPERATOR
REDUCTION
.
208
12.2
UPWARD
SOLUTION
PROPERTY
.
213
12.2.1
LOSING
THE
PROPERTY
.
213
12.2.2
A
VARIABLE-ASSIGNMENT
EXAMPLE
.
214
12.2.3
THE
CHINESE
BEAR
EXAMPLE
.
214
12.3
IMPOSING
RESTRICTIONS
.
216
12.3.1
MOTIVATION
.
216
12.3.2
UNRESOLVABLE
CONFLICTS
.
217
12.3.3
THE
UNIQUE-MAIN-SUBACTION
RESTRICTION
.
217
12.4
PREPROCESSING
.
219
12.5
MODIFYING
OPERATOR-REDUCTION
SCHEMATA
.
219
12.6
OPEN
PROBLEMS
.
221
12.7
SUMMARY
.
223
12.8
BACKGROUND
.
223
13.
EFFECT
ABSTRACTION
.
225
13.1
A
MOTIVATING
EXAMPLE
.
225
13.2
PRIMARY-EFFECT
RESTRICTED
PLANNING
.
226
13.3
INCOMPLETENESS
AND
SUB-OPTIMAL
SOLUTIONS
.
229
13.4
AN
INDUCTIVE
LEARNING
ALGORITHM
.
230
13.5
HOW
MANY
TRAINING
EXAMPLES
.
233
13.5.1
INFORMAL
DESCRIPTION
.
233
13.5.2
A
BRIEF
INTRODUCTION
TO
PAC
LEARNING
.
233
13.5.3
APPLICATION
OF
THE
PAC
MODEL
.
235
13.6
OPEN
PROBLEMS
.
236
13.7
SUMMARY
.
237
13.8
BACKGROUND
.
237
REFERENCES
.
239
INDEX
.
249 |
| any_adam_object | 1 |
| author | Yang, Qiang |
| author_facet | Yang, Qiang |
| author_role | aut |
| author_sort | Yang, Qiang |
| author_variant | q y qy |
| building | Verbundindex |
| bvnumber | BV011117709 |
| callnumber-first | Q - Science |
| callnumber-label | Q334 |
| callnumber-raw | Q334.Y36 1997 |
| callnumber-search | Q334.Y36 1997 |
| callnumber-sort | Q 3334 Y36 41997 |
| callnumber-subject | Q - General Science |
| classification_rvk | ST 134 ST 300 ST 690 |
| classification_tum | DAT 716f DAT 060f |
| ctrlnum | (OCoLC)845318046 (DE-599)BVBBV011117709 |
| dewey-full | 006.321 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 21 |
| dewey-search | 006.3 21 |
| dewey-sort | 16.3 221 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| genre_facet | Konferenzschrift |
| id | DE-604.BV011117709 |
| illustrated | Illustrated |
| indexdate | 2024-08-18T00:39:15Z |
| institution | BVB |
| isbn | 3540619011 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007450050 |
| oclc_num | 845318046 |
| open_access_boolean | |
| owner | DE-20 DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-824 DE-384 DE-706 DE-521 DE-522 DE-526 DE-634 DE-83 |
| owner_facet | DE-20 DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-824 DE-384 DE-706 DE-521 DE-522 DE-526 DE-634 DE-83 |
| physical | XXII, 252 S. graph. Darst. |
| publishDate | 1997 |
| publishDateSearch | 1997 |
| publishDateSort | 1997 |
| publisher | Springer |
| record_format | marc |
| series2 | Artificial intelligence |
| spelling | Yang, Qiang Verfasser aut Intelligent planning a decomposition and abstraction based approach Qiang Yang Berlin [u.a.] Springer 1997 XXII, 252 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Artificial intelligence Datenverarbeitung Künstliche Intelligenz Artificial intelligence -- Congresses Planning -- Data processing -- Congresses Automatische Handlungsplanung (DE-588)4211191-2 gnd rswk-swf (DE-588)1071861417 Konferenzschrift gnd-content Automatische Handlungsplanung (DE-588)4211191-2 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007450050&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Yang, Qiang Intelligent planning a decomposition and abstraction based approach Datenverarbeitung Künstliche Intelligenz Artificial intelligence -- Congresses Planning -- Data processing -- Congresses Automatische Handlungsplanung (DE-588)4211191-2 gnd |
| subject_GND | (DE-588)4211191-2 (DE-588)1071861417 |
| title | Intelligent planning a decomposition and abstraction based approach |
| title_auth | Intelligent planning a decomposition and abstraction based approach |
| title_exact_search | Intelligent planning a decomposition and abstraction based approach |
| title_full | Intelligent planning a decomposition and abstraction based approach Qiang Yang |
| title_fullStr | Intelligent planning a decomposition and abstraction based approach Qiang Yang |
| title_full_unstemmed | Intelligent planning a decomposition and abstraction based approach Qiang Yang |
| title_short | Intelligent planning |
| title_sort | intelligent planning a decomposition and abstraction based approach |
| title_sub | a decomposition and abstraction based approach |
| topic | Datenverarbeitung Künstliche Intelligenz Artificial intelligence -- Congresses Planning -- Data processing -- Congresses Automatische Handlungsplanung (DE-588)4211191-2 gnd |
| topic_facet | Datenverarbeitung Künstliche Intelligenz Artificial intelligence -- Congresses Planning -- Data processing -- Congresses Automatische Handlungsplanung Konferenzschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007450050&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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