Muutke küpsiste eelistusi

E-raamat: Understanding Planning Tasks: Domain Complexity and Heuristic Decomposition

Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 55,56 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Action planning has always played a central role in Artificial Intelligence. Given a description of the current situation, a description of possible actions and a description of the goals to be achieved, the task is to identify a sequence of actions, i.e., a plan that transforms the current situation into one that satisfies the goal description.



This monograph is a revised version of Malte Helmert's doctoral thesis, Solving Planning Tasks in Theory and Practice, written under the supervision of Professor Bernhard Nebel as thesis advisor at Albert-Ludwigs-Universität Freiburg, Germany, in 2006. The book contains an exhaustive analysis of the computational complexity of the benchmark problems that have been used in the past decade, namely the standard benchmark domains of the International Planning Competitions (IPC). At the same time, it contributes to the practice of solving planning tasks by presenting a powerful new approach to heuristic planning. The author also provides an in-depth analysis of so-called routing and transportation problems.



All in all, this book will contribute significantly to advancing the state of the art in automatic planning.
Part I Planning Benchmarks
The Role of Benchmarks
3(10)
Evaluating Planner Performance
3(4)
Worst-Case Evaluation
4(1)
Average-Case Evaluation
5(2)
Planning Benchmarks Are Important
7(1)
Theoretical Analyses of Planning Benchmarks
8(1)
Why Theoretical Analyses Are Useful
8(1)
Published Results on Benchmark Complexity
9(1)
Standard Benchmarks
9(2)
Summary and Overview
11(2)
Defining Planning Domains
13(18)
Optimization Problems
13(8)
Minimization Problems
14(1)
Approximation Algorithms
15(1)
Approximation Classes
16(2)
Reductions
18(3)
Formalizing Planning Domains
21(3)
General Results and Reductions
24(7)
Upper Bounds
24(1)
Shortest Plan Length
25(1)
Approximation Classes of Limited Interest
26(2)
Relating Planning and (Bounded) Plan Existence
28(1)
Generalization and Specialization
29(2)
The Benchmark Suite
31(8)
Defining the Competition Domains
31(1)
The Benchmark Suite
32(4)
IPC1 Domains
32(2)
IPC2 Domains
34(1)
IPC3 Domains
34(1)
IPC4 Domains
35(1)
Domains and Domain Families
36(3)
Transportation and Route Planning
39(36)
Transport and Route
39(7)
The Transport Domain
41(2)
The Route Domain
43(1)
Special Cases and Hierarchy
44(2)
General Results
46(6)
Plan Existence
52(2)
Hardness of Optimization
54(5)
Constant Factor Approximation
59(3)
Hardness of Constant Factor Approximation
62(6)
Summary
68(3)
Beyond Transport and Route
71(4)
IPC Domains: Transportation and Route Planning
75(38)
Gripper
75(1)
Mystery and MysteryPrime
76(2)
Logistics
78(5)
Zenotravel
83(2)
Depots
85(3)
Miconic-10
88(5)
Rovers
93(5)
Grid
98(5)
Driverlog
103(5)
Airport
108(3)
Summary
111(2)
LPC Domains: Others
113(38)
Assembly
113(4)
Blocksworld
117(1)
FreeCell
117(9)
Movie
126(1)
Pipesworld
127(5)
Promela
132(6)
PSR
138(4)
Satellite
142(3)
Schedule
145(4)
Summary
149(2)
Conclusions
151(6)
Ten Conclusions
151(3)
Going Further
154(3)
Part II Fast Downward
Solving Planning Tasks Hierarchically
157(14)
Introduction
157(6)
Related Work
163(5)
Causal Graphs and Abstraction
164(1)
Causal Graphs and Unary Strips Operators
165(2)
Multi-Valued Planning Tasks
167(1)
Architecture and Overview
168(3)
Translation
171(36)
PDDL and Multi-valued Planning Tasks
171(4)
Translation Overview
175(1)
Normalization
176(4)
Compiling Away Types
177(1)
Simplifying Conditions
177(2)
Simplifying Effects
179(1)
Normalization Result
179(1)
Invariant Synthesis
180(10)
Initial Candidates
182(1)
Proving Invariance
183(3)
Refining Failed Candidates
186(2)
Examples
188(1)
Related Work
188(2)
Grounding
190(7)
Overview of Horn Exploration
191(1)
Generating the Logic Program
191(2)
Translating the Logic Program to Normal Form
193(2)
Computing the Canonical Model
195(2)
Axiom and Operator Instantiation
197(1)
Multi-valued Planning Task Generation
197(6)
Variable Selection
198(1)
Converting the Initial State
199(1)
Converting Operator Effects
200(1)
Converting Conditions
201(1)
Computing Axiom Layers
202(1)
Generating the Output
202(1)
Performance Notes
203(4)
Relative Performance Compared to MIPS Translator
203(2)
Absolute Performance
205(2)
Knowledge Compilation
207(16)
Overview
207(1)
Domain Transition Graphs
208(5)
Causal Graphs
213(7)
Acyclic Causal Graphs
214(1)
Generating and Pruning Causal Graphs
215(2)
Causal Graph Examples
217(3)
Successor Generators and Axiom Evaluators
220(3)
Successor Generators
220(1)
Axiom Evaluators
221(2)
Search
223(16)
Overview
223(1)
The Causal Graph Heuristic
224(6)
Conceptual View of the Causal Graph Heuristic
225(1)
Computation of the Causal Graph Heuristic
226(2)
States with Infinite Heuristic Value
228(1)
Helpful Transitions
229(1)
The FF Heuristic
230(1)
Greedy Best-First Search in Fast Downward
231(2)
Preferred Operators
231(1)
Deferred Heuristic Evaluation
232(1)
Multi-heuristic Best-First Search
233(1)
Focused Iterative-Broadening Search
234(5)
Experiments
239(14)
Experiment Design
239(4)
Benchmark Set
240(2)
Experiment Setup
242(1)
Translation and Knowledge Compilation vs. Search
243(1)
Strips Domains from IPC1-3
243(3)
ADL Domains from IPC1-3
246(2)
Domains from IPC4
248(3)
Conclusions from the Experiment
251(2)
Discussion
253(6)
Summary
253(1)
Major Contributions
254(3)
Multi-valued Representations
254(2)
Task Decomposition Heuristics
256(1)
Minor Contributions
257(1)
Going Further
258(1)
References 259(8)
Index 267