TY  - BOOK
AU  - 
AU  - 
AU  - 
ED  - SpringerLink (Online service)
TI  - Handbook of Weighted Automata
T2  - Monographs in Theoretical Computer Science. An EATCS Series,
SN  - 9783642014925
AV  - QA8.9-QA10.3 
U1  - 005.131 23
PY  - 2009///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Artificial intelligence
KW  - Logic, Symbolic and mathematical
KW  - Computer Science
KW  - Mathematical Logic and Formal Languages
KW  - Computation by Abstract Devices
KW  - Mathematical Logic and Foundations
KW  - Mathematics of Computing
KW  - Artificial Intelligence (incl. Robotics)
N1  - Foundations -- Semirings and Formal Power Series -- Fixed Point Theory -- Concepts of Weighted Recognizability -- Finite Automata -- Rational and Recognisable Power Series -- Weighted Automata and Weighted Logics -- Weighted Automata Algorithms -- Weighted Discrete Structures -- Algebraic Systems and Pushdown Automata -- Lindenmayer Systems -- Weighted Tree Automata and Tree Transducers -- Traces, Series-Parallel Posets, and Pictures: AWeighted Study -- Applications -- Digital Image Compression -- Fuzzy Languages -- Model Checking Linear-Time Properties ofProbabilistic Systems -- Applications of Weighted Automata in Natural Language Processing; ZDB-2-SCS
N2  - The purpose of this Handbook is to highlight both theory and applications of weighted automata. Weighted ?nite automata are classical nondeterministic ?nite automata in which the transitions carry weights. These weights may model, e. g. , the cost involved when executing a transition, the amount of resources or time neededforthis,ortheprobabilityorreliabilityofitssuccessful execution. The behavior of weighted ?nite automata can then be considered as the function (suitably de?ned) associating with each word the weight of its execution. Clearly, weights can also be added to classical automata with in?nite state sets like pushdown automata; this extension constitutes the general concept of weighted automata. To illustrate the diversity of weighted automata, let us consider the f- lowing scenarios. Assume that a quantitative system is modeled by a classical automaton in which the transitions carry as weights the amount of resources needed for their execution. Then the amount of resources needed for a path in this weighted automaton is obtained simply as the sum of the weights of its transitions. Given a word, we might be interested in the minimal amount of resources needed for its execution, i. e. , for the successful paths realizing the given word. In this example, we could also replace the ǣresourcesǥ by ǣpro?tǥ and then be interested in the maximal pro?t realized, correspondingly, by a given word
UR  - http://dx.doi.org/10.1007/978-3-642-01492-5
ER  -