---
res:
bibo_abstract:
- 'The traditional synthesis question given a specification asks for the automatic
construction of a system that satisfies the specification, whereas often there
exists a preference order among the different systems that satisfy the given specification.
Under a probabilistic assumption about the possible inputs, such a preference
order is naturally expressed by a weighted automaton, which assigns to each word
a value, such that a system is preferred if it generates a higher expected value.
We solve the following optimal synthesis problem: given an omega-regular specification,
a Markov chain that describes the distribution of inputs, and a weighted automaton
that measures how well a system satisfies the given specification under the input
assumption, synthesize a system that optimizes the measured value. For safety
specifications and quantitative measures that are defined by mean-payoff automata,
the optimal synthesis problem reduces to finding a strategy in a Markov decision
process (MDP) that is optimal for a long-run average reward objective, which can
be achieved in polynomial time. For general omega-regular specifications along
with mean-payoff automata, the solution rests on a new, polynomial-time algorithm
for computing optimal strategies in MDPs with mean-payoff parity objectives. Our
algorithm constructs optimal strategies that consist of two memoryless strategies
and a counter. The counter is in general not bounded. To obtain a finite-state
system, we show how to construct an ε-optimal strategy with a bounded counter,
for all ε > 0. Furthermore, we show how to decide in polynomial time if it
is possible to construct an optimal finite-state system (i.e., a system without
a counter) for a given specification. We have implemented our approach and the
underlying algorithms in a tool that takes qualitative and quantitative specifications
and automatically constructs a system that satisfies the qualitative specification
and optimizes the quantitative specification, if such a system exists. We present
some experimental results showing optimal systems that were automatically generated
in this way.@eng'
bibo_authorlist:
- foaf_Person:
foaf_givenName: Krishnendu
foaf_name: Chatterjee, Krishnendu
foaf_surname: Chatterjee
foaf_workInfoHomepage: http://www.librecat.org/personId=2E5DCA20-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0002-4561-241X
- foaf_Person:
foaf_givenName: Thomas A
foaf_name: Henzinger, Thomas A
foaf_surname: Henzinger
foaf_workInfoHomepage: http://www.librecat.org/personId=40876CD8-F248-11E8-B48F-1D18A9856A87
orcid: 0000−0002−2985−7724
- foaf_Person:
foaf_givenName: Barbara
foaf_name: Jobstmann, Barbara
foaf_surname: Jobstmann
- foaf_Person:
foaf_givenName: Rohit
foaf_name: Singh, Rohit
foaf_surname: Singh
bibo_doi: 10.1145/2699430
bibo_issue: '1'
bibo_volume: 62
dct_date: 2015^xs_gYear
dct_language: eng
dct_publisher: ACM@
dct_title: Measuring and synthesizing systems in probabilistic environments@
...