56 research outputs found
Soft constraint abstraction based on semiring homomorphism
The semiring-based constraint satisfaction problems (semiring CSPs), proposed
by Bistarelli, Montanari and Rossi \cite{BMR97}, is a very general framework of
soft constraints. In this paper we propose an abstraction scheme for soft
constraints that uses semiring homomorphism. To find optimal solutions of the
concrete problem, the idea is, first working in the abstract problem and
finding its optimal solutions, then using them to solve the concrete problem.
In particular, we show that a mapping preserves optimal solutions if and only
if it is an order-reflecting semiring homomorphism. Moreover, for a semiring
homomorphism and a problem over , if is optimal in
, then there is an optimal solution of such that
has the same value as in .Comment: 18 pages, 1 figur
A Compositional Framework for Preference-Aware Agents
A formal description of a Cyber-Physical system should include a rigorous
specification of the computational and physical components involved, as well as
their interaction. Such a description, thus, lends itself to a compositional
model where every module in the model specifies the behavior of a
(computational or physical) component or the interaction between different
components. We propose a framework based on Soft Constraint Automata that
facilitates the component-wise description of such systems and includes the
tools necessary to compose subsystems in a meaningful way, to yield a
description of the entire system. Most importantly, Soft Constraint Automata
allow the description and composition of components' preferences as well as
environmental constraints in a uniform fashion. We illustrate the utility of
our framework using a detailed description of a patrolling robot, while
highlighting methods of composition as well as possible techniques to employ
them.Comment: In Proceedings V2CPS-16, arXiv:1612.0402
Tree Projections and Constraint Optimization Problems: Fixed-Parameter Tractability and Parallel Algorithms
Tree projections provide a unifying framework to deal with most structural
decomposition methods of constraint satisfaction problems (CSPs). Within this
framework, a CSP instance is decomposed into a number of sub-problems, called
views, whose solutions are either already available or can be computed
efficiently. The goal is to arrange portions of these views in a tree-like
structure, called tree projection, which determines an efficiently solvable CSP
instance equivalent to the original one. Deciding whether a tree projection
exists is NP-hard. Solution methods have therefore been proposed in the
literature that do not require a tree projection to be given, and that either
correctly decide whether the given CSP instance is satisfiable, or return that
a tree projection actually does not exist. These approaches had not been
generalized so far on CSP extensions for optimization problems, where the goal
is to compute a solution of maximum value/minimum cost. The paper fills the
gap, by exhibiting a fixed-parameter polynomial-time algorithm that either
disproves the existence of tree projections or computes an optimal solution,
with the parameter being the size of the expression of the objective function
to be optimized over all possible solutions (and not the size of the whole
constraint formula, used in related works). Tractability results are also
established for the problem of returning the best K solutions. Finally,
parallel algorithms for such optimization problems are proposed and analyzed.
Given that the classes of acyclic hypergraphs, hypergraphs of bounded
treewidth, and hypergraphs of bounded generalized hypertree width are all
covered as special cases of the tree projection framework, the results in this
paper directly apply to these classes. These classes are extensively considered
in the CSP setting, as well as in conjunctive database query evaluation and
optimization
A graph-based design framework for services
Service-oriented systems rely on software applications that offer services through the orchestration of activities performed by external services procured on the fly when they are needed. This paper presents an overview of a graph-based framework developed around the notions of service and activity module for supporting the design of service-oriented systems in a way that is independent of execution languages and deployment platforms. The framework supports both behaviour and quality-of-service constraints for the discovery, ranking and selection of external services. Service instantiation and binding are captured as algebraic operations on configuration graphs. © 2012 Springer-Verlag
CALF: Categorical Automata Learning Framework
Automata learning is a popular technique used to automatically construct an automaton model from queries, and much research has gone into devising specific adaptations of such algorithms for different types of automata. This thesis presents a unifying approach to many existing algorithms using category theory, which eases correctness proofs and guides the design of new automata learning algorithms. We provide a categorical automata learning framework---CALF---that at its core includes an abstract version of the popular L* algorithm. Using this abstract algorithm we derive several concrete ones. We instantiate the framework to a large class of Set functors, by which we recover for the first time a tree automata learning algorithm from an abstract framework, which moreover is the first to cover also algebras of quotiented polynomial functors. We further develop a general algorithm to learn weighted automata over a semiring. On the one hand, we identify a class of semirings, principal ideal domains, for which this algorithm terminates and for which no learning algorithm previously existed; on the other hand, we show that it does not terminate over the natural numbers. Finally, we develop an algorithm to learn automata with side-effects determined by a monad and provide several optimisations, as well as an implementation with experimental evaluation. This allows us to improve existing algorithms and opens the door to learning a wide range of automata
The SENSORIA reference modelling language
This chapter provides an overview of SRML - the Sensoria Reference Modelling Language. Our focus will be on the language primitives that SRML offers for modelling business services and activities, the methodological approach that SRML supports, and the mathematical semantics the underpins the modelling approach, including techniques for qualitative and quantitative analysis. © 2011 Springer-Verlag Berlin Heidelberg
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