Papers by Patricia Katherine Jaramillo Gutierrez
Journal of Artificial Intelligence Research, 2012
This note considers how to modify BnB-ADOPT, a well-known algorithm for optimally solving distrib... more This note considers how to modify BnB-ADOPT, a well-known algorithm for optimally solving distributed constraint optimization problems, with a double aim: (i) to avoid sending most of the redundant messages and (ii) to handle cost functions of any arity. Some of the messages exchanged by BnB-ADOPT turned out to be redundant. Removing most of the redundant messages increases substantially communication efficiency: the number of exchanged messages is - in most cases - at least three times fewer (keeping the other measures almost unchanged), and termination and optimality are maintained. On the other hand, handling n-ary cost functions was addressed in the original work, but the presence of thresholds makes their practical usage more complex. Both issues - removing most of the redundant messages and efficiently handling n-ary cost functions - can be combined, producing the new version BnB-ADOPT+. Experimentally, we show the benefits of this version over the original one.
ADOPT and BnB-ADOPT are two optimal DCOP search algorithms that are similar except for their sear... more ADOPT and BnB-ADOPT are two optimal DCOP search algorithms that are similar except for their search strategies: the former uses best-first search and the latter uses depth-first branch-and-bound search. In this paper, we present a new algorithm, called ADOPT(k), that generalizes them. Its behavior depends on the k parameter. It behaves like ADOPT when k = 1, like BnB-ADOPT when k = ∞ and like a hybrid of ADOPT and BnB-ADOPT when 1 < k < ∞. We prove that ADOPT(k) is a correct and complete algorithm and experimentally show that ADOPT(k) outperforms ADOPT and BnB-ADOPT on several benchmarks across several metrics.
BnB-ADOPT+ with Several Soft Arc Consistency Levels
Distributed constraint optimization problems can be solved by BnB-ADOPT+, a distributed asynchron... more Distributed constraint optimization problems can be solved by BnB-ADOPT+, a distributed asynchronous search algorithm. In the centralized case, local consistency techniques applied to constraint optimization have been shown very beneficial to increase performance. In this paper, we combine BnB-ADOPT+ with different levels of soft arc consistency, propagating unconditional deletions caused by either the enforced local consistency or by distributed search. The new algorithm maintains BnB-ADOPT+ optimality and termination. In practice, this approach decreases substantially BnB-ADOPT+ requirements in communication cost and computation effort when solving commonly used benchmarks.
Graphical model processing is a central problem in artificial intelligence. The optimization of t... more Graphical model processing is a central problem in artificial intelligence. The optimization of the combined cost of a network of local cost functions federates a variety of famous problems including CSP, SAT and Max-SAT but also optimization in stochastic variants such as Markov Random Fields and Bayesian networks. Exact solving methods for these problems typically include branch and bound and local inference-based bounds. In this paper we are interested in understanding when and how dynamic programming based optimization can be used to efficiently enforce soft local consistencies on Global Cost Functions, defined as parameterized families of cost functions of unbounded arity. Enforcing local consistencies in cost function networks is performed by applying so-called Equivalence Preserving Transformations (EPTs) to the cost functions. These EPTs may transform global cost functions and make them intractable to optimize. We identify as tractable projection-safe those global cost functions whose optimization is and remains tractable after applying the EPTs used for enforcing arc consistency. We also provide new classes of cost functions that are tractable projection-safe thanks to dynamic programming. We show that dynamic programming can either be directly used inside filtering algorithms, defining polynomially DAG-filterable cost functions, or emulated by arc consistency filtering on a Berge-acyclic network of bounded-arity cost functions, defining Berge-acyclic network-decomposable cost functions. We give examples of such cost functions and we provide a systematic way to define decompositions from existing decomposable global constraints. These two approaches to enforcing consistency in global cost functions are then embedded in a solver for extensive experiments that confirm the feasibility and efficiency of our proposal.
Lecture Notes in Computer Science, 2012
In the centralized context, global constraints have been essential for the advancement of constra... more In the centralized context, global constraints have been essential for the advancement of constraint reasoning. In this paper we propose to include soft global constraints in distributed constraint optimization problems (DCOPs). Looking for efficiency, we study possible decompositions of global constraints, including the use of extra variables. We extend the distributed search algorithm BnB-ADOPT + to support these representations of global constraints. In addition, we explore the relation of global constraints with soft local consistency in DCOPs, in particular for the generalized soft arc consistency (GAC) level. We include specific propagators for some well-known soft global constraints. Finally, we provide empirical results on several benchmarks.
Connecting BnB-ADOPT with Soft Arc Consistency: Initial Results
Lecture Notes in Computer Science, 2011
Distributed constraint optimization problems with finite domains can be solved by asynchronous pr... more Distributed constraint optimization problems with finite domains can be solved by asynchronous procedures. ADOPT is the reference algorithm for this kind of problems. Several versions of this algorithm have been proposed, one of them is BnB-ADOPT which changes the nature of the original algorithm from best-first to depth-first search. With BnB-ADOPT, we can assure in some cases that the value
The Computer Journal, 2013
Global constraints are an essential component in the efficiency of centralized constraint program... more Global constraints are an essential component in the efficiency of centralized constraint programming. We propose to include global constraints in distributed constraint satisfaction and optimization problems (DisCSPs and DCOPs). We detail how this inclusion can be done, considering different representations for global constraints (direct, nested, binary). We explore the relation of global constraints with local consistency (both in the hard and soft cases), in particular for generalized arc consistency (GAC). We provide experimental evidence of the benefits of global constraints on several benchmarks, both for distributed constraint satisfaction and for distributed constraint optimization.
sus conocimientos aportados en este proyecto. Al Dr. Hermel Salinas por toda la colaboración brin... more sus conocimientos aportados en este proyecto. Al Dr. Hermel Salinas por toda la colaboración brindada, por sus valiosas aportaciones que hicieron posible este proyecto y por la gran calidad humana que me ha demostrado con su amistad. A la Bq.F Marisol Vacacela por su valioso asesoramiento y guiarme durante el desarrollo del proyecto. Deseo expresar mi agradecimiento a todas aquellas personas que de una manera u otra hicieron posible la realización de este trabajo de Tesis.
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Papers by Patricia Katherine Jaramillo Gutierrez