Tiep Le

Improving DPOP with Branch Consistency for Solving Distributed Constraint Optimization Problems

The DCOP model has gained momentum in recent years thanks to its ability to capture problems that are naturally distributed and cannot be realistically addressed in a centralized manner. Dynamic programming based techniques have been recognized to be among the most effective techniques for building complete DCOP solvers (e.g., DPOP). Unfortunately, they also suffer from a widely recognized drawback: their messages are exponential in size. Another limitation is that most current DCOP algorithms do not actively exploit hard constraints, which are common in many real problems. This paper addresses these two limitations by introducing an algorithm, called BrC-DPOP, that exploits arc consistency and a form of consistency that applies to paths in pseudo-trees to reduce the size of the messages. Experimental results shows that BrC-DPOP uses messages that are up to one order of magnitude smaller than DPOP, and that it can scale up well, being able to solve problems that its counterpart can not.

Exploiting GPUs in solving (Distributed) constraint optimization problems with dynamic programming

This paper proposes the design and implementation of a dynamic programming based algorithm for (distributed) constraint optimization, which exploits modern massively parallel architectures, such as those found in modern Graphical Processing Units (GPUs). The paper studies the proposed algorithm in both centralized and distributed optimization contexts. The experimental analysis, performed on unstructured and structured graphs, shows the advantages of employing GPUs, resulting in enhanced performances and scalability. This research is partially supported by the National Science Foundation under grant number HRD-1345232. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government.

ASP at Work: An ASP Implementation of PhyloWS.

This paper continues the exploration started in [Bininda-Emonds,2004], aimed at demonstrating the use of logic programming technology to support a large scale deployment and analysis of phylogenetic data from biological studies. This application paper illustrates the use of ASP technology in implementing the PhyloWS web service API — a recently proposed and community-agreed standard API to enable uniform access and inter-operation among phylogenetic applications and repositories. To date, only very incomplete implementations of PhyloWS have been realized; this paper demonstrates how ASP provides an ideal technology to support a more comprehensive realization of PhyloWS on a repository of semantically-described phylogenetic studies. The paper also presents a challenge for the developers of ASP-solvers.

Preference Elicitation for DCOPs

Distributed Constraint Optimization Problems (DCOPs) offer a powerful approach for the description and resolution of cooperative multi-agent problems. In this model, a group of agents coordinate their actions to optimize a global objective function, taking into account their preferences or constraints. A core limitation of this model is the assumption that the preferences of all agents or the costs of all constraints are specified a priori. Unfortunately, this assumption does not hold in a number of application domains where preferences or constraints must be elicited from the users. One of such domains is the Smart Home Device Scheduling (SHDS) problem. Motivated by this limitation, we make the following contributions in this paper: (1) We propose a general model for preference elicitation in DCOPs; (2) We propose several heuristics to elicit preferences in DCOPs; and (3) We empirically evaluate the effect of these heuristics on random binary DCOPs as well as SHDS problems.

Multi-Context Systems with Preferences

This paper presents an extension of the Multi-Context Systems (MCS) framework to allow preferences to be expressed at the context level. The work is motivated by the observation that a casual use of preference logics at a context level in MCS can lead to undesirable outcomes (e.g., inconsistency of the MCS). To address this issue, the paper introduces the notion of a ranked logic, suitable for use with multiple sources of preferences, and employs it in the definition of weakly and strongly-preferred equilibria in a Multi-Context Systems with Preferences (MCSP) framework. The usefulness of MCSP is demonstrated in two applications: modeling of distributed configuration problems and finding explanations for distributed abductive diagnosis problems.

On Computing World Views of Epistemic Logic Programs

This paper presents a novel algorithm for computing world views of different semantics of epistemic logic programs (ELP) and two of its realization, called EP-ASP (for an older semantics) and EPASP (for the newest semantics), whose implementation builds on the theoretical advancement in the study of ELPs and takes advantage of the multishot computation paradigm of the answer set solver CLINGO. The new algorithm differs from the majority of earlier algorithms in its strategy. Specifically, it computes one world view at a time and utilizes properties of world views to reduce its search space. It starts by computing an answer set and then determines whether or not a world view containing this answer set exists. In addition, it allows for the computation to focus on world views satisfying certain properties. The paper includes an experimental analysis of the performance of the two solvers comparing against a recently developed solver. It also contains an analysis of their performance in goal directed computing against a logic programming based conformant planning system, DLV-K. It concludes with some final remarks and discussion on the future work.

Solving distributed constraint optimization problems using logic programming

This paper explores the use of answer set programming (ASP) in solving distributed constraint optimization problems (DCOPs). It makes the following contributions: (i) It shows how one can formulate DCOPs as logic programs; (ii) It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (iii) It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative-programming counterpart) as well as solve some problems that DPOP fails to solve due to memory limitations; and (iv) It demonstrates the applicability of ASP in the wide array of multi-agent problems currently modeled as DCOPs.

Multi-context Systems with Preferences

This paper presents an extension of the Multi-Context Systems (MCS) framework to allow preferences to be expressed at the context level. The work is motivated by the observation that a casual use of preference logics at a context level in MCS can lead to undesirable outcomes (e.g., inconsistency of the MCS). To address this issue, the paper introduces the notion of a ranked logic, suitable for use with multiple sources of preferences, and employs it in the definition of weakly and strongly-preferred equilibria in a Multi-Context Systems with Preferences (MCSP) framework. The usefulness of MCSP is demonstrated in two applications: modeling of distributed configuration problems and finding explanations for distributed abductive diagnosis problems.

Doctoral Consortium Extended Abstract: Multi-context Systems with Preferences

Multi-Context Systems (MCSs) have been introduced in [1] as a framework for integration of knowledge from different sources. This research formalizes MCSs with preferences (MCSPs) that allows to integrate preferences into an MCS at the context level and at the MCS level, and proposes novel distributed algorithms to compute their semantics.