Ferdinando Fioretto

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.

A GPU implementation of large neighborhood search for solving constraint optimization problems

Constraint programming has gained prominence as an effective and declarative paradigm for modeling and solving complex combinatorial problems. Techniques based on local search have proved practical to solve real-world problems, providing a good compromise between optimality and efficiency. In spite of the natural presence of concurrency, there has been relatively limited effort to use novel massively parallel architectures, such as those found in modern Graphical Processing Units (GPUs), to speedup local search techniques in constraint programming. This paper describes a novel framework which exploits parallelism from a popular local search method (the Large Neighborhood Search method), using GPUs.

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.

A Dynamic Programming-Based MCMC Framework for Solving DCOPs with GPUs

The field of Distributed Constraint Optimization (DCOP) has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent coordination. Nevertheless, solving DCOPs is computationally challenging. Thus, in large scale, complex applications, incomplete DCOP algorithms are necessary. Recently, researchers have introduced a promising class of incomplete DCOP algorithms, based on sampling. However, this paradigm requires a multitude of samples to ensure convergence. This paper exploits the property that sampling is amenable to parallelization, and introduces a general framework, called Distributed MCMC (DMCMC), that is based on a dynamic programming procedure and uses Markov Chain Monte Carlo (MCMC) sampling algorithms to solve DCOPs. Additionally, DMCMC harnesses the parallel computing power of Graphical Processing Units (GPUs) to speed-up the sampling process. The experimental results show that DMCMC can find good solutions up to two order of magnitude faster than other incomplete DCOP algorithms.

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.

A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs

The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques for solving Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field’s inception, the number of DCOP realistic applications available to assess the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describes the problem of coordinating smart devices schedules across multiple homes as a multi-agent system, (ii) detail the physical models adopted to simulate smart sensors, smart actuators, and homes’ environments, and (iii) introduce a realistic benchmark for SHDS problems.

Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs

Discrete optimization is a central problem in artificial intelligence. The optimization of the aggregated cost of a network of cost functions arises in a variety of problems including Weighted Constraint Programs (WCSPs), Distributed Constraint Optimization (DCOP), as well as optimization in stochastic variants such as the tasks of finding the most probable explanation (MPE) in belief networks. Inference-based algorithms are powerful techniques for solving discrete optimization problems, which can be used independently or in combination with other techniques. However, their applicability is often limited by their compute intensive nature and their space requirements. This paper proposes the design and implementation of a novel inference-based technique, which exploits modern massively parallel architectures, such as those found in Graphical Processing Units (GPUs), to speed up the resolution of exact and approximated inference-based algorithms for discrete optimization. The paper studies the proposed algorithm in both centralized and distributed optimization contexts. The paper demonstrates that the use of GPUs provides significant advantages in terms of runtime and scalability, achieving up to two orders of magnitude in speedups and showing a considerable reduction in execution time (up to 345 times faster) with respect to a sequential version.

A filtering technique for fragment assembly- based proteins loop modeling with constraints

Methods to predict the structure of a protein often rely on the knowledge of macro sub-structures and their exact or approximated relative positions in space. The parts connecting these sub-structures are called loops and, in general, they are characterized by a high degree of freedom. The modeling of loops is a critical problem in predicting protein conformations that are biologically realistic. This paper introduces a class of constraints that models a general multi-body system; we present a proof of NP-completeness and provide filtering techniques, inspired by inverse kinematics, that can drastically reduce the search space of potential conformations. The paper shows the application of the constraint in solving the protein loop modeling problem, based on fragments assembly.

A Large Neighboring Search Schema for Multi-Agent Optimization

The Distributed Constraint Optimization Problem (DCOP) is an elegant paradigm for modeling and solving multi-agent problems which are distributed in nature, and where agents cooperate to optimize a global objective within the confines of localized communication. Since solving DCOPs optimally is NP-hard, recent effort in the development of DCOP algorithms has focused on incomplete methods. Unfortunately, many of such proposals do not provide quality guarantees or provide a loose quality assessment. Thus, this paper proposes the Distributed Large Neighborhood Search (DLNS), a novel iterative local search framework to solve DCOPs, which provides guarantees on solution quality refining lower and upper bounds in an iterative process. Our experimental analysis of DCOP benchmarks on several important classes of graphs illustrates the effectiveness of DLNS in finding good solutions and tight upper bounds in both problems with and without hard constraints.

Investigation of Learning Strategies for the SPOT Broker in Power TAC

The Power TAC simulation emphasizes the strategic problems that broker agents face in managing the economics of a smart grid. The brokers must make trades in multiple markets and, to be successful, brokers must make many good predictions about future supply, demand, and prices in the wholesale and tariff markets. In this paper, we investigate the feasibility of using learning strategies to improve the performance of our broker, SPOT. Specifically, we investigate the use of decision trees and neural networks to predict the clearing price in the wholesale market and the use of reinforcement learning to learn good strategies for pricing our tariffs in the tariff market. Our preliminary results show that our learning strategies are promising ways to improve the performance of the agent for future competitions.