Distributed Constraint Optimization Research Articles

HS-CAI: A Hybrid DCOP Algorithm via Combining Search with Context-Based Inference

Search and inference are two main strategies for optimally solving Distributed Constraint Optimization Problems (DCOPs). Recently, several algorithms were proposed to combine their advantages. Unfortunately, such algorithms only use an approximated inference as a one-shot preprocessing phase to construct the initial lower bounds which lead to inefficient pruning under the limited memory budget. On the other hand, iterative inference algorithms (e.g., MB-DPOP) perform a context-based complete inference for all possible contexts but suffer from tremendous traffic overheads. In this paper, (i) hybridizing search with context-based inference, we propose a complete algorithm for DCOPs, named HS-CAI where the inference utilizes the contexts derived from the search process to establish tight lower bounds while the search uses such bounds for efficient pruning and thereby reduces contexts for the inference. Furthermore, (ii) we introduce a context evaluation mechanism to select the context patterns for the inference to further reduce the overheads incurred by iterative inferences. Finally, (iii) we prove the correctness of our algorithm and the experimental results demonstrate its superiority over the state-of-the-art.

Maintaining ethical resolution in distributed constraint reasoning

Multi-agent systems (MAS) consist of autonomous agents that parcel out different tasks and make decisions in dynamic environments. Distributed constraint satisfaction problem (DisCSP) is the most effective and applicative MAS framework. In DisCSP, each agent is connected to the other agents via constraints and holds its own local constrained problem. Those agents find solutions satisfying their own constraints and the linking ones too by collaborating and exchanging messages holding their instantiations. This formalism does not take into consideration the possibility of the presence of unethical agents which can make irrelevant or even dangerous decisions, especially when human agents are involved in the resolution. In this paper, we propose an extension of the DisCSP into an ethical formalism “E-DisCSP”. It allows to control agents, detect intrusions and apply the convenient actions when an unethical agent is picked up. All these functionalities are done via control framework, in order to maintain the DisCSP resolution as normal as possible. Experimental results show the efficiency of our contribution. The detection rate of unethical agents achieves up to 100%. And the convenient actions’ application allows, to go from 45 to 0% of wrong solutions.

Applying Max-sum to asymmetric distributed constraint optimization problems

We study the adjustment and use of the Max-sum algorithm for solving Asymmetric Distributed Constraint Optimization Problems (ADCOPs). First, we formalize asymmetric factor-graphs and apply the different versions of Max-sum to them. Apparently, in contrast to local search algorithms, most Max-sum versions perform similarly when solving symmetric and asymmetric problems and some even perform better on asymmetric problems. Second, we prove that the convergence properties of Max-sum_ADVP (an algorithm that was previously found to outperform standard Max-sum and Bounded Max-sum) and the quality of the solutions it produces, are dependent on the order between nodes involved in each constraint, i.e., the inner constraint order (ICO). A standard ICO allows to reproduce the properties achieved for symmetric problems. Third, we demonstrate that a non-standard ICO can be used to balance exploration and exploitation. Our results indicate that Max-sum_ADVP with non-standard ICO and Damped Max-sum, when solving asymmetric problems, both outperform other versions of Max-sum, as well as local search algorithms specifically designed for solving ADCOPs.

Incentive-based search for equilibria in boolean games

Search for equilibria in games is a hard problem and many games do not have a pure Nash equilibrium (PNE). Incentive mechanisms have been shown to secure a PNE in certain families of games. The present study utilizes the similarity between Asymmetric Distributed Constraints Optimization Problems (ADCOPs) and games, to construct search algorithms for finding outcomes and incentives that secure a pure Nash equilibrium in Boolean games. The set of values returned by the search algorithm for a chosen incentive mechanism is termed an incentive plan. The two incentive mechanisms that are used by the present study are taxation and side-payments. Both are described and their performance on PNE search in Boolean games is evaluated. An incentive plan for the taxation mechanism consists of the values of imposed tax, while an incentive plan for the side payments mechanism consists of values for a set of transfer functions. The distributed search algorithms address two different requirements. One is to find an incentive plan that enables to secure a PNE. The other requirement is that the algorithms return a PNE that satisfies some global objective, such as a PNE that maximizes social welfare. The Boolean game is first transformed into an ADCOP. Then, a designated distributed search algorithm is applied to find the desired outcome. Two distributed search algorithms are described, incorporating k-ary constraints as well as soft constraints that relate to global objectives. The new and innovative algorithm - Concurrent Asymmetric Branch and Bound - is found experimentally to be much faster than the former algorithm. An extensive experimental evaluation on several types of social-network-based Boolean games is presented. The degree of intervention in the game is found to be small for both incentive mechanisms. In other words, the overall tax or the total amount of side-payments is a small fraction of the general utility. The density of the networks has a substantial effect on the solution quality as well as on the computational effort to find them.

Multi-Context System for Optimization Problems

This paper proposes Multi-context System for Optimization Problems (MCS-OP) by introducing conditional costassignment bridge rules to Multi-context Systems (MCS). This novel feature facilitates the definition of a preorder among equilibria, based on the total incurred cost of applied bridge rules. As an application of MCS-OP, the paper describes how MCS-OP can be used in modeling Distributed Constraint Optimization Problems (DCOP), a prominent class of distributed optimization problems that is frequently employed in multi-agent system (MAS) research. The paper shows, by means of an example, that MCS-OP is more expressive than DCOP, and hence, could potentially be useful in modeling distributed optimization problems which cannot be easily dealt with using DCOPs. It also contains a complexity analysis of MCS-OP.

A Generic Approach to Accelerating Belief Propagation Based Incomplete Algorithms for DCOPs via a Branch-and-Bound Technique

Belief propagation approaches, such as Max-Sum and its variants, are important methods to solve large-scale Distributed Constraint Optimization Problems (DCOPs). However, for problems with n-ary constraints, these algorithms face a huge challenge since their computational complexity scales exponentially with the number of variables a function holds. In this paper, we present a generic and easy-touse method based on a branch-and-bound technique to solve the issue, called Function Decomposing and State Pruning (FDSP). We theoretically prove that FDSP can provide monotonically non-increasing upper bounds and speed up belief propagation based incomplete DCOP algorithms without an effect on solution quality. Also, our empirically evaluation indicates that FDSP can reduce 97% of the search space at least and effectively accelerate Max-Sum, compared with the state-of-the-art.

Using Collective Behavior of Coupled Oscillators for Solving DCOP

The distributed constraint optimization problem (DCOP) has emerged as one of the most promising coordination techniques in multiagent systems. However, because DCOP is known to be NP-hard, the existing DCOP techniques are often unsuitable for large-scale applications, which require distributed and scalable algorithms to deal with severely limited computing and communication. In this paper, we present a novel approach to provide approximate solutions for large-scale, complex DCOPs. This approach introduces concepts of synchronization of coupled oscillators for speeding up the convergence process towards high-quality solutions. We propose a new anytime local search DCOP algorithm, called Coupled Oscillator OPTimization (COOPT), which amounts to iteratively solving a DCOP by agents exchanging local information that brings them to a consensus. We empirically evaluate COOPT on constraint networks involving hundreds of variables with different topologies, domains, and densities. Our experimental results demonstrate that COOPT outperforms other incomplete state-of-the-art DCOP algorithms, especially in terms of the agents' communication cost and solution quality.

Privacy stochastic games in distributed constraint reasoning

In this work, we approach the issue of privacy in distributed constraint reasoning by studying how agents compromise solution quality for preserving privacy, using utility and game theory. We propose a utilitarian definition of privacy in the context of distributed constraint reasoning, detail its different implications, and present a model and solvers, as well as their properties. We then show how important steps in a distributed constraint optimization with privacy requirements can be modeled as a planning problem, and more specifically as a stochastic game. We present experiments validating the interest of our approach, according to several criteria.

Distributed Gibbs: A Linear-Space Sampling-Based DCOP Algorithm


 
 
 Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this article, we introduce two new sampling-based DCOP algorithms called Sequential Distributed Gibbs (SD-Gibbs) and Parallel Distributed Gibbs (PD-Gibbs). Both algorithms have memory requirements per agent that is linear in the number of agents in the problem. Our empirical results show that our algorithms can find solutions that are better than DUCT, run faster than DUCT, and solve some large problems that DUCT failed to solve due to memory limitations.
 
 

Primal-Dual Subgradient Algorithm for Distributed Constraint Optimization Over Unbalanced Digraphs

This paper investigates the distributed convex optimization problem with coupled inequality constraints over unbalanced digraphs depicted as row-stochastic matrices, where each agent in the network only has access to its local information, while the local constraint functions of all the agents are coupled in inequality constraints. To solve this kind of problems, a novel distributed iterative algorithm is proposed via consensus scheme and the projected primal-dual subgradient method. Different from the previous related results, our algorithm can not only deal with coupling inequality constraints but also conquer the unbalanced topology caused by directed graphs. Moreover, it is proved that under certain assumptions, the optimal solution of the problem can be asymptotically obtained by performing the designed algorithm. Finally, the numerical examples are presented to further illustrate the efficacy of the proposed approach.

Communication-Sensitive Pseudo-Tree Heuristics for DCOP Algorithms

Distributed Constraint Optimization Problem (DCOP) is a powerful paradigm to model multi-agent systems through enabling multiple agents to coordinate with each other to solve a problem. These agents are often assumed to be cooperative, that is, they communicate with other agents in order to optimize a global objective. However, the communication times between all pairs of agents are assumed to be identical in the evaluation of most DCOP algorithms. This assumption is impractical in almost all real-world applications. In this paper, we study the impact of empirically evaluating a DCOP algorithm under the assumption that communication times between pairs of agents can vary. In addition, we evaluate a DCOP algorithm using ns-2, a discrete-event simulator that is widely used in the computer networking community, to simulate the communication times, as opposed to the standard DCOP simulators that are used to evaluate DCOP algorithms in the AI community. Furthermore, we propose heuristics that exploit the non-uniform communication times to speed up DCOP algorithms that operate on pseudo-trees. Our empirical results demonstrate that the proposed heuristics improve the runtime of those algorithms up to 20%. These heuristics are evaluated on different benchmarks such as scale-free graphs, random graphs, and an instance of the smart grid, Customer-Driven Microgrid (CDMG) application.

Privacy preserving region optimal algorithms for symmetric and asymmetric DCOPs

Region-optimal algorithms are local search algorithms for solving Distributed Constraint Optimization Problems (DCOPs). In each iteration of the search in such algorithms, every agent selects a group of agents that comply with some selection criteria (each algorithm specifies different criteria). Then, the agent who selected the group, called the mediator, collects assignment information from the group and neighboring agents outside the group, in order to find an optimal set of assignments for its group's agents. A contest between mediators of adjacent groups determines which groups will replace their assignments in that iteration to the found optimal ones. In this work we present a framework called RODA (Region Optimal DCOP Algorithm) that encompasses the algorithms in the region optimality family, and in particular any method for selecting groups. We devise a secure implementation of RODA, called P-RODA, which preserves constraint privacy and partial decision privacy. Our discussion covers both symmetric and asymmetric DCOPs. The two main cryptographic means that enable this privacy preservation are secret sharing and homomorphic encryption. We estimate the computational overhead of P-RODA with respect to RODA and give an upper bound that depends on the group and domain sizes and on the graph topology, but not on the number of agents. The estimations are substantiated with experimental results, including experiments on a simulator, that compare the performance of P-RODA to that of its competing algorithm, P-Max-Sum.

Distributed Cooperative Onboard Planning for the Conflict Resolution of Unmanned Aerial Vehicles

The distributed cooperative onboard conflict resolution problem of unmanned aerial vehicles is studied in this paper. First, the dynamics of unmanned aerial vehicles and the safe separation constraint are studied. The monotonicity feature of the constraint condition is analyzed. The distributed conflict resolution problem is modeled as a distributed constraint optimization problem. Second, the distributed constraint optimization problem is solved in two steps. The feasible solutions are searched first, and the overall costs are minimized thereafter. The distributed stochastic gradient descent method is proposed to search the local near-optimal solutions. The self-improving mechanism is proposed to enhance the utilization efficiency of data exchanged among unmanned aerial vehicles. The “virtue adjustment” theorem is defined to motivate the cooperative coordination between unmanned aerial vehicles. Simulation results show that, compared with existing short-term algorithms, our algorithm could reduce the summation of additional flight distances and impacts on flight plans of unmanned aerial vehicles significantly and coordinate the flights of unmanned aerial vehicles in complex scenarios.

A class of iterative refined Max-sum algorithms via non-consecutive value propagation strategies

As an important technique to solve distributed constraint optimization problems, Max-sum has drawn a lot of attention and successfully been deployed in real applications. Unfortunately, Max-sum fails to converge in cyclic problems and usually traverses states with low quality. Max-sum_AD and Max-sum_ADVP were proposed to guarantee the single phase convergence and the cross phase convergence respectively, and greatly improve the solution quality of Max-sum. However, the solution quality is closely related to the timing for starting value propagation in Max-sum_ADVP. In other words, low-quality initial assignments will lead to a poor result. In this paper, we prove that value propagation could restrict the exploration ability brought by Max-sum and eventually makes Max-sum_ADVP equivalent to a sequential greedy local search algorithm. For getting a balance between exploration and exploitation, several non-consecutive value propagation strategies are proposed to relax the restriction caused by value propagation: single-side value propagation which executes value propagation and Max-sum_AD in an interleaved way, probabilistic value propagation which performs value propagation stochastically and hybrid belief/value propagation where agents perform Max-sum_AD and value propagation in one round. We illustrate that agents in our algorithms can make decisions beyond local functions. Our empirical evaluations demonstrate the superiority of our methods over Max-sum and its variants. It also can be found that our methods are independent of the value propagation timing which is a major concern in Max-sum_ADVP.

Dynamic process scheduling and resource allocation in distributed environment: an agent-based modelling and simulation

ABSTRACTThis paper addresses the issues concerning resource allocation and process scheduling in a dynamic environment, where resources are distributed and availability of them is uncertain. In this context, we introduce a new multi-agent-based resource allocation and process scheduling approach, where agents communicate and cooperate among themselves to produce an optimal schedule. A distributed constraint optimization problem-based model in accordance with Markov Decision Process is proposed in this regard. We overcome the hardship of existing centralized approach and our technique optimizes not only the process completion delay but also the number of resources being idle, which is much more beneficial. Apart from the theoretical approach, we take a case study in its practical application domain to validate our claim. Analysis and experimental results show that this proposed method outperforms the state-of-the-art methods and bridges the gap between theory and its applications.

A Distributed Agent-Based Approach for Optimal QoS Selection in Web of Object Choreography

The Internet of Things (IoT) refers to the domain of physical/logical objects connected to the Internet that can be accessible anywhere and anytime, giving the access policies. Web objects and services can be seen as distributed and cooperative agents that need to collaborate in order to reach advanced functionalities and also to optimize the overall quality offered to the end users. However, the number of the services that can be supplied through these devices on the Internet will increase more and more. In this context, performing the composition and selection of the best services according to quality-of-service (QoS) criteria become a complex task, especially when dealing with global service selection to satisfy global constraints of users in decentralized environments. For this purpose, we propose a distributed and optimal QoS selection approach based on the multiagent paradigm and the distributed constraint optimization problem (DCOP) formalism. Hence, we revisited a DCOP technique for developing an efficient algorithm: the DPOP4QoS implemented under a real-time Web protocol for IoT communication, the extensible messaging and presence protocol. The proposed algorithm takes into account the specificities of the service composition context and the satisfaction of the global user constraints. This paper also presents a set of experiments conducted under realistic distributed environment to evaluate the performance of the proposed algorithm.

An Ant-Based Algorithm to Solve Distributed Constraint Optimization Problems

As an important population-based algorithm, ant colony optimization (ACO) has been successfully applied into various combinatorial optimization problems. However, much existing work in ACO focuses on solving centralized problems. In this paper, we present a novel algorithm that takes the power of ants to solve Distributed Constraint Optimization Problems (DCOPs), called ACO_DCOP. In ACO_DCOP, a new mechanism that captures local benefits is proposed to compute heuristic factors and a new method that considers the cost structure of DCOPs is proposed to compute pheromone deltas appropriately. Moreover, pipelining technique is introduced to make full use of the computational capacity and improve the efficiency. In our theoretical analysis, we prove that ACO_DCOP is an anytime algorithm. Our empirical evaluation indicates that ACO_DCOP is able to find solutions of equal or significantly higher quality than state-of-the-art DCOP algorithms.

Probabilistic optimal solution assessment for DCOPs

Distributed Constraint Optimization Problems (DCOPs) are widely used in Multi-Agent Systems for coordination and scheduling. The present paper proposes a heuristic algorithm that uses probabilistic assessment of the optimal solution in order to quickly find a solution that is not far from the optimal one. The heuristic assessment uses two passes by the agents to produce a high-quality solution. Extensive performance evaluation demonstrates that the solution of the proposed probabilistic assessment algorithm is indeed very close to the optimum, on smaller problems where this could be measured. In larger set-ups, the quality of the solution is demonstrated relatively to standard incomplete search algorithms.

Distributed Constraint Optimization Problems and Applications: A Survey

The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments. This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

Applying max-sum to teams of mobile sensing agents

Multi-agent applications that include teams of mobile sensing agents are challenging since they are inherently dynamic and a single movement of a mobile sensor can change the problem that the entire team is facing. A variation of the Distributed Constraint Optimization model for Mobile Sensor Teams (DCOP_MST) was previously adjusted to represent such problems along with local search algorithms that were enhanced with exploration methods. This paper considers the use of the Max-sum algorithm for solving problems of deploying a mobile sensor team in an unknown environment to track and monitor points of interest (targets), represented by the DCOP_MST model.The DCOP_MST model allows the representation of different functions for aggregating the joint coverage of targets by multiple sensors. The use of different functions has a dramatic effect on the complexity of the Max-sum algorithm. When using cardinality functions, Max-sum can be performed efficiently regardless of the arity of constraints. When Max-sum is used to solve applications that require other (more complex) aggregation functions, its complexity is exponential in the arity of the constraints and thus, its usefulness is limited.In this paper we investigate the performance of the Max-sum algorithm on two implementations of the DCOP_MST model. Each implementation considers a different joint credibility function for determining the coverage for each target, with respect to the locations and the credibility of agents. In the first, the coverage is calculated according to the number of agents that are located within sensing range from the target. This function can be calculated efficiently. The second takes the angle between the lines of sight of different agents to a target into consideration. The larger the difference in the angle between the lines of sight, the higher the coverage efficiency.We analyze the challenges in adjusting the Max-sum algorithm in both scenarios and propose enhancements of the algorithm that make it more efficient. We provide empirical evidence of the advantages resulting from these enhancements in comparison to the naive algorithm.