In this paper we deal with the problem of optimally placing a set of query operators in an overlay network. Each user is interested in performing a query on streaming data and each query has an associated set of innetwork operators that filter, aggregate and process the data in various ways. Each user has private information about the operators associated with a query and about the utility from different combinations of operator placements. Each server in the overlay network is able to perform some set of operators, and servers differ in their network and computational characteristics. We model this problem as a Distributed Constraint Optimization Problem (DCOP), and apply the M-DPOP algorithm from Petcu et al. [19], executed here by clients associated with users and situated at nodes on the overlay network. M-DPOP makes truth-telling an ex-post Nash equilibrium and determines the social-welfare maximizing placement of operators to servers. No client can benefit by deviating from the M-DPOP algorithm and nodes need only communicate with other nodes that have an interest in placing an operator on the same server. The only central authority required is a bank that can extract payments from users. Preliminary results from simulation show that message size will be a bottleneck in applying M-DPOP to operator placement unless structure can be enforced and then exploited. 1

Abstract. A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses DCOPolis—a framework for comparing and deploying DCOP software in heterogeneous environments—to contribute an analysis of two state-of-the-art DCOP algorithms run in various network environments solving a number of different problem types. Then, we use this empirical validation to evaluate the use of both cycle-based runtime and concurrent constraint checks. 1

Several distributed constraint reasoning algorithms employ Depth First Search (DFS) trees on the constraint graph that spans involved agents. In this article we show that it is possible to dynamically detect a minimal DFS tree, compatible with the current order on agents, during the distributed constraint reasoning process of the ADOPT algorithm. This also allows for shorter DFS trees during the initial steps of the algorithm, while some constraints did not yet prove useful given visited combinations of assignments. Earlier distributed algorithms for finding spanning trees on agents did not look to maintain compatibility with an order already used. We also show that announcing a nogood to a single optional agent is bringing significant improvements in the total number of messages. The dynamic detection of the DFS tree brings improvements in simulated time. 1

Multi-agent systems that work with people to accomplish tasks require access to infor-mation that their users consider private. Mechanisms that protect this private informa-tion from the other participants and accurate characterizations of the extent to which these mechanisms do so are essential for the adoption of such systems. This thesis examines these issues in the context of algorithms for distributed constraint optimization (DCOP), a prominent technique for multi-agent coordination. Prior research on DCOP algorithms has focused on the tradeoffs between efficiency and optimality and largely ignored privacy questions. To characterize the level of privacy protection in DCOP algorithms, this thesis defines four privacy properties: the Global Loss Property, the Maximum Adversary Property, the Maximum Victim Property and the Cost-For-Loss Property. These properties provide a global view of the amount of private information lost during optimization as well as a more local view of the way that the leakage of private information affects individual participants. The thesis analyzes the extent to which existing metrics assess privacy loss as defined by

Abstract. The original ADOPT-ng has three major versions, corresponding to three different classes of feedback possibilities. The first version is identical to the scheme of the original ADOPT, where messages with feedback are communicated only to the variable of one’s parent node in the DFS of the constraint graph. It is similar to the Graph-Based Backjumping concept common in Constraint Satisfaction (CSPs), except that the asynchronous computation paradigm makes the term “backjumping ” less intuitively accurate. The second major version of ADOPT-ng communicates costs to higher priority agents based on dependencies detected dynamically. The third version combined dependencies detected dynamically with statically analyzed constraint graph structure. These versions are related to Conflict-Based Backjumping schemes in CSPs in the way conflicts are announced to earlier variables. Here we discuss and experiment in more detail the advantages and drawbacks of the different backjumping schemes and of some of their variations. While past experiments have shown that sending more feedback is better than sending the minimal information needed for correctness, new experiments show that one should not exaggerate sending too much feedback and that the best strategy is at an intermediary point. 1

Abstract. The Vehicle Routing Problem (VRP) has been extensively studied over the last twenty years, because it is an abstraction of many real-life logistics problems. In its multiple-depot variant (MDVRP), the routes of vehicles located at various depots must be optimized to serve a number of customers. In this paper, we investigate how to protect the privacy of delivery companies, when each depot is owned by a different company with a limited view of the overall problem. Companies then need to exchange messages with each other to coordinate the assignment of customers to depots. We show how Distributed Constraint Optimization (DCOP) can be used to solve the assignment problem using distributed computation, and we study the guarantees that can be provided with respect to the protection of each company’s knowledge about the problem. 1

This article presents an asynchronous algorithm for solving Distributed Constraint Optimization problems (DCOPs). The proposed technique unifies asynchronous backtracking (ABT) and asynchronous distributed optimization (ADOPT) where valued nogoods enable more flexible reasoning and more opportunities of communication, leading to an important speed-up. The concept of valued nogood is an extension of the concept of classic nogood that associates the list of conflicting assignments with a threshold and, optionally, with a set of references to culprit constraints. DCOPs have been shown to have very elegant distributed solutions, such as ADOPT, distributed asynchronous overlay (DisAO), or DPOP. These algorithms are typically tuned to minimize the longest causal chain of messages as a measure of how the algorithms will scale for systems with remote agents (with large latency in communication). ADOPT has the property of maintaining the initial distribution of the problem. ADOPT needs a preprocessing step consisting of computing a Depth-First Search (DFS) tree on the constraint graph. Valued nogoods allow for automatically detecting and exploiting the best DFS tree compatible with the current ordering and it is sufficient to ensure that a short such DFS tree exists. Also, the inference rules available for valued nogoods help to exploit schemes of communication where more feedback is sent to higher priority agents. Together they result in an order of magnitude improvement. 1

This article presents an asynchronous algorithm for solving Distributed Constraint Optimization problems (DCOPs). The proposed technique unifies asynchronous backtracking (ABT) and asynchronous distributed optimization (ADOPT) where valued nogoods enable more flexible reasoning and more opportunities for communication, leading to an important speed-up. While feedback can be sent in ADOPT by COST messages only to one predefined predecessor, our extension allows for sending such information to any relevant agent. The concept of valued nogood is an extension by Dago and Verfaille of the concept of classic nogood that associates the list of conflicting assignments with a cost and, optionally, with a set of references to culprit constraints. DCOPs have been shown to have very elegant distributed solutions, such as ADOPT, distributed asynchronous overlay (DisAO), or DPOP. These algorithms are typically tuned to minimize the longest causal chain of messages as a measure of how the algorithms will scale for systems with remote agents (with large latency in communication). ADOPT has the property of maintaining the initial distribution of the problem. To be efficient, ADOPT needs a preprocessing step consisting of computing a Depth-First Search (DFS) tree on the constraint graph. Valued nogoods allow for automatically detecting and exploiting the best DFS tree compatible with the current ordering. To exploit such DFS trees it is now sufficient to ensure that they exist. Also, the inference rules available for valued nogoods help to exploit schemes of communication where more feedback is sent to higher priority agents. Together they result in an order of magnitude improvement. 1.

We propose an efficient method that applies directed soft arc consistency to a Distributed Constraint Optimization Problem (DCOP) which is a fundamental framework of multi-agent systems. With DCOPs a multi-agent system is represented as a set of variables and a set of constraints/cost functions. We focus on DCOP solvers that employ pseudo-trees. A pseudo-tree is a graph structure for a constraint network that represents a partial ordering of variables. Most pseudo-tree-based search algorithms perform optimistic searches using explicit/implicit backtracking in parallel. However, for cost functions taking a wide range of cost values, such exact algorithms require many search iterations, even if the constraint density is relatively low. Therefore additional improvements are necessary to reduce the search process. A previous study used a dynamic programming-based preprocessing technique that estimates the lower

Abstract — A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses DCOPolis—a framework for comparing and deploying DCOP software in heterogeneous environments—to contribute an analysis of two state-of-the-art DCOP algorithms solving a number of different problem types. Then, we use this empirical validation to evaluate the use of both cycle-based runtime and concurrent constraint checks. I.