Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments

Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility

A Superstabilizing log⁡(n)\log(n)-Approximation Algorithm for Dynamic Steiner Trees

In this paper we design and prove correct a fully dynamic distributed algorithm for maintaining an approximate Steiner tree that connects via a minimum-weight spanning tree a subset of nodes of a network (referred as Steiner members or Steiner group) . Steiner trees are good candidates to efficiently implement communication primitives such as publish/subscribe or multicast, essential building blocks for the new emergent networks (e.g. P2P, sensor or adhoc networks). The cost of the solution returned by our algorithm is at most $\log |S|$ times the cost of an optimal solution, where $S$ is the group of members. Our algorithm improves over existing solutions in several ways. First, it tolerates the dynamism of both the group members and the network. Next, our algorithm is self-stabilizing, that is, it copes with nodes memory corruption. Last but not least, our algorithm is \emph{superstabilizing}. That is, while converging to a correct configuration (i.e., a Steiner tree) after a modification of the network, it keeps offering the Steiner tree service during the stabilization time to all members that have not been affected by this modification

Superstabilizing, Fault-containing Multiagent Combinatorial Optimization

Self stabilization in distributed systems is the ability of a system to respond to transient failures by eventually reaching a legal state, and maintaining it afterwards. This makes such systems particularly interesting because they can tolerate faults, and are able to cope with dynamic environments. In this paper we propose the first self stabilizing mechanism for multiagent combinatorial optimization, which stabilizes in a state corresponding to the optimal solution of the optimization problem. Our algorithm is based on dynamic programming, and requires a linear number of messages to find the optimal solution in the absence of faults. We show how our algorithm can be made super-stabilizing, in the sense that while transiting from one stable state to the next, our system preserves the assignments from the previous optimal state (similar to a "last-known-good" state), until the new optimal solution is found (without "random" changes to the variables). We offer equal bounds for the stabilization and the superstabilization time. Furthermore, we describe a general scheme for fault containment and fast response time upon low impact failures. Multiple, isolated failures are handled effectively. To show the merits of our approach we report on experiments with practical sized distributed meeting scheduling problems in a multiagent system

FRODO 2.0: An Open-Source Framework for Distributed Constraint Optimization

Distributed Constraint Optimization (DCOP) is a field that has recently been getting more and more attention from academia and industry. However, very few open-source, off-the-shelf tools are currently available to solve DCOPs; examples are FRODO, DisChoco and DCOPolis. A DCOP platform should possess the following key qualities: the framework should be reliable and extensively tested, deployable in a truly distributed setting, and modular so that it is easy to customize and extend. This paper introduces the Java-based FRODO 2.0 framework, which possesses all three qualities. It is a complete re-design of the FRODO framework, released under the GNU Affero GPL license

Optimal Dynamic Distributed MIS

Finding a maximal independent set (MIS) in a graph is a cornerstone task in distributed computing. The local nature of an MIS allows for fast solutions in a static distributed setting, which are logarithmic in the number of nodes or in their degrees. The result trivially applies for the dynamic distributed model, in which edges or nodes may be inserted or deleted. In this paper, we take a different approach which exploits locality to the extreme, and show how to update an MIS in a dynamic distributed setting, either \emph{synchronous} or \emph{asynchronous}, with only \emph{a single adjustment} and in a single round, in expectation. These strong guarantees hold for the \emph{complete fully dynamic} setting: Insertions and deletions, of edges as well as nodes, gracefully and abruptly. This strongly separates the static and dynamic distributed models, as super-constant lower bounds exist for computing an MIS in the former. Our results are obtained by a novel analysis of the surprisingly simple solution of carefully simulating the greedy \emph{sequential} MIS algorithm with a random ordering of the nodes. As such, our algorithm has a direct application as a $3$-approximation algorithm for correlation clustering. This adds to the important toolbox of distributed graph decompositions, which are widely used as crucial building blocks in distributed computing. Finally, our algorithm enjoys a useful \emph{history-independence} property, meaning the output is independent of the history of topology changes that constructed that graph. This means the output cannot be chosen, or even biased, by the adversary in case its goal is to prevent us from optimizing some objective function.Comment: 19 pages including appendix and reference

Embedding Preference Elicitation Within the Search for DCOP Solutions

The Distributed Constraint Optimization Problem(DCOP)formulation is a powerful tool to model cooperative multi-agent problems, especially when they are sparsely constrained with one another. A key assumption in this model is that all constraints are fully specified or known a priori, which may not hold in applications where constraints encode preferences of human users. In this thesis, we extend the model to Incomplete DCOPs (I-DCOPs), where some constraints can be partially specified. User preferences for these partially-specified constraints can be elicited during the execution of I-DCOP algorithms, but they incur some elicitation costs. Additionally, we propose two parameterized heuristics that can be used in conjunction with Synchronous Branch-and-Bound to solve I-DCOPs. These heuristics allow users to trade-off solution quality for faster runtimes and a smaller number of elicitations. They also provide theoretical quality guarantees for problems where elicitations are free. Our model and heuristics thus extend the state of the art in distributed constraint reasoning to better model and solve distributed agent-based applications with user preferences

Heuristics for Distributed Pseudo-tree Regeneration

The goal of this project is to develop a new heuristic for pseudo-tree regeneration in S-DPOP. S-DPOP is a version of DPOP which should show performance improvements when resolving problems after small changes. Examples of such problems are: tracking moving targets in sensor networks, truck-task scheduling and also matching patients and donors in kidney exchanges - a new problem for DCOP, which we investigate in this project . In this project, the DCOP-platform FRODO, developed by the artificial intelligence lab (LIA) at EPFL, will be used. Because there is no existing im- plementation of S-DPOP on FRODO, a significant part of the project consists in implementing S-DPOP on FRODO for the first time. The most important part of the project is the development and validation of a new DFS heuristic, which should maximize the amount of reuse when a problem is resolved after a minor change. Furthermore, we want to evaluate the performance of S-DPOP and the new heuristic on a real-life problem. For this, we use the kidney exchange problem, which is a problem class that has been studied in the centralized setting, but which is new to the DCOP community

Automatic construction, maintenance, and optimization of dynamic agent organizations

The goal of this dissertation is to generate organizational structures that increase the overall performance of a multiagent coalition, subject to the system's complex coordination requirements and maintenance of a certain operating point. To this end, a generalized framework capable of producing distributed approximation algorithms based on the new concept of multidirectional graph search is proposed and applied to a family of connectivity problems. It is shown that a wide variety of seemingly unrelated multiagent organization problems live within this family. Su cient conditions are identi ed in which the approach is guaranteed to discover a solution that is within a constant factor of the cost of the optimal solution. The procedure is guaranteed to require no more than linear|and in some well de ned cases logarithmic|communication rounds. A number of examples are given as to how the framework can be applied to create, maintain, and optimize multiagent organizations in the context of real world problems. Finally, algorithmic extensions are introduced that allow for the framework to handle problems in which the agent topology and/or coordination constraints are dynamic, without signi cant consequences to the general runtime, memory, and quality guarantees.Ph.D., Computer Science -- Drexel University, 201