Distributed constraint optimization with structured resource constraints

Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents ’ resource consumption must be taken into account. To address such scenarios, an extension of DCOP- Resource Constrained DCOP- has been proposed. However, certain type of resources have an additional structure associated with them and exploiting it can result in more efficient algorithms than possible with a general framework. An example of these are distribution networks, where the flow of a commodity from sources to sinks is limited by the flow capacity of edges. We present a new model of structured resource constraints that exploits the acyclicity and the flow conservation property of distribution networks. We show how this model can be used in efficient algorithms for finding the optimal flow configuration in distribution networks, an essential problem in managing power distribution networks. Experiments demonstrate the efficiency and scalability of our approach on publicly available benchmarks and compare favorably against a specialized solver for this task. Our results extend significantly the effectiveness of distributed constraint optimization for practical multi-agent settings

Distributed Constraint Optimization with Structured Resource Constraints

Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents' resource consumption must be taken into account. To address such scenarios, an extension of DCOP- Resource Constrained DCOP- has been proposed. However, certain type of resources have an additional structure associated with them and exploiting it can result in more efficient algorithms than possible with a general framework. An example of these are distribution networks, where the flow of a commodity from sources to sinks is limited by the flow capacity of edges. We present a new model of structured resource constraints that exploits the acyclicity and the flow conservation property of distribution networks. We show how this model can be used in efficient algorithms for finding the optimal flow configuration in distribution networks, an essential problem in managing power distribution networks. Experiments demonstrate the efficiency and scalability of our approach on publicly available benchmarks and compare favorably against a specialized solver for this task. Our results extend significantly the effectiveness of distributed constraint optimization for practical multi-agent settings

Solving DCOPs with Distributed Large Neighborhood Search

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. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale, complex applications, incomplete DCOP algorithms are necessary. Current incomplete DCOP algorithms suffer of one or more of the following limitations: they (a) find local minima without providing quality guarantees; (b) provide loose quality assessment; or (c) are unable to benefit from the structure of the problem, such as domain-dependent knowledge and hard constraints. Therefore, capitalizing on strategies from the centralized constraint solving community, we propose a Distributed Large Neighborhood Search (D-LNS) framework to solve DCOPs. The proposed framework (with its novel repair phase) provides guarantees on solution quality, refining upper and lower bounds during the iterative process, and can exploit domain-dependent structures. Our experimental results show that D-LNS outperforms other incomplete DCOP algorithms on both structured and unstructured problem instances

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

Distributed Gibbs: A memory-bounded sampling-based DCOP algorithm

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Near-Optimal Decentralized Power Supply Restoration in Smart Grids

Next generation of smart grids face a number of challenges includ-ing co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market dereg-ulation, and robust operation in the face of disasters. Such het-erogeneous nature and high operational readiness requirement of smart grids necessitates decentralized control for critical tasks such as power supply restoration (PSR) after line failures. We present a novel multiagent system based approach for PSR using Lagrangian dual decomposition. Our approach works on general graphs, pro-vides provable quality-bounds and requires only local message-passing among different connected sub-regions of a smart grid, en-abling decentralized control. Using these quality bounds, we show that our approach can provide near-optimal solutions on a num-ber of large real-world and synthetic benchmarks. Our approach compares favorably both in solution quality and scalability with previous best multiagent PSR approach