39 research outputs found
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
Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law
In this paper we propose a novel message-passing algorithm, the so-called Action-GDL, as an extension to the generalized distributive law (GDL) to ef¿ciently solve DCOPs. Action-GDL provides a unifying perspective of several dynamic programming DCOP algorithms that are based on GDL, such as DPOP and DCPOP algorithms. We empirically show how Action-GDL using a novel distributed post-processing heuristic can outperform DCPOP, and by extension DPOP, even when the latter uses the best arrangement provided by multiple state-of-the-art heuristics.Work funded by IEA (TIN2006-15662-C02-01), AT (CONSOLIDER CSD2007-0022, INGENIO 2010) and EVE (TIN2009-14702-C02-01 and 02). Vinyals is supported by the Spanish Ministry of Education (FPU grant AP2006-04636)Peer Reviewe
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,
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, we introduce a context evaluation mechanism
to select the context patterns for the inference to further reduce the
overheads incurred by iterative inferences. Finally, we prove the
correctness of our algorithm and the experimental results demonstrate its
superiority over the state-of-the-art
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning
Modern representation learning methods often struggle to adapt quickly under
non-stationarity because they suffer from catastrophic forgetting and decaying
plasticity. Such problems prevent learners from fast adaptation since they may
forget useful features or have difficulty learning new ones. Hence, these
methods are rendered ineffective for continual learning. This paper proposes
Utility-based Perturbed Gradient Descent (UPGD), an online learning algorithm
well-suited for continual learning agents. UPGD protects useful weights or
features from forgetting and perturbs less useful ones based on their
utilities. Our empirical results show that UPGD helps reduce forgetting and
maintain plasticity, enabling modern representation learning methods to work
effectively in continual learning
A Distributed, Complete Method for Multi-Agent Constraint Optimization
We present in this paper a new complete method for distributed constraint optimization. This is a utility-propagation method, inspired by the sum-product algorithm. The original algorithm requires fixed message sizes, linear memory, and is time-linear in the size of the problem. However, it is correct only for tree-shaped constraint networks. In this paper, we show how to extend the algorithm to arbitrary topologies using cycle cutsets, while preserving the linear message size and memory requirements. We present some preliminary experimental results on randomly generated problems. The algorithm is formulated for optimization problems, but can be easily applied to satisfaction problems as well
Comparison of three concepts for offshore CO2 temporary storage and injection facilities
AbstractThis study proposed three concepts for the offshore temporary storage and injection facility for ship-based CO2 disposal in the deep sea geological formations. The first was floating on the sea surface, another was placed in the mid-water, and the last was stranded on the seabed. The first received the pressurized liquid CO2 from the CO2 carrier and stored it under an elevated pressure. As injection continued, the liquid level would drop, and the vaporized CO2 was supposed to make up the decrease in the liquid volume. The mid-water and stranded ones had the same functions of buffering storage and injection as the floating one. Their working principle, however, was strikingly different from the last. They were supposed to be installed in a depth where the hydrostatic pressure was greater than the liquid CO2 vapor pressure. Under the circumstance the liquid in the mid-water and stranded ones was in subcooled liquid state. The pressure difference between their interior and the surrounding water was negligible, only due to the density difference. This implied that the main structure for them did not have to be a pressure vessel