3,459 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
Multi-robot team formation control in the GUARDIANS project
Purpose
The GUARDIANS multi-robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots guide and accompany
the firefighters on site whilst indicating possible obstacles and the locations of danger and maintaining communications links.
Design/methodology/approach
In order to fulfill the aforementioned tasks the robots need to exhibit certain behaviours. Among the basic behaviours are capabilities to stay together as a
group, that is, generate a formation and navigate while keeping this formation.
The control model used to generate these behaviours is based on the so-called social potential field framework, which we adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots.
Findings
The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus the application has forced us to redefine the concept of a formation. Using the graph-theoretic terminology, we can say that a formation may be stretched out as a path or be compact as a star or wheel. We have implemented the developed behaviours in simulation environments as well as on real ERA-MOBI robots commonly referred to as Erratics. We discuss advantages and shortcomings of our model, based on the simulations as
well as on the implementation with a team of Erratics.</p
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.Comment: ACL 201
Generating Preview Tables for Entity Graphs
Users are tapping into massive, heterogeneous entity graphs for many
applications. It is challenging to select entity graphs for a particular need,
given abundant datasets from many sources and the oftentimes scarce information
for them. We propose methods to produce preview tables for compact presentation
of important entity types and relationships in entity graphs. The preview
tables assist users in attaining a quick and rough preview of the data. They
can be shown in a limited display space for a user to browse and explore,
before she decides to spend time and resources to fetch and investigate the
complete dataset. We formulate several optimization problems that look for
previews with the highest scores according to intuitive goodness measures,
under various constraints on preview size and distance between preview tables.
The optimization problem under distance constraint is NP-hard. We design a
dynamic-programming algorithm and an Apriori-style algorithm for finding
optimal previews. Results from experiments, comparison with related work and
user studies demonstrated the scoring measures' accuracy and the discovery
algorithms' efficiency.Comment: This is the camera-ready version of a SIGMOD16 paper. There might be
tiny differences in layout, spacing and linebreaking, compared with the
version in the SIGMOD16 proceedings, since we must submit TeX files and use
arXiv to compile the file
Handling constraints in Robust multi-objective optimization
Robust multi-objective optimization has emerged as an active research. A recent study proposed two different definitions of robust solutions in the context of multi-objective optimization. In this paper, we extend the concepts for finding robust solutions in the presence of active constraints. The meaning of robust solutions for constrained problems is demonstrated by suggesting three test problems and simulating an evolutionary multi-objective optimization method using the two definitions of robustness. The inclusion of constraint handling strategies makes the multi-objective robust optimization procedure more pragmatic and the procedure is now ready to be applied to real-world problems
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