246 research outputs found
Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning
Integrating robotic systems in architectural and construction processes is of
core interest to increase the efficiency of the building industry. Automated
planning for such systems enables design analysis tools and facilitates faster
design iteration cycles for designers and engineers. However, generic
task-and-motion planning (TAMP) for long-horizon construction processes is
beyond the capabilities of current approaches. In this paper, we develop a
multi-agent TAMP framework for long horizon problems such as constructing a
full-scale building. To this end we extend the Logic-Geometric Programming
framework by sampling-based motion planning,a limited horizon approach, and a
task-specific structural stability optimization that allow an effective
decomposition of the task. We show that our framework is capable of
constructing a large pavilion built from several hundred geometrically unique
building elements from start to end autonomously
Automatic Context Pattern Generation for Entity Set Expansion
Entity Set Expansion (ESE) is a valuable task that aims to find entities of
the target semantic class described by given seed entities. Various NLP and IR
downstream applications have benefited from ESE due to its ability to discover
knowledge. Although existing bootstrapping methods have achieved great
progress, most of them still rely on manually pre-defined context patterns. A
non-negligible shortcoming of the pre-defined context patterns is that they
cannot be flexibly generalized to all kinds of semantic classes, and we call
this phenomenon as "semantic sensitivity". To address this problem, we devise a
context pattern generation module that utilizes autoregressive language models
(e.g., GPT-2) to automatically generate high-quality context patterns for
entities. In addition, we propose the GAPA, a novel ESE framework that
leverages the aforementioned GenerAted PAtterns to expand target entities.
Extensive experiments and detailed analyses on three widely used datasets
demonstrate the effectiveness of our method. All the codes of our experiments
will be available for reproducibility.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
Given a small set of seed entities (e.g., ``USA'', ``Russia''), corpus-based
set expansion is to induce an extensive set of entities which share the same
semantic class (Country in this example) from a given corpus. Set expansion
benefits a wide range of downstream applications in knowledge discovery, such
as web search, taxonomy construction, and query suggestion. Existing
corpus-based set expansion algorithms typically bootstrap the given seeds by
incorporating lexical patterns and distributional similarity. However, due to
no negative sets provided explicitly, these methods suffer from semantic drift
caused by expanding the seed set freely without guidance. We propose a new
framework, Set-CoExpan, that automatically generates auxiliary sets as negative
sets that are closely related to the target set of user's interest, and then
performs multiple sets co-expansion that extracts discriminative features by
comparing target set with auxiliary sets, to form multiple cohesive sets that
are distinctive from one another, thus resolving the semantic drift issue. In
this paper we demonstrate that by generating auxiliary sets, we can guide the
expansion process of target set to avoid touching those ambiguous areas around
the border with auxiliary sets, and we show that Set-CoExpan outperforms strong
baseline methods significantly.Comment: WWW 202
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