8,480 research outputs found
Correct-by-Construction Approach for Self-Evolvable Robots
The paper presents a new formal way of modeling and designing reconfigurable
robots, in which case the robots are allowed to reconfigure not only
structurally but also functionally. We call such kind of robots
"self-evolvable", which have the potential to be more flexible to be used in a
wider range of tasks, in a wider range of environments, and with a wider range
of users. To accommodate such a concept, i.e., allowing a self-evovable robot
to be configured and reconfigured, we present a series of formal constructs,
e.g., structural reconfigurable grammar and functional reconfigurable grammar.
Furthermore, we present a correct-by-construction strategy, which, given the
description of a workspace, the formula specifying a task, and a set of
available modules, is capable of constructing during the design phase a robot
that is guaranteed to perform the task satisfactorily. We use a planar
multi-link manipulator as an example throughout the paper to demonstrate the
proposed modeling and designing procedures.Comment: The paper has 17 pages and 4 figure
Synthesis of Attributed Feature Models From Product Descriptions: Foundations
Feature modeling is a widely used formalism to characterize a set of products
(also called configurations). As a manual elaboration is a long and arduous
task, numerous techniques have been proposed to reverse engineer feature models
from various kinds of artefacts. But none of them synthesize feature attributes
(or constraints over attributes) despite the practical relevance of attributes
for documenting the different values across a range of products. In this
report, we develop an algorithm for synthesizing attributed feature models
given a set of product descriptions. We present sound, complete, and
parametrizable techniques for computing all possible hierarchies, feature
groups, placements of feature attributes, domain values, and constraints. We
perform a complexity analysis w.r.t. number of features, attributes,
configurations, and domain size. We also evaluate the scalability of our
synthesis procedure using randomized configuration matrices. This report is a
first step that aims to describe the foundations for synthesizing attributed
feature models
Program Synthesis using Natural Language
Interacting with computers is a ubiquitous activity for millions of people.
Repetitive or specialized tasks often require creation of small, often one-off,
programs. End-users struggle with learning and using the myriad of
domain-specific languages (DSLs) to effectively accomplish these tasks.
We present a general framework for constructing program synthesizers that
take natural language (NL) inputs and produce expressions in a target DSL. The
framework takes as input a DSL definition and training data consisting of
NL/DSL pairs. From these it constructs a synthesizer by learning optimal
weights and classifiers (using NLP features) that rank the outputs of a
keyword-programming based translation. We applied our framework to three
domains: repetitive text editing, an intelligent tutoring system, and flight
information queries. On 1200+ English descriptions, the respective synthesizers
rank the desired program as the top-1 and top-3 for 80% and 90% descriptions
respectively
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