13,709 research outputs found
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
We develop a natural language interface for human robot interaction that
implements reasoning about deep semantics in natural language. To realize the
required deep analysis, we employ methods from cognitive linguistics, namely
the modular and compositional framework of Embodied Construction Grammar (ECG)
[Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference
resolution problems and other issues related to deep semantics and
compositionality of natural language. This also includes verbal interaction
with humans to clarify commands and queries that are too ambiguous to be
executed safely. We implement our NLU framework as a ROS package and present
proof-of-concept scenarios with different robots, as well as a survey on the
state of the art
Understanding Internet topology: principles, models, and validation
Building on a recent effort that combines a first-principles approach to modeling router-level connectivity with a more pragmatic use of statistics and graph theory, we show in this paper that for the Internet, an improved understanding of its physical infrastructure is possible by viewing the physical connectivity as an annotated graph that delivers raw connectivity and bandwidth to the upper layers in the TCP/IP protocol stack, subject to practical constraints (e.g., router technology) and economic considerations (e.g., link costs). More importantly, by relying on data from Abilene, a Tier-1 ISP, and the Rocketfuel project, we provide empirical evidence in support of the proposed approach and its consistency with networking reality. To illustrate its utility, we: 1) show that our approach provides insight into the origin of high variability in measured or inferred router-level maps; 2) demonstrate that it easily accommodates the incorporation of additional objectives of network design (e.g., robustness to router failure); and 3) discuss how it complements ongoing community efforts to reverse-engineer the Internet
A Labelled Analytic Theorem Proving Environment for Categorial Grammar
We present a system for the investigation of computational properties of
categorial grammar parsing based on a labelled analytic tableaux theorem
prover. This proof method allows us to take a modular approach, in which the
basic grammar can be kept constant, while a range of categorial calculi can be
captured by assigning different properties to the labelling algebra. The
theorem proving strategy is particularly well suited to the treatment of
categorial grammar, because it allows us to distribute the computational cost
between the algorithm which deals with the grammatical types and the algebraic
checker which constrains the derivation.Comment: 11 pages, LaTeX2e, uses examples.sty and a4wide.st
Social Status and Badge Design
Many websites rely on user-generated content to provide value to consumers.
These websites typically incentivize participation by awarding users badges
based on their contributions. While these badges typically have no explicit
value, they act as symbols of social status within a community. In this paper,
we consider the design of badge mechanisms for the objective of maximizing the
total contributions made to a website. Users exert costly effort to make
contributions and, in return, are awarded with badges. A badge is only valued
to the extent that it signals social status and thus badge valuations are
determined endogenously by the number of users who earn each badge. The goal of
this paper is to study the design of optimal and approximately badge mechanisms
under these status valuations. We characterize badge mechanisms by whether they
use a coarse partitioning scheme, i.e. awarding the same badge to many users,
or use a fine partitioning scheme, i.e. awarding a unique badge to most users.
We find that the optimal mechanism uses both fine partitioning and coarse
partitioning. When status valuations exhibit a decreasing marginal value
property, we prove that coarse partitioning is a necessary feature of any
approximately optimal mechanism. Conversely, when status valuations exhibit an
increasing marginal value property, we prove that fine partitioning is
necessary for approximate optimality
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