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A corpus-based analysis of route instructions in human-robot interaction
This paper investigates how users employ spatial descriptions to navigate a speech-enabled robot. We created a simulated environment in which users gave route instructions in a dialogic real-time interaction with a robot, which was
operated by naïve participants. The ability of robot monitoring was also manipulated in two experimental conditions. The results provide evidence that the content of the instructions and strategies of the users vary depending on the conditions and
demands of the interaction. As expected, the route instructions frequently were underspecified and arbitrary. The findings of
this study elucidate the complexity in interpreting spatial language in HRI. However, they also point to the need for
endowing mobile robots with richer dialogue resources to compensate for the uncertainties arising from language as well
as the environment
Codeco: A Grammar Notation for Controlled Natural Language in Predictive Editors
Existing grammar frameworks do not work out particularly well for controlled
natural languages (CNL), especially if they are to be used in predictive
editors. I introduce in this paper a new grammar notation, called Codeco, which
is designed specifically for CNLs and predictive editors. Two different parsers
have been implemented and a large subset of Attempto Controlled English (ACE)
has been represented in Codeco. The results show that Codeco is practical,
adequate and efficient
Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking
Chain-of-Thought(CoT) prompting and its variants explore equipping large
language models (LLMs) with high-level reasoning abilities by emulating
human-like linear cognition and logic. However, the human mind is complicated
and mixed with both linear and nonlinear thinking. In this work, we propose
\textbf{I}nferential \textbf{E}xclusion \textbf{P}rompting (IEP), a novel
prompting that combines the principles of elimination and inference in order to
guide LLMs to think non-linearly. IEP guides LLMs to plan and then utilize
Natural Language Inference (NLI) to deduce each possible solution's entailment
relation with context, commonsense, or facts, therefore yielding a broader
perspective by thinking back for inferring. This forward planning and backward
eliminating process allows IEP to better simulate the complex human thinking
processes compared to other CoT-based methods, which only reflect linear
cognitive processes. We conducted a series of empirical studies and have
corroborated that IEP consistently outperforms CoT across various tasks.
Additionally, we observe that integrating IEP and CoT further improves the
LLMs' performance on certain tasks, highlighting the necessity of equipping
LLMs with mixed logic processes. Moreover, to better evaluate comprehensive
features inherent in human logic, we introduce \textbf{M}ental-\textbf{A}bility
\textbf{R}easoning \textbf{B}enchmark (MARB). The benchmark comprises six novel
subtasks with a total of 9,115 questions, among which 1,685 are developed with
hand-crafted rationale references. We believe both \textsc{IEP} and
\textsc{MARB} can serve as a promising direction for unveiling LLMs' logic and
verbal reasoning abilities and drive further advancements. \textsc{MARB} will
be available at ~\texttt{anonymity link} soon
A Visual Language for Web Querying and Reasoning
As XML is increasingly being used to represent information on the Web, query and reasoning languages for such data are needed. This article argues that in contrast to the navigational approach taken in particular by XPath and XQuery, a positional approach as used in the language Xcerpt is better suited for a straightforward visual representation. The constructs of the pattern- and rule-based query language Xcerpt are introduced and it is shown how the visual representation visXcerpt renders these constructs to form a visual query language for XML
The virtual guide: a direction giving embodied conversational agent
We present the Virtual Guide, an embodied conversational agent that can give directions in a 3D virtual environment. We discuss how dialogue management, language generation and the generation of appropriate gestures are carried out in our system
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