135,714 research outputs found
Information and Experience in Metaphor: A Perspective From Computer Analysis
Novel linguistic metaphor can be seen as the assignment of attributes to a topic through a vehicle belonging to another domain. The experience evoked by the vehicle is a significant aspect of the meaning of the metaphor, especially for abstract metaphor, which involves more than mere physical similarity. In this article I indicate, through description of a specific model, some possibilities as well as limitations of computer processing directed toward both informative and experiential/affective aspects of metaphor. A background to the discussion is given by other computational treatments of metaphor analysis, as well as by some questions about metaphor originating in other disciplines. The approach on which the present metaphor analysis model is based is consistent with a theory of language comprehension that includes both the intent of the originator and the effect on the recipient of the metaphor. The model addresses the dual problem of (a) determining potentially salient properties of the vehicle concept, and (b) defining extensible symbolic representations of such properties, including affective and other connotations. The nature of the linguistic analysis underlying the model suggests how metaphoric expression of experiential components in abstract metaphor is dependent on the nominalization of actions and attributes. The inverse process of undoing such nominalizations in computer analysis of metaphor constitutes a translation of a metaphor to a more literal expression within the metaphor-nonmetaphor dichotomy
Inference of magnetic fields in inhomogeneous prominences
Most of the quantitative information about the magnetic field vector in solar
prominences comes from the analysis of the Hanle effect acting on lines formed
by scattering. As these lines can be of non-negligible optical thickness, it is
of interest to study the line formation process further. We investigate the
multidimensional effects on the interpretation of spectropolarimetric
observations, particularly on the inference of the magnetic field vector. We do
this by analyzing the differences between multidimensional models, which
involve fully self-consistent radiative transfer computations in the presence
of spatial inhomogeneities and velocity fields, and those which rely on simple
one-dimensional geometry. We study the formation of a prototype line in ad hoc
inhomogeneous, isothermal 2D prominence models. We solve the NLTE polarized
line formation problem in the presence of a large-scale oriented magnetic
field. The resulting polarized line profiles are then interpreted (i.e.
inverted) assuming a simple 1D slab model. We find that differences between
input and the inferred magnetic field vector are non-negligible. Namely, we
almost universally find that the inferred field is weaker and more horizontal
than the input field. Spatial inhomogeneities and radiative transfer have a
strong effect on scattering line polarization in the optically thick lines. In
real-life situations, ignoring these effects could lead to a serious
misinterpretation of spectropolarimetric observations of chromospheric objects
such as prominences.Comment: 11 pages, 9 figure
Inferring Acceptance and Rejection in Dialogue by Default Rules of Inference
This paper discusses the processes by which conversants in a dialogue can
infer whether their assertions and proposals have been accepted or rejected by
their conversational partners. It expands on previous work by showing that
logical consistency is a necessary indicator of acceptance, but that it is not
sufficient, and that logical inconsistency is sufficient as an indicator of
rejection, but it is not necessary. I show how conversants can use information
structure and prosody as well as logical reasoning in distinguishing between
acceptances and logically consistent rejections, and relate this work to
previous work on implicature and default reasoning by introducing three new
classes of rejection: {\sc implicature rejections}, {\sc epistemic rejections}
and {\sc deliberation rejections}. I show how these rejections are inferred as
a result of default inferences, which, by other analyses, would have been
blocked by the context. In order to account for these facts, I propose a model
of the common ground that allows these default inferences to go through, and
show how the model, originally proposed to account for the various forms of
acceptance, can also model all types of rejection.Comment: 37 pages, uses fullpage, lingmacros, name
Flexibly Instructable Agents
This paper presents an approach to learning from situated, interactive
tutorial instruction within an ongoing agent. Tutorial instruction is a
flexible (and thus powerful) paradigm for teaching tasks because it allows an
instructor to communicate whatever types of knowledge an agent might need in
whatever situations might arise. To support this flexibility, however, the
agent must be able to learn multiple kinds of knowledge from a broad range of
instructional interactions. Our approach, called situated explanation, achieves
such learning through a combination of analytic and inductive techniques. It
combines a form of explanation-based learning that is situated for each
instruction with a full suite of contextually guided responses to incomplete
explanations. The approach is implemented in an agent called Instructo-Soar
that learns hierarchies of new tasks and other domain knowledge from
interactive natural language instructions. Instructo-Soar meets three key
requirements of flexible instructability that distinguish it from previous
systems: (1) it can take known or unknown commands at any instruction point;
(2) it can handle instructions that apply to either its current situation or to
a hypothetical situation specified in language (as in, for instance,
conditional instructions); and (3) it can learn, from instructions, each class
of knowledge it uses to perform tasks.Comment: See http://www.jair.org/ for any accompanying file
Space exploration: The interstellar goal and Titan demonstration
Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology
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