15,747 research outputs found
Metaphor in Analytic Philosophy and Cognitive Science
This article surveys theories of metaphor in analytic philosophy and cognitive science. In particular, it focuses on contemporary semantic, pragmatic and non-cognitivist theories of linguistic metaphor and on the Conceptual Metaphor Theory advanced by George Lakoff and his school. Special attention is given to the mechanisms that are shared by nearly all these approaches, i.e. mechanisms of interaction and mapping between conceptual domains. Finally, the article discusses several recent attempts to combine these theories of linguistic and conceptual metaphor into a unitary account
LTLf and LDLf Monitoring: A Technical Report
Runtime monitoring is one of the central tasks to provide operational
decision support to running business processes, and check on-the-fly whether
they comply with constraints and rules. We study runtime monitoring of
properties expressed in LTL on finite traces (LTLf) and in its extension LDLf.
LDLf is a powerful logic that captures all monadic second order logic on finite
traces, which is obtained by combining regular expressions and LTLf, adopting
the syntax of propositional dynamic logic (PDL). Interestingly, in spite of its
greater expressivity, LDLf has exactly the same computational complexity of
LTLf. We show that LDLf is able to capture, in the logic itself, not only the
constraints to be monitored, but also the de-facto standard RV-LTL monitors.
This makes it possible to declaratively capture monitoring metaconstraints, and
check them by relying on usual logical services instead of ad-hoc algorithms.
This, in turn, enables to flexibly monitor constraints depending on the
monitoring state of other constraints, e.g., "compensation" constraints that
are only checked when others are detected to be violated. In addition, we
devise a direct translation of LDLf formulas into nondeterministic automata,
avoiding to detour to Buechi automata or alternating automata, and we use it to
implement a monitoring plug-in for the PROM suite
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering
Many vision and language tasks require commonsense reasoning beyond
data-driven image and natural language processing. Here we adopt Visual
Question Answering (VQA) as an example task, where a system is expected to
answer a question in natural language about an image. Current state-of-the-art
systems attempted to solve the task using deep neural architectures and
achieved promising performance. However, the resulting systems are generally
opaque and they struggle in understanding questions for which extra knowledge
is required. In this paper, we present an explicit reasoning layer on top of a
set of penultimate neural network based systems. The reasoning layer enables
reasoning and answering questions where additional knowledge is required, and
at the same time provides an interpretable interface to the end users.
Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) based
engine to reason over a basket of inputs: visual relations, the semantic parse
of the question, and background ontological knowledge from word2vec and
ConceptNet. Experimental analysis of the answers and the key evidential
predicates generated on the VQA dataset validate our approach.Comment: 9 pages, 3 figures, AAAI 201
Semantics as a gateway to language
This paper presents an account of semantics as a system that integrates conceptual representations into language. I define the semantic system as an interface level of the conceptual system CS that translates conceptual representations into a format that is accessible by language. The analysis I put forward does not treat the make up of this level as idiosyncratic, but subsumes it under a unified notion of linguistic interfaces. This allows us to understand core aspects of the linguistic-conceptual interface as an instance of a general pattern underlying the correlation of linguistic and non-linguistic structures. By doing so, the model aims to provide a broader perspective onto the distinction between and interaction of conceptual and linguistic processes and the correlation of semantic and syntactic structures
Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction
This paper presents a computational model of the processing of dynamic
spatial relations occurring in an embodied robotic interaction setup. A
complete system is introduced that allows autonomous robots to produce and
interpret dynamic spatial phrases (in English) given an environment of moving
objects. The model unites two separate research strands: computational
cognitive semantics and on commonsense spatial representation and reasoning.
The model for the first time demonstrates an integration of these different
strands.Comment: in: Pham, D.-N. and Park, S.-B., editors, PRICAI 2014: Trends in
Artificial Intelligence, volume 8862 of Lecture Notes in Computer Science,
pages 958-971. Springe
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