3,350 research outputs found
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
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
Robots frequently face complex tasks that require more than one action, where
sequential decision-making (SDM) capabilities become necessary. The key
contribution of this work is a robot SDM framework, called LCORPP, that
supports the simultaneous capabilities of supervised learning for passive state
estimation, automated reasoning with declarative human knowledge, and planning
under uncertainty toward achieving long-term goals. In particular, we use a
hybrid reasoning paradigm to refine the state estimator, and provide
informative priors for the probabilistic planner. In experiments, a mobile
robot is tasked with estimating human intentions using their motion
trajectories, declarative contextual knowledge, and human-robot interaction
(dialog-based and motion-based). Results suggest that, in efficiency and
accuracy, our framework performs better than its no-learning and no-reasoning
counterparts in office environment.Comment: In proceedings of 34th AAAI conference on Artificial Intelligence,
202
Applying spatial reasoning to topographical data with a grounded geographical ontology
Grounding an ontology upon geographical data has been pro-
posed as a method of handling the vagueness in the domain more effectively. In order to do this, we require methods of reasoning about the spatial relations between the regions within the data. This stage can be computationally expensive, as we require information on the location of
points in relation to each other. This paper illustrates how using knowledge about regions allows us to reduce the computation required in an efficient and easy to understand manner. Further, we show how this system can be implemented in co-ordination with segmented data to reason abou
A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets
Multimedia reasoning, which is suitable for, among others, multimedia content
analysis and high-level video scene interpretation, relies on the formal and
comprehensive conceptualization of the represented knowledge domain. However,
most multimedia ontologies are not exhaustive in terms of role definitions, and
do not incorporate complex role inclusions and role interdependencies. In fact,
most multimedia ontologies do not have a role box at all, and implement only a
basic subset of the available logical constructors. Consequently, their
application in multimedia reasoning is limited. To address the above issues,
VidOnt, the very first multimedia ontology with SROIQ(D) expressivity and a
DL-safe ruleset has been introduced for next-generation multimedia reasoning.
In contrast to the common practice, the formal grounding has been set in one of
the most expressive description logics, and the ontology validated with
industry-leading reasoners, namely HermiT and FaCT++. This paper also presents
best practices for developing multimedia ontologies, based on my ontology
engineering approach
Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web
Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Action Stories for Counter Terrorism (extended abstract)
Due to the raised terrorist threat worldwide, there is an urgent need to research that assists security and police services to protect the public and key assets and to prevent attacks from taking place. Successful protection and prevention may require potential and known suspects to be monitored or arrested. These operations are high risk because inappropriate surveillance, interview or arrest may have damaging political, public relations and intelligence effects. In addition to better tracking information on which to base suspicions, the security and police services need to have confidence that operations will yield evidence that can demonstrate conclusively that a deceptive activity such as a terrorist attack was in the process of being planned or executed before an operation takes place
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