143,976 research outputs found
On the cost-complexity of multi-context systems
Multi-context systems provide a powerful framework for modelling
information-aggregation systems featuring heterogeneous reasoning components.
Their execution can, however, incur non-negligible cost. Here, we focus on
cost-complexity of such systems. To that end, we introduce cost-aware
multi-context systems, an extension of non-monotonic multi-context systems
framework taking into account costs incurred by execution of semantic operators
of the individual contexts. We formulate the notion of cost-complexity for
consistency and reasoning problems in MCSs. Subsequently, we provide a series
of results related to gradually more and more constrained classes of MCSs and
finally introduce an incremental cost-reducing algorithm solving the reasoning
problem for definite MCSs
Asynchronous Multi-Context Systems
In this work, we present asynchronous multi-context systems (aMCSs), which
provide a framework for loosely coupling different knowledge representation
formalisms that allows for online reasoning in a dynamic environment. Systems
of this kind may interact with the outside world via input and output streams
and may therefore react to a continuous flow of external information. In
contrast to recent proposals, contexts in an aMCS communicate with each other
in an asynchronous way which fits the needs of many application domains and is
beneficial for scalability. The federal semantics of aMCSs renders our
framework an integration approach rather than a knowledge representation
formalism itself. We illustrate the introduced concepts by means of an example
scenario dealing with rescue services. In addition, we compare aMCSs to
reactive multi-context systems and describe how to simulate the latter with our
novel approach.Comment: International Workshop on Reactive Concepts in Knowledge
Representation (ReactKnow 2014), co-located with the 21st European Conference
on Artificial Intelligence (ECAI 2014). Proceedings of the International
Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014),
pages 31-37, technical report, ISSN 1430-3701, Leipzig University, 2014.
http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-15056
Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification
Audio events are quite often overlapping in nature, and more prone to noise
than visual signals. There has been increasing evidence for the superior
performance of representations learned using sparse dictionaries for
applications like audio denoising and speech enhancement. This paper
concentrates on modifying the traditional reconstructive dictionary learning
algorithms, by incorporating a discriminative term into the objective function
in order to learn class-specific adversarial dictionaries that are good at
representing samples of their own class at the same time poor at representing
samples belonging to any other class. We quantitatively demonstrate the
effectiveness of our learned dictionaries as a stand-alone solution for both
binary as well as multi-class audio classification problems.Comment: Accepted in Asian Conference of Pattern Recognition (ACPR-2017
Belief merging within fragments of propositional logic
Recently, belief change within the framework of fragments of propositional
logic has gained increasing attention. Previous works focused on belief
contraction and belief revision on the Horn fragment. However, the problem of
belief merging within fragments of propositional logic has been neglected so
far. This paper presents a general approach to define new merging operators
derived from existing ones such that the result of merging remains in the
fragment under consideration. Our approach is not limited to the case of Horn
fragment but applicable to any fragment of propositional logic characterized by
a closure property on the sets of models of its formulae. We study the logical
properties of the proposed operators in terms of satisfaction of merging
postulates, considering in particular distance-based merging operators for Horn
and Krom fragments.Comment: To appear in the Proceedings of the 15th International Workshop on
Non-Monotonic Reasoning (NMR 2014
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