A formal verification framework and associated tools for enterprise modeling : application to UEML

The aim of this paper is to propose and apply a verification and validation approach to Enterprise Modeling that enables the user to improve the relevance and correctness, the suitability and coherence of a model by using properties specification and formal proof of properties

Reasoning on Grasp-Action Affordances

Artificial intelligence is essential to succeed in challenging activities that involve dynamic environments, such as object manipulation tasks in indoor scenes. Most of the state-of-the-art literature explores robotic grasping methods by focusing exclusively on attributes of the target object. When it comes to human perceptual learning approaches, these physical qualities are not only inferred from the object, but also from the characteristics of the surroundings. This work proposes a method that includes environmental context to reason on an object affordance to then deduce its grasping regions. This affordance is reasoned using a ranked association of visual semantic attributes harvested in a knowledge base graph representation. The framework is assessed using standard learning evaluation metrics and the zero-shot affordance prediction scenario. The resulting grasping areas are compared with unseen labelled data to asses their accuracy matching percentage. The outcome of this evaluation suggest the autonomy capabilities of the proposed method for object interaction applications in indoor environments.Comment: Annual Conference Towards Autonomous Robotic Systems (TAROS19

A geometry of information, I: Nerves, posets and differential forms

The main theme of this workshop (Dagstuhl seminar 04351) is `Spatial Representation: Continuous vs. Discrete'. Spatial representation has two contrasting but interacting aspects (i) representation of spaces' and (ii) representation by spaces. In this paper, we will examine two aspects that are common to both interpretations of the theme, namely nerve constructions and refinement. Representations change, data changes, spaces change. We will examine the possibility of a `differential geometry' of spatial representations of both types, and in the sequel give an algebra of differential forms that has the potential to handle the dynamical aspect of such a geometry. We will discuss briefly a conjectured class of spaces, generalising the Cantor set which would seem ideal as a test-bed for the set of tools we are developing.Comment: 28 pages. A version of this paper appears also on the Dagstuhl seminar portal http://drops.dagstuhl.de/portals/04351

Quotational higher-order thought theory

© 2015. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Due to their reliance on constitutive higher-order representing to generate the qualities of which the subject is consciously aware, I argue that the major existing higher-order representational theories of consciousness insulate us from our first-order sensory states. In fact on these views we are never properly conscious of our sensory states at all. In their place I offer a new higher-order theory of consciousness, with a view to making us suitably intimate with our sensory states in experience. This theory relies on the idea of ‘quoting’ sensory qualities, so is dubbed the ‘quotational higher-order thought theory’. I argue that it can capture something of the idea that we are ‘acquainted’ with our conscious states without slipping beyond the pale for naturalists, whilst also providing satisfying treatments of traditional problems for higher-order theories concerning representational mismatch. The theory achieves this by abandoning a representational mechanism for mental intentionality, in favour of one based on ‘embedding’Peer reviewedFinal Published versio

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Consciousness and information integration

Integration information theories posit that the integration of information is necessary and/or sufficient for consciousness. In this paper, we focus on three of the most prominent information integration theories: Information Integration Theory, Global Workspace Theory, and Attended Intermediate-Level Theory. We begin by explicating each theory and key concepts they utilize. We then argue that the current evidence indicates that the integration of information is neither necessary nor sufficient for consciousness. Unlike GWT and AIR, IIT maintains that conscious experience is both necessary and sufficient for consciousness. We present empirical evidence indicating that simple features are experienced in the absence of feature integration and argue that it challenges IIT’s necessity claim. In addition, we challenge IIT’s sufficiency claim by presenting evidence from hemineglect cases and amodal completion indicating that contents may be integrated and yet fail to give rise to subjective experience. Moreover, we present empirical evidence from subjects with frontal lesions who are unable to carry out simple instructions and argue that they are irreconcilable with GWT. Lastly, we argue that empirical evidence indicating that patients with visual agnosia fail to identify objects they report being conscious of present a challenge to AIR’s necessity claim

Meta-analyses support a taxonomic model for representations of different categories of audio-visual interaction events in the human brain

Our ability to perceive meaningful action events involving objects, people and other animate agents is characterized in part by an interplay of visual and auditory sensory processing and their cross-modal interactions. However, this multisensory ability can be altered or dysfunctional in some hearing and sighted individuals, and in some clinical populations. The present meta-analysis sought to test current hypotheses regarding neurobiological architectures that may mediate audio-visual multisensory processing. Reported coordinates from 82 neuroimaging studies (137 experiments) that revealed some form of audio-visual interaction in discrete brain regions were compiled, converted to a common coordinate space, and then organized along specific categorical dimensions to generate activation likelihood estimate (ALE) brain maps and various contrasts of those derived maps. The results revealed brain regions (cortical “hubs”) preferentially involved in multisensory processing along different stimulus category dimensions, including (1) living versus non-living audio-visual events, (2) audio-visual events involving vocalizations versus actions by living sources, (3) emotionally valent events, and (4) dynamic-visual versus static-visual audio-visual stimuli. These meta-analysis results are discussed in the context of neurocomputational theories of semantic knowledge representations and perception, and the brain volumes of interest are available for download to facilitate data interpretation for future neuroimaging studies