Discovering information flow using a high dimensional conceptual space

This paper presents an informational inference mechanism realized via the use of a high dimensional conceptual space. More specifically, we claim to have operationalized important aspects of G?rdenforss recent three-level cognitive model. The connectionist level is primed with the Hyperspace Analogue to Language (HAL) algorithm which produces vector representations for use at the conceptual level. We show how inference at the symbolic level can be implemented by employing Barwise and Seligmans theory of information flow. This article also features heuristics for enhancing HAL-based representations via the use of quality properties, determining concept inclusion and computing concept composition. The worth of these heuristics in underpinning informational inference are demonstrated via a series of experiments. These experiments, though small in scale, show that informational inference proposed in this article has a very different character to the semantic associations produced by the Minkowski distance metric and concept similarity computed via the cosine coefficient. In short, informational inference generally uncovers concepts that are carried, or, in some cases, implied by another concept, (or combination of concepts)

Intersemiotic translation and transformational creativity

In this article we approach a case of intersemiotic translation as a paradigmatic example of Boden’s ‘transformational creativity’ category. To develop our argument, we consider Boden’s fundamental notion of ‘conceptual space’ as a regular pattern of semiotic action, or ‘habit’ (sensu Peirce). We exemplify with Gertrude Stein’s intersemiotic translation of Cézanne and Picasso’s proto-cubist and cubist paintings. The results of Stein’s IT transform the conceptual space of modern literature, constraining it towards new patterns of semiosis. Our association of Boden’s framework to describe a cognitive creative phenomenon with a philosophically robust theory of meaning results in a cognitive semiotic account of IT

Metaphor Across Time and Conceptual Space by James J. Mischler III

No abstract available

Probabilistic hyperspace analogue to language

Song and Bruza introduce a framework for Information Retrieval(IR) based on Gardenfor's three tiered cognitive model; Conceptual Spaces. They instantiate a conceptual space using Hyperspace Analogue to Language (HAL to generate higher order concepts which are later used for ad-hoc retrieval. In this poster, we propose an alternative implementation of the conceptual space by using a probabilistic HAL space (pHAL). To evaluate whether converting to such an implementation is beneficial we have performed an initial investigation comparing the concept combination of HAL against pHAL for the task of query expansion. Our experiments indicate that pHAL outperforms the original HAL method and that better query term selection methods can improve performance on both HAL and pHAL

Coexistences: ethics, society, and forms of life. Guest editor’s preface

The article is a preface to the symposium "Coexistences: Ethics, Society, and Forms of Life". It briefly illustrates the conceptual space where the notions of forms of life and coexistencies shape an approach to ethics and social philosophy

Bargaining over a common conceptual space

URL des Documents de travail : http://centredeconomiesorbonne.univ-paris1.fr/documents-de-travail/Documents de travail du Centre d'Economie de la Sorbonne 2014.52 - ISSN : 1955-611XTwo agents endowed with different individual conceptual spaces are engaged in a dialectic process to reach a common understanding. We model the process as a simple non-cooperative game and demonstrate three results. When the initial disagreement is focused, the bargaining process has a zero-sum structure. When the disagreement is widespread, the zero-sum structure disappears and the unique equilibrium requires a retraction of consensus: two agents who individually agree to associate a region with the same concept end up rebranding it as a different concept. Finally, we document a conversers' dilemma: such equilibrium outcome is Pareto-dominated by a cooperative solution that avoids retraction.Nous présentons dans cet article un modèle simple de jeux non-coopératif où deux agents dotés de deux espaces conceptuels différents entament un processus dialectique afin d'atteindre un terrain d'entente commun. On montre principalement trois résultats : Quand le désaccord est « focalisé », le processus de négociation est un jeu à somme nulle. Cependant, quand le désaccord est « répandu », l'unique équilibre exige une rétraction du consensus due à une forme d'entêtement : Deux agents qui étaient d'accord à associer à une même région le même concept finissent par la changer en un autre différent. Finalement, on observe que cet équilibre est Pareto-dominé par la solution coopérative

CONCEPTUAL SPACE OF THE NEW TESTAMENT’S TEXT

Artykuł poświęcony jest analizie poszczególnych konceptów w kontekście tekstowej całości. W pracy badano relacje głównych tematów i idei utworu ze strukturami konceptualnymi, które skupiają sensy pól tekstowych słów kluczy. Zaproponowana metodologia pozwala pokazać sens warstwy głębokiej tekstu określany przez konceptosferę utworu.This article is dedicated to the concepts research in the context of a text space. The proposed method shows the means of explication of deep textual meanings, which were formed by the Testament’s sphere of concepts.This article is dedicated to the concepts research in the context of a text space. The proposed method shows the means of explication of deep textual meanings, which were formed by the Testament’s sphere of concepts

Associative conceptual space-based information retrieval systems

In this `Information Era' with the availability of large collections of books, articles, journals, CD-ROMs, video films and so on, there exists an increasing need for intelligent information retrieval systems that enable users to find the information desired easily. Many attempts have been made to construct such retrieval systems, including the electronic ones used in libraries and including the search engines for the World Wide Web. In many cases, however, the so-called `precision' and `recall' of these systems leave much to be desired. In this paper, a new AI-based retrieval system is proposed, inspired by, among other things, the WEBSOM-algorithm. However, contrary to that approach where domain knowledge is extracted from the full text of all books, we propose a system where certain specific meta-information is automatically assembled using only the index of every document. This knowledge extraction process results into a new type of concept space, the so-called Associative Conceptual Space where the `concepts' as found in all documents are clustered using a Hebbian-type of learning algorithm. Then, each document can be characterised by comparing the concepts as occurring in it to those present in the associative conceptual space. Applying these characterisations, all documents can be clustered such that semantically similar documents lie close together on a Self-Organising Map. This map can easily be inspected by its user

Disentanglement in conceptual space during sensorimotor interaction

The disentanglement of different objective properties from the external world is the foundation of language development for agents. The basic target of this process is to summarise the common natural properties and then to name it todescribe those properties in the future. To realise this purpose, a new learning model is introduced for the disentanglement of several sensorimotor concepts (e.g. sizes, colours and shapes of objects) while the causal relationship is being learnt during interaction without much a priori experience and external instructions. This learning model links predictive deep neural models and the variational auto-encoder (VAE) and provides the possibility that the independent concepts can be extracted and disentangled from both perception and action. Moreover, such extraction is further learnt by VAE to memorise their common statistical features. The authors examine this model in the affordance learning setting, where the robot is trying to learn to disentangle about shapes of the tools and objects. The results show that such a process can be found in the neural activities of the β-VAE unit, which indicate that using similar VAE models is a promising way to learn the concepts, and thereby to learn the causal relationship of the sensorimotor interaction