Topological Foundations of Cognitive Science

A collection of papers presented at the First International Summer Institute in Cognitive Science, University at Buffalo, July 1994, including the following papers: ** Topological Foundations of Cognitive Science, Barry Smith ** The Bounds of Axiomatisation, Graham White ** Rethinking Boundaries, Wojciech Zelaniec ** Sheaf Mereology and Space Cognition, Jean Petitot ** A Mereotopological Definition of 'Point', Carola Eschenbach ** Discreteness, Finiteness, and the Structure of Topological Spaces, Christopher Habel ** Mass Reference and the Geometry of Solids, Almerindo E. Ojeda ** Defining a 'Doughnut' Made Difficult, N .M. Gotts ** A Theory of Spatial Regions with Indeterminate Boundaries, A.G. Cohn and N.M. Gotts ** Mereotopological Construction of Time from Events, Fabio Pianesi and Achille C. Varzi ** Computational Mereology: A Study of Part-of Relations for Multi-media Indexing, Wlodek Zadrozny and Michelle Ki

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

A general cognitive framework for context-aware systems: extensions and applications for high level information fusion approaches

Mención Internacional en el título de doctorContext-aware systems aims at the development of computational systems that process data acquired from different datasources and adapt their behaviour in order to provide the 'right' information, at the 'right' time, in the 'right' place, in the 'right' way to the 'right' person (Fischer, 2012). Traditionally computational research has tried to answer these needs by means of low-level algorithms. In the last years the combination of numeric and symbolic approaches has offered the opportunity to create systems to deal with these issues. However, although the performance of algorithms and the quality of the data directly provided by computers and devices has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors. This dissertation proposes a set of extensions and applications focused on a cognitive framework for the implementation of context-aware systems based on a general model inspired by the Information Fusion paradigm. This model is stepped in several abstraction levels from low-level raw data to high level scene interpretation whose structure is determined by a set of ontologies. Each ontology level provides a skeleton that includes general concepts and relations to describe entities and their connections. This structure has been designed to promote extensibility and modularity, and might be refined to apply this model in specific domains. This framework combines a priori context knowledge represented with ontologies with real data coming from sensors to support logic-based high-level interpretation of the current situation and to automatically generate feedback recommendations to adjust data acquisition procedures. This work advocates for the introduction of general purpose cognitive layers in order to obtain a closer representation to the human cognition, generate additional knowledge and improve the high-level interpretation. Extensibility and adaptability of the basic ontology levels is demonstrated with the introduction of these traverse semantic layers which are able to be present and represent information at several granularity levels of knowledge using a common formalism. Context-based system must be able to reason about uncertainty. However the reasoning associated to ontologies has been limited to classical description logic mechanisms. This research also tackle the problem of reasoning under uncertainty circumstances through a logic-based paradigm for abductive reasoning: the Belief-Argumentation System. The main contribution of this dissertation is the adaptation of the general architecture and the theoretical proposals to several context-aware application areas such as Ambient Intelligence, Social Signal Processing and surveillance systems. The implementation of prototypes and examples for these areas are explained along this dissertation to progressively illustrate the improvements and extensions in the framework. To initially depict the general model, its components and the basic reasoning mechanisms a video-based Ambient Intelligence application is presented. The advantages and features of the framework extensions through traverse cognitive layers are demonstrated in a Social Signal Processing case for the elaboration of automatic market researches. Finally, the functioning of the system under uncertainty circumstances is illustrated with several examples to support decision makers in the detection of potential threats in common harbor scenarios.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Manuel Molina López.- Secretario: Ángel Arroyo.- Vocal: Nayat Sánchez P

The Metaphysics of Mental Representation

The representational theory of mind (RTM) explains the phenomenon of intentionality in terms of the existence and nature of mental representations. Despite the typical characterisation of mental representations in terms of their semantics, RTM is best understood as a metaphysical – more specifically formal ontological – theory whose primary defining feature is stipulating the existence of a class of mental particulars called representations. In this regard it is false, since mental representations do not exist. My argument is primarily methodological. Using an extended analysis of mereology and its variants as paradigmatic examples of a formal ontological theory, I argue for a 'synthetic’ approach to ontology which seeks to form a sound descriptive characterisation of the relevant phenomena from empirical data, to which philosophical analysis is applied to produce a rigorous theory. The value and necessity of this method is proved by example in our discussion of mereology which is shown to be defensible given certain assumptions, in particular perdurantism, but still inadequate as an account of parthood without considerable supplementation. We also see that there are viable alternatives which adopt a more synthetic approach and do not require the same assumptions. Having effectively demonstrated the value of a synthetic approach in ontology I critically examine the methodology employed by RTM and find it severely lacking. In the guise of ‘commonsense psychology’ RTM cavalierly imposes a theoretical framework without regard to empirical data, and this results in a severe distortion of the phenomenon of intentionality it purports to explain. RTM is methodologically unsound, and so its commitment to the existence of mental representations is utterly undermined. Furthermore the most attractive aspect of RTM – its semantics – can be separated from any commitment to mental representations existing. Even RTM’s strongest advocates lack motivation to believe that mental representations exist

“Voltage control on active networks under adverse conditions.”

Due to the inclusion of new loads and the predominant increase in electricity demand associated with the limitations of new environmental projects to minimize carbon emissions, such as pollution resulting from the energy generated by fossil fuels, the incorporation of electrical systems with distributed generation attributes to the energy planning, plans greater efficiency for various sectors of energy consumer groups worldwide. To maintain the effectiveness and reliable operation of the entire power system interconnected between grids and intelligent microgrids of electricity supply, standards must follow the established voltage levels in all terminals of the electrical power supply equipment supply, keeping them within limits. Both power utilities and distributed generation and consumers maintain the required design specifications for a reliable range of variation. The need to maintain a standardized voltage level is summed up in the treatment of possible failures that can occur when there is a voltage level acting beyond the limits established in extended equipment operating times. Due to the failure to maintain constant voltage levels along the electrical power grids several voltage control methods are applied, mainly controlling absorption, production and reactive power flow at all levels of the system, as well as when adverse system conditions where levels can achieve loss of system stability and voltage collapse. This research aims to characterize the appropriate methods for voltage correction and stability in active electrical networks under the influence of adverse conditions, whether natural influences or disasters, to influences related to the conditions of electrical energy systems, such as contingencies and distortions in other factors of the system that influence the level of voltage, to which some scientific publications relate [1-4], analyzing in an equationally calculated experimental way and simulations in MATLAB and ATPDRAW to prove the results.Agência

Modelling geographic phenomena at multiple levels of detail: A model generalisation approach based on aggregation

Considerable interest remains in capturing once geographical information at the fine scale, and from this, automatically deriving information at various levels of detail and scale via the process of map generalisation. This research aims to develop a methodology for transformation of geographic phenomena at a high level of detail directly into geographic phenomena at higher levels of abstraction. Intuitive and meaningful interpretation of geographical phenomena requires their representation at multiple levels of detail. This is due to the scale dependent nature of their properties. Prior to the cartographic portrayal of that information, model generalisation is required in order to derive higher order phenomena typically associated with the smaller scales. This research presents a model generalisation approach able to support the derivation of phenomena typically present at 1:250,000 scale mapping, directly from a large scale topographic database (1:1250/1:2500/1:10,000). Such a transformation involves creation of higher order or composite objects, such as settlement, forest, hills and ranges, from lower order or component objects, such as buildings, trees, streets, and vegetation, in the source database. In order to perform this transformation it is important to model the meaning and relationships among source database objects rather than to consider the object in terms of their geometric primitives (points, lines and polygons). This research focuses on two types of relationships: taxonomic and partonomic. These relationships provide different but complimentary strategies for transformation of source database objects into required target database objects. The proposed methodology highlights the importance of partonomic relations for transformation of spatial databases over large changes in levels of detail. The proposed approach involves identification of these relationships and then utilising these relationships to create higher order objects. The utility of the results obtained, via the implementation of the proposed methodology, is demonstrated using spatial analysis techniques and the creation of ‘links’ between objects at different representations needed for multiple representation databases. The output database can then act as input to cartographic generalisation in order to create maps (digital or paper). The results are evaluated using manually generalised datasets

Formal Design Concept And Participant Behavior Analysis For Crowdsourcing Design

Crowdsourcing has emerged as a new design resource for conceptual design process and multiple crowdsourcing services provide an opportunity for design idea collection and concept generation by crowds. However, few formal methods are available to extract and evaluate design concepts from the activities of the design crowd. Scarcity of information and non-guaranteed quality of contributions are often challenges to be tackled. To overcome the challenges, the research aims to answer how a system systematically extracts and represents the explicit or implicit hidden design concepts from crowdsourcing design activities and how crowdsourcing design activities of participants are captured as design information to develop a product in crowdsourcing platform in the perspectives of process and elements. This research provides taxonomy of design features to represent crowdsourcing design activities. With the taxonomy, a formal concept analysis method, Galois lattices, is applied to evaluate activities of design crowd and to extract possible design concepts. Using this approach, the crowd activities are represented with design features and participant information and it allows modeling the potential design concepts with the contributions of participants. Two participant evaluating measures, Participant Individual Score and Participant Group Score, are proposed to enhance the extracted design concepts with participants\u27 information. By employing the proposed scores and design features, this research figure out the significance of participants\u27 behavior in crowdsourcing design. In addition, a formal method to represent the processes and elements in crowdsourcing design activities with the theory adopted from social science, Actor Network Theory. The presented method and metrics are validated with a real design data collected from a crowdsourcing service by focus group interview and precision and recall tests