Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the most time consuming and key components of CECEP is the mining of knowledge from a repository described as a Cognitive Domain Ontology (CDO). Given the number of possible solutions in the problems tasked to CDOs, determining the optimal solutions can be very time consuming. In this work we show how problems that are often solved using CDOs can be carried out using spiking neurons. Furthermore, this work discusses using the Intel Loihi manycore spiking neural network processor to solve CDOs using a technique inspired by a confabulation network. This work demonstrates the feasibility of implementing CDOs on embedded, low power, neuromorphic spiking hardware.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Cognitive Domain Ontologies Based on Loihi Spiking Neurons Implemented Using a Confabulation Inspired Network


    Beteiligte:
    Yakopcic, Chris (Autor:in) / Freeman, Jacob (Autor:in) / Taha, Tarek M. (Autor:in) / Douglass, Scott (Autor:in) / Wu, Qing (Autor:in)


    Erscheinungsdatum :

    2019-06-01


    Format / Umfang :

    366034 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    High Speed Approximate Cognitive Domain Ontologies for Asset Allocation based on Isolated Spiking Neurons

    Yakopcic, Chris / Atahary, Tanvir / Taha, Tarek M. et al. | IEEE | 2018



    Action Recognition Using a Bio-Inspired Feedforward Spiking Network

    Escobar, M. J. / Masson, G. S. / Vieville, T. et al. | British Library Online Contents | 2009


    Spiking Neural Network for Asset Allocation Implemented Using the TrueNorth System

    Yakopcic, Chris / Rahman, Nayim / Atahary, Tanvir et al. | IEEE | 2019