Now showing 1 - 4 of 4
  • Publication
    Intentional embodied agents
    Virtual environments present a suitable platform for the deployment of agent technologies. We advocate a system of virtual agents that are capable of changing their form in order to expand their capability sets. We use strong BDI agents for the control of this adaptation of form and behaviour. This paper outlines a system that allows for adaptable virtual agents, with the ability to change their form to suit the task at hand based upon deliberative reasoning.
  • Publication
    Evaluating the benefits of Octree-based indexing for LiDAR data
    (American Society for Photogrammetry & Remote Sensing, 2012) ; ; ;
    In recent years the geospatial domain has seen a significant increase in the availability of very large three dimensional (3D) point datasets. These datasets originate from a variety of sources, such as for example Light Detection and Ranging (LiDAR) or meteorological weather recordings. Increasingly, a desire within the geospatial community has been expressed to exploit these types of 3D point data in a meaningful engineering context that goes beyond mere visualization. However, current Spatial Information Systems (SISs) provide only limited support for vast 3D point datasets. Even those systems that advertise their support for in-built 3D data types provide very limited functionality to manipulate such data types. In particular, an effective means of indexing large 3D point datasets is yet missing, however it is crucial for effective analysis. Next to the large size of 3D point datasets they may also be information rich, for example they may contain color information or some other associated semantic. This paper presents an alternative spatial indexing technique, which is based on an octree data structure. We show that it outperforms R-tree index, while being able to group 3D points based on their attribute values at the same time. This paper presents an evaluation employing this octree spatial indexing technique and successfully highlights its advantages for sparse as well as uniformly distributed data on the basis of an extensive LiDAR dataset.
  • Publication
    Future reasoning machines : mind and body
    Purpose – In investing energy in developing reasoning machines of the future, one must abstract away from the specific solutions to specific problems and ask what are the fundamental research questions that should be addressed. This paper aims to revisit some fundamental perspectives and promote new approaches to reasoning machines and their associated form and function. Design/methodology/approach – Core aspects are discussed, namely the one-mind-many-bodies metaphor as introduced in the agent Chameleon work. Within this metaphor the agent's embodiment form may take many guises with the artificial mind or agent potentially exhibiting a nomadic existence opportunistically migrating between a myriad of instantiated embodiments. The paper animates these concepts with reference to two case studies. Findings – The two case studies illustrate how a machine can have fundamentally different capabilities than a human which allows us to exploit, rather than be constrained, by these important differences. Originality/value – Aids in understanding some of the fundamental research questions of reasoning machines that should be addressed.
      470Scopus© Citations 6
  • Publication
    Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS
    A large proportion of today's digital datasets have a spatial component. The effective storage and management of which poses particular challenges, especially with light detection and ranging (LiDAR), where datasets of even small geographic areas may contain several hundred million points. While in the last decade 2.5-dimensional data were prevalent, true 3-dimensional data are increasingly commonplace via LiDAR. They have gained particular popularity for urban applications including generation of city-scale maps, baseline data disaster management, and utility planning. Additionally, LiDAR is commonly used for flood plane identification, coastal-erosion tracking, and forest biomass mapping. Despite growing data availability, current spatial information systems do not provide suitable full support for the data's true 3D nature. Consequently, one system is needed to store the data and another for its processing, thereby necessitating format transformations. The work presented herein aims at a more cost-effective way for managing 3D LiDAR data that allows for storage and manipulation within a single system by enabling a new index within existing spatial database management technology. Implementation of an octree index for 3D LiDAR data atop Oracle Spatial 11g is presented, along with an evaluation showing up to an eight-fold improvement compared to the native Oracle R-tree index.
      2180Scopus© Citations 22