Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
A framework for the comparative evaluation of knowledge acquisition tools and techniques
Knowledge Acquisition
Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
Borg: A Knowledge-Based System for Automatic Generation of Image Processing Programs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge engineering and management: the CommonKADS methodology
Knowledge engineering and management: the CommonKADS methodology
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Jess in Action: Java Rule-Based Systems
Jess in Action: Java Rule-Based Systems
Supporting knowledge-intensive inspection tasks with application ontologies
International Journal of Human-Computer Studies
Segmentation and description of natural outdoor scenes
Image and Vision Computing
Learning semantic object parts for object categorization
Image and Vision Computing
Ontology based complex object recognition
Image and Vision Computing
A cognitive vision approach to early pest detection in greenhouse crops
Computers and Electronics in Agriculture
Towards semantic maps for mobile robots
Robotics and Autonomous Systems
Computer Vision and Image Understanding
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In agriculture, a major challenge is to automate knowledge-intensive tasks. Task-performing software is often opaque, which has a negative impact on a system's adaptability and on the end user's understanding and trust of the system's operation. A more transparent, declarative way of specifying the expert knowledge required in such software is needed. We argue that a white-box approach is in principle preferred over systems in which the applied expertise is hidden in the system code. Internal transparency makes it easier to adapt the system to new conditions and to diagnose faulty behaviour. At the same time, explicitness comes at a price and is always bounded by practical considerations. Therefore we introduce the notion of bounded transparency, implying a balanced decision between transparency and opaqueness. The method proposed in this paper provides a set of pragmatic objectives and decision criteria to decide on each level of a task's decomposition whether more transparency is sensible or whether delegation to a black-box component is acceptable. We apply the proposed method in a real-world case study involving a computer vision application for seedling inspection in horticulture and show how bounded transparency is obtained. We conclude that the proposed method offers structure to the application designer in making substantiated implementation decisions.