126,540 research outputs found
Applying semantic web technologies to knowledge sharing in aerospace engineering
This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Behavior planning and decision-making are some of the biggest challenges for
highly automated systems. A fully automated vehicle (AV) is confronted with
numerous tactical and strategical choices. Most state-of-the-art AV platforms
implement tactical and strategical behavior generation using finite state
machines. However, these usually result in poor explainability, maintainability
and scalability. Research in robotics has raised many architectures to mitigate
these problems, most interestingly behavior-based systems and hybrid
derivatives. Inspired by these approaches, we propose a hierarchical
behavior-based architecture for tactical and strategical behavior generation in
automated driving. It is a generalizing and scalable decision-making framework,
utilizing modular behavior blocks to compose more complex behaviors in a
bottom-up approach. The system is capable of combining a variety of scenario-
and methodology-specific solutions, like POMDPs, RRT* or learning-based
behavior, into one understandable and traceable architecture. We extend the
hierarchical behavior-based arbitration concept to address scenarios where
multiple behavior options are applicable but have no clear priority against
each other. Then, we formulate the behavior generation stack for automated
driving in urban and highway environments, incorporating parking and emergency
behaviors as well. Finally, we illustrate our design in an explanatory
evaluation
The use of data-mining for the automatic formation of tactics
This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques
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