5 found
Order:
  1. The Token Reification Approach to Temporal Reasoning.Lluís Vila & Han Reichgelt - 1996 - Artificial Intelligence 83 (1):59-74.
  2.  1
    Logic-Based Knowledge Representation.Peter Jackson, Han Reichgelt & Frank Van Harmelen - 1989 - Mit Press.
    This book explores the building of expert systems using logic for knowledge representation and meta-level inference for control. It presents research done by members of the expert systems group of the Department of Artificial Intelligence in Edinburgh, often in collaboration with others, based on two hypotheses: that logic is a suitable knowledge representation language, and that an explicit representation of the control regime of the theorem prover has many advantages. The editors introduce these hypotheses and present the arguments in their (...)
    Direct download  
     
    Export citation  
     
    Bookmark  
  3.  55
    Mental Models and Discourse.Han Reichgelt - 1982 - Journal of Semantics 1 (3-4):371-386.
    In this paper I take the view that using language amounts to constructing ‘mental models’. Accordingly, semantics has to explain the structure of these mental models and the principles by which people construct them. The system proposed, which was developed jointly with Nigel Shadbolt, is called S-R Semantics. Among the fundamental features of the system is a functional distinction drawn between two sorts of mental object: epistemic objects, which are supposed to model the long-term established knowledge a processor brings to (...)
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark  
  4.  32
    D. Peterson, Ed., Forms of Representation: An Interdisciplinary Theme for Cognitive Science. [REVIEW]Han Reichgelt - 2000 - Minds and Machines 10 (3):455-457.
  5.  16
    Avoiding Omnidoxasticity in Logics of Belief: A Reply to MacPherson.Kieron O'Hara, Han Reichgelt & Nigel Shadbolt - 1995 - Notre Dame Journal of Formal Logic 36 (3):475-495.
    In recent work MacPherson argues that the standard method of modeling belief logically, as a necessity operator in a modal logic, is doomed to fail. The problem with normal modal logics as logics of belief is that they treat believers as "ideal" in unrealistic ways (i.e., as omnidoxastic); however, similar problems re-emerge for candidate non-normal logics. The authors argue that logics used to model belief in artificial intelligence (AI) are also flawed in this way. But for AI systems, omnidoxasticity is (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark