language: en

Assessing Responses to Situated Cognition

Tim Menzies
Dept. of Artifical Intelligence, School of Computer Science and Engineering, The University of New South Wales,
Sydney, Australia, 2052
timm@cse.unsw.edu.au; September 17, 1996

Abstract Situated cognition (SC) claims that knowledge is mostly context-dependent and that symbolic descriptions elicited prior to direct experience are less important than functional units developed via direct experience with the current problem. If this were true, then we would need to modify the knowledge modeling approaches of KA which assume that re-using old symbolic descriptions are a productivity tool for new applications. There are numerous tools which, if added to conventional knowledge modeling, could be said to handle SC (e.g. machine learning, abduction, verification & validation tools, repertory grids, certain frameworks for decision support systems, expert critiquing systems, and ripple-down-rules). However, we require an experiment to assess the effectiveness of these tools as a response to SC.

1 Introduction