Selective Abstraction of Al System Activity

Jasmina Pavlin, Daniel D. Corkill

The need for presenting useful descriptions of problem solving activities has grown with the size and complexity of contemporary AI systems. Simply tracing and explaining the activities that led to a solution is no longer satisfactory. We describe a domain-independent approach for selectively abstracting the chronological history of problem solving activity (a system trace) based upon user-supplied abstraction goals. An important characteristic of our approach is that, given different abstraction goals, abstracted traces with significantly different emphases can be generated from the same original trace. Although we are not concerned here with the generation of an explanation from the abstracted trace, this approach is a useful step towards such an explanation facility.


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