dc.contributor.author | O'Keefe, Richard A. | en |
dc.date.accessioned | 2018-01-31T11:23:36Z | |
dc.date.available | 2018-01-31T11:23:36Z | |
dc.date.issued | 1987 | |
dc.identifier.uri | http://hdl.handle.net/1842/26819 | |
dc.description.abstract | | en |
dc.description.abstract | The work partially reported here concerned the development ot a prototype Expert System for
giving advice about Statistics experiments, called ASA, and an inference engine to support
ASA, called ABASE. | en |
dc.description.abstract | This involved discovering what knowledge was necessary for performing the task at a satis¬
factory level of competence, working out how to represent this knowledge in a computer, and
how to process the representations efficiently. | en |
dc.description.abstract | Two areas of Statistical knowledge are described in detail: the classification of measure¬
ments and statistical variables, and the structure of elementary statistical experiments. A
knowledge representation system based on lattices is proposed, and it is shown that such
representations are learnable by computer programs, and lend themselves to particularly
efficient implementation. | en |
dc.description.abstract | ABASE was influenced by MBASE, the inference engine of MECHO [Bundy et al 79a]. Both
are theorem provers working on typed function-free Horn clauses, with controlled creation of
new entities. Their type systems and proof procedures are radically different, though, and
ABASE is "conversational" while MBASE is not. | en |
dc.publisher | The University of Edinburgh | en |
dc.relation.ispartof | Annexe Thesis Digitisation Project 2017 Block 15 | en |
dc.relation.isreferencedby | | en |
dc.title | Logic and lattices for a statistics advisor | en |
dc.type | Thesis or Dissertation | en |
dc.type.qualificationlevel | Doctoral | en |
dc.type.qualificationname | PhD Doctor of Philosophy | en |