8/9/2019 recenzie.volIII
1/3
Artificial Intelligence in Accounting and Auditing:
Knowledge Representation, Accounting Applications and the
Future, vol. III
by Miklos A. asarhelyi, Markus !iener "ublishers, #ew $ersey,
%&&'
I()# %* digit: %+ - /+* -+'0 I()# %1 digit: & -+%+ - /+*
-+1
Miklos A. asarhelyi is the editor of the artificial intelligence
in accounting. 2e is
professor of accounting at the 3raduate (chool of Manage4ent at
Rutgers 5niversity. and
auditing.
6his volu4e focuses on the areas of accounting applications,
knowledge representation
and the future of the field and is divided into five parts: the
first enco4passes two surveys of the
literature that position the current state+of+the+art of the
field. 6he second section looks at
Knowledge 7licitation and Representation through two articles
that use the analysis of verbal
protocols applied to the financial area. 78pert syste4s rely on
for4s of knowledge representation
for inference..
6he third section of this book introduces an e4erging paradig4
of audit research that has
great i4port for the ne8t generations of applications of
Artificial Intelligence and 78pert
(yste4s. !hile early e8pert syste4s focused on probability+based
rules and their use in
inference and e8pert knowledge representation, 4ore recent
develop4ents in the field accept the
fact that these have to coe8ist with deter4inistic situations,
operations research 4odels and theactual set of rigid rules of the
e4pirical relational syste4.
6he fourth section introduces two papers that further e4phasi9e
the accounting side of
these applications. Mc arthy, Rockwell, and !allingford present
an idea paper that relates
events+based accounting to decision support syste4s
6he fifth and last section of the book looks at the future
through three future+oriented
papers. 6he second article in this section, by Mock and 3ray,
looks at the future i4pact of
78pert (yste4s on Auditing. Mock and 3ray present a scenario for
the future of auditing, with particular e4phasis on audit
planning.
In conclusion, syste4s have been abandoned in practice, as they
did not resolve the real
proble4 of practice, under certain conditions presented absurd
results, were too threatening to
the users, re;uired too large of a continuing invest4ent for its
full deploy4ent, had an
inade;uate knowledge base, the inferencing 4echanis4s were
inade;uate, etc.
