SHARON@SU-SCORE.ARPA@sri-unix.UUCP (10/04/83)
From: Sharon Bergman <SHARON@SU-SCORE.ARPA>
[Reprinted from the SU-SCORE bboard.]
Computer Science Department
Ph.D. Oral, Jim Davidson
October 18, 1983 at 2:30 p.m.
Rm. 303, Building 200
Interpreting Natural Language Database Updates
Although the problems of querying databases in natural language are well
understood, the performance of database updates via natural language introduces
additional difficulties. This talk discusses the problems encountered in
interpreting natural language updates, and describes an implemented system that
performs simple updates.
The difficulties associated with natural language updates result from the fact
that the user will naturally phrase requests with respect to his conception of
the domain, which may be a considerable simplification of the actual underlying
database structure. Updates that are meaningful and unambiguous from the
user's standpoint may not translate into reasonable changes to the underlying
database.
The PIQUE system (Program for Interpretation of Queries and Updates in English)
operates by maintaining a simple model of the user, and interpreting update
requests with respect to that model. For a given request, a limited set of
"candidate updates"--alternative ways of fulfilling the request--are
considered, and ranked according to a set of domain-independent heuristics that
reflect general properties of "reasonable" updates. The leading candidate may
be performed, or the highest ranking alternatives presented to the user for
selection. The resultant action may also include a warning to the user about
unanticipated side effects, or an explanation for the failure to fulfill a
request.
This talk describes the PIQUE system in detail, presents examples of its
operation, and discusses the effectiveness of the system with respect to
coverage, accuracy, efficiency, and portability. The range of behaviors
required for natural language update systems in general is discussed, and
implications of updates on the design of data models are briefly considered.SHARON@SU-SCORE.ARPA (11/08/83)
From: Sharon Bergman <SHARON@SU-SCORE.ARPA>
[Reprinted from the SU-SCORE bboard.]
Ph.D. Oral
COMPILING LOGIC SPECIFICATIONS FOR PROGRAMMING ENVIRONMENTS
November 16, 1983
2:30 p.m., Location to be announced
Stephen J. Westfold
A major problem in building large programming systems is in keeping track of
the numerous details concerning consistency relations between objects in the
domain of the system. The approach taken in this thesis is to encourage the
user to specify a system using very-high-level, well-factored logic
descriptions of the domain, and have the system compile these into efficient
procedures that automatically maintain the relations described. The approach
is demonstrated by using it in the programming environment of the CHI
Knowledge-based Programming system. Its uses include describing and
implementing the database manager, the dataflow analyzer, the project
management component and the system's compiler itself. It is particularly
convenient for developing knowledge representation schemes, for example for
such things as property inheritance and automatic maintenance of inverse
property links.
The problem description using logic assertions is treated as a program such as
in PROLOG except that there is a separation of the assertions that describe the
problem from assertions that describe how they are to be used. This
factorization allows the use of more general logical forms than Horn clauses as
well as encouraging the user to think separately about the problem and the
implementation. The use of logic assertions is specified at a level natural to
the user, describing implementation issues such as whether relations are stored
or computed, that some assertions should be used to compute a certain function,
that others should be treated as constraints to maintain the consistency of
several interdependent stored relations, and whether assertions should be used
at compile- or execution-time.
Compilation consists of using assertions to instantiate particular procedural
rule schemas, each one of which corresponds to a specialized deduction, and
then compiling the resulting rules to LISP. The rule language is a convenient
intermediate between the logic assertion language and the implementation
language in that it has both a logic interpretation and a well-defined
procedural interpretation. Most of the optimization is done at the logic
level.SHARON@SU-SCORE.ARPA (11/11/83)
From: Sharon Bergman <SHARON@SU-SCORE.ARPA>
[Reprinted from the SU-SCORE bboard.]
Ph.D. Oral
Tuesday, Nov. 15, 1983, 2:30 p.m.
Bldg. 170 (history corner), conference room
A DEDUCTIVE MODEL OF BELIEF
Kurt Konolige
Reasoning about knowledge and belief of computer and human agents is assuming
increasing importance in Artificial Intelligence systems in the areas of
natural language understanding, planning, and knowledge representation in
general. Current formal models of belief that form the basis for most of these
systems are derivatives of possible- world semantics for belief. However,,
this model suffers from epistemological and heuristic inadequacies.
Epistemologically, it assumes that agents know all the consequences of their
belief. This assumption is clearly inaccurate, because it doesn't take into
account resource limitations on an agent's reasoning ability. For example, if
an agent knows the rules of chess, it then follows in the possible- world model
that he knows whether white has a winning strategy or not. On the heuristic
side, proposed mechanical deduction procedures have been first-order
axiomatizations of the possible-world belief.
A more natural model of belief is a deductions model: an agent has a set of
initial beliefs about the world in some internal language, and a deduction
process for deriving some (but not necessarily all) logical consequences of
these beliefs. Within this model, it is possible to account for resource
limitations of an agent's deduction process; for example, one can model a
situation in which an agent knows the rules of chess but does not have the
computational resources to search the complete game tree before making a move.
This thesis is an investigation of Gentzen-type formalization of the deductive
model of belief. Several important original results are proven. Among these
are soundness and completeness theorems for a deductive belief logic; a
corespondence result that shows the possible- worlds model is a special case of
the deduction model; and a model analog ot Herbrand's Theorem for the belief
logic. Several other topics of knowledge and belief are explored in the thesis
from the viewpoint of the deduction model, including a theory of introspection
about self-beliefs, and a theory of circumscriptive ignorance, in which facts
an agent doesn't know are formalized by limiting or circumscribing the
information available to him. Here it is!SHARON@SU-SCORE.ARPA (02/17/84)
From: Sharon Bergman <SHARON@SU-SCORE.ARPA>
[Forwarded from the Stanford bboard by Laws@SRI-AI.]
PH.D. ORAL
USE OF ARTIFICIAL INTELLIGENCE
AND SIMPLE MATHEMATICS
TO ANALYZE A PHYSIOLOGICAL MODEL
JOHN C. KUNZ, STANFORD/INTELLIGENETICS
23 FEBRUARY 1984
MARGARET JACKS HALL, RM. 146, 2:30-3:30 PM
The objective of this research is to demonstrate a methodology for design
and use of a physiological model in a computer program that suggests medical
decisions. This methodology uses a physiological model based on first
principles and facts of physiology and anatomy. The model includes inference
rules for analysis of causal relations between physiological events. The model
is used to analyze physiological behavior, identify the effects of
abnormalities, identify appropriate therapies, and predict the results of
therapy. This methodology integrates heuristic knowledge traditionally used in
artificial intelligence programs with mathematical knowledge traditionally used
in mathematical modeling programs. A vocabulary for representing a
physiological model is proposed.