KSL Seminar Schedule - Fall Quarter 96
- Location: Gates Building, Room 100
- Time: Mondays, 12:30-2:00
- Lunch Provided
Stanford students, faculty, and staff are invited to a weekly series
of presentations and discussions of
current research being conducted in the Computer Science Department's
Knowledge Systems
Laboratory (KSL). KSL conducts research in Artificial
Intelligence with an emphasis on the areas of knowledge representation
and reasoning. Seminar presentations will be made by KSL faculty,
research associates, and Ph.D. students describing their research on
network-based information brokers, ontology development and use,
model-based support of distributed collaborative engineering,
function-based product identification, adaptive intelligent systems,
and virtual theaters. KSL's home page is http://ksl-web.stanford.edu/.
KSL Seminar Schedule
Seminars will be held Mondays, 12:30-2:00 in Gates Building, Room
100. Lunch will be provided!
Abstracts and Supporting Information
Speaker: Prof. Richard Fikes
Abstract
In recent years, the AI community has been evolving a notion of ontologies as artifacts that play significant roles in knowledge representation and reasoning. Even though ontologies are generally considered to provide definitions for the vocabulary used to represent knowledge, there is no agreement on precisely what an ontology is. So, we begin by proposing a formal definition of an ontology as an integral component of the language in which knowledge is represented and explore the role ontologies play in collaboration, interoperation, education, and domain modeling. We then describe some of the novel features of KSL's Ontolingua Web-based ontology development system that support collaborative development of ontologies by assembling and extending reusable ontologies from an on-line library. We conclude by surveying current research issues and challenges related to enabling the effective creation and use of ontologies.
Speaker: Barbara Hayes-Roth
Abstract
Improvisational performers create engaging vignettes in real time,
without detailed planning, and often working within constraints
provided by the audience. My research group is exploring the
possibility of creating intelligent computer agents that can be
embodied as animated characters, can perform in a manner loosely
resembling that of human improvisors, and can tailor their
performances to abstract directions offered by users or other system
components. I'll talk a little bit about functional requirements for
improvisational characters, how these differ from the requirements set
for other sorts of intelligent agents, and how they resemble
requirements for everyday human behavior. I'll describe our general
approach to meeting the requirements and show videotapes of three
implemented systems.
Speaker: Adam Farquhar
Abstract
This seminar will be very much discussing work-in-progress. It follows from a
series of discussions with Angela Dappert and Joachim Hammer.
Integration of heterogeneous information systems has become a problem of growing
importance. The integration problem can be broadly divided into syntax,
protocol, and semantics. As the infrastructure to move bits from place to place
and program to program becomes robust, reliable, and ubiquitous, solving the
semantic issues becomes increasingly critical.
In this seminar, I will focus on the semantic issues that arise in integration,
ignoring issues that arise at run-time such as query planning and execution,
optimization, and tractability. I will present an architecture for performing
semantic integration that employs a library of reusable ontologies to ease the
integration process and articulate some of the consequences of this
architecture.
Our work employs Context Logic, developed by McCarthy and Buvac, to define
mappings between different representations. I will briefly describe this logic
and show how it can be implemented by a standard theorem prover. Time allowing,
we will look at some examples using context logic to reuse ontologies, and
perform data-model conversions between contexts.
Speaker: Yumi Iwasaki
Abstract
The How Things Work project in progress at the Knowledge Systems, AI Laboratory aims
to develop knowledge-based technology to support designers of electro-mechanical
devices in a collaborative engineering environment. In particular, we are
developing the Collaborative Device Modeling Environment (CDME), a system that
provides tools for distributed collaborative development, testing, and
maintenance of engineering ontologies, models, and specifications. An important
component of CDME is a facility for automatically formulating a model of a
design that embodies the abstractions, approximations, assumptions, and
perspectives that are appropriate for a given analysis task. We have developed
an efficient algorithm that formulates a simplest, appropriate model for
tracking the values and causal influences on a given set of variables during a
behavior simulation.
In this talk, I will discuss the objectives of the project, describe the work on
model formulation, and present some experimental results on model formulation.
Speaker: Angela Dappert
Abstract
My work is embedded in the information broker project at KSL. The purpose of
the project is to find solutions to semantic issues of integrating heterogeneous
information sources for global query
mediation.
In an information broker environment, one has to expect semantic mismatches
between the queries asked of the information broker and the heterogeneous
information sources which are used for query answering. Sometimes this
mismatched information still can be used to create an answer that will give an
important lead to the user of the system even if an exact answer cannot be
given. The system should be able to
answer queries partially - as long as the missing query parts are relatively
unimportant or as long as complete query coverage is very important to the
application.
We identify three semantic mismatch categories:
- Domain Mismatch: Discrepancy between the expected domain coverage of objects
for the query and the actual domain coverage of the information sources;
- Property Mismatch: Discrepancy between object properties that make up the
query and properties that are used within the information sources to describe
objects of interest;
- Intentional Mismatch: Discrepancy between the information available from
information sources and the information accessible via deductive reasoning.
In order to address these problems we suggest a cooperative approach with the
following properties:
- The cooperative system returns a three valued answer space. It guarantees to
return all the true answers available (TRUE set). In addition, it attempts to
give a complete, lowest upper bound for
objects that cannot be concluded to not satisfy the query (MAYBE set). If this
is not possible, it should give intentional and extensional descriptions of the
object set that can be concluded to fail to satisfy the query (FALSE set).
- It explains why the objects in the MAYBE set could not be determined to be
true or false and what additional information would be needed for each one of
them to make a definite decision. This explains the limitations of the
available information sources.
We will propose the techniques of partial query answering,
approximated query answering, and integrated query answering to
achieve this goal.
Speaker: Todd Neller
Abstract
In this talk, we'll look at the current research in verification of hybrid
systems, the (ir)relevance of such research to a specific verification problem,
and consider the advantages/disadvantages of different approaches for that
special case. "Hybrid systems" are systems manifesting behavior with both
discrete and continuous change (e.g. a discrete controller affecting continuous
physics). We consider the problem of verifying safety against stalling for a
stepper motor driven by a fixed acceleration strategy without
feedback. Thus we must consider open-loop control and nonlinear dynamics in the
face of uncertain system parameters with bounded error. We discuss the
relevance of tools and approaches to this verification problem, present
promising approaches, and conclude with an open discussion of the involved
tradeoffs.
Speaker: Karl Pfleger
Abstract
In this talk I'll discuss the ideas surrounding my dissertation. I won't
present results. Neither will I discuss detailed algorithmic mechanisms.
Instead, I will concentrate on describing what I intend to compute and how it
fits in with AI, machine learning, etc. Plus, I'll throw in lots of
motivations. Specifically, I'll present a number of different views each of
which is a different simple way to think about my thesis:
Hierarchical compositional structure constitutes one important form of
abstraction. In compositional hierarchies, high level entities represent
aggregations of lower level entities. This type of structure is ubiquitous in
the real world and in the types of information people and computers encounter
every day.
This dissertation examines methods for discovering hierarchical compositional
structure and exploiting it in useful ways. Specifically, this structure can be
uncovered from the bottom up through repeated composition, or chunking, of lower
level entities, beginning with atomic level primitives. We concentrate only on
the most general such mechanisms, those that make use only of the raw, atomic
level data presented to the system, rather than any separate domain theory or
extra task or goal specific information. Aggregation can be performed using
only this information simply by chunking frequently occurring combinations of
primitives or previous chunks. Once learned, this structure can be used to
predict future observations, filter noise, fill in missing or ambiguous entries
from context, compress data, or detect anomalies or errors and suggest
corrections. Applications based on these uses are virtually limitless. The
chunks themselves serve as high level abstractions useful for explanation,
communication, memory, and reasoning in general.
Speaker: Sheila McIlraith
Abstract
In recent years, a number of researchers have argued that diagnostic problem
solving is purposive in nature, that in some instances, identifying candidate
diagnoses is only relevant to the extent that it enables an agent to act --- to
execute a test, to repair a system, to control it, to invoke a contingency plan,
or perhaps to perform preventative maintenance. From this viewpoint, we claim
that a comprehensive account of diagnostic problem solving must involve
reasoning about action and change.
In this work we examine an important set of representation issues that have not
been addressed by the model-based diagnosis community. In particular, we
examine the problem of integrating a system
description, SD, with a theory of action and change, to parsimoniously
represent the effect of actions on a system and the effects of a system on
actions in the world. We employ the situation calculus, a first-order language,
as our representation language for action and change. In the context of the
situation calculus, SD presents an often complex set of state constraints.
These state constraints implicitly define indirect effects of actions as well as
indirectly imposing further preconditions on the performance of an action. As a
consequence, SD poses further complications to addressing the frame,
ramification and qualification problems.
In addressing these problems, we examine a syntactically restricted SD, which
commonly occurs in the axiomatization of model-based diagnosis domains. The
contributions of this work are as follows:
- a framework for integrating SD and a theory of action and change.
- a procedure for compiling SD into a set of successor state axioms. These
axioms captures the intended interpretation of SD, while providing a
closed-form solution to the frame and ramification problems.
- a circumscriptive specification of a solution to the frame and ramification
problems which provides formal justification for our procedure.
This talk will focus on items 1 and 2.
afarquhar@ksl.stanford.edu
Last modified: Wed Nov 27 12:31:46 PST 1996