

Algorithm
Invention and Verification
by Lazy Thinking
Some time ago, we introduced
the paradigm of
computersupported theory
exploration as an
alternative to the isolated
theorem proving paradigm
that prevailed in formal,
computersupported
mathematics for the past
decades and we proposed
various approaches to
systematic,
computersupported
mathematical theory
exploration, see for example
our paper at SYNASC 2001.
In this paper, we
study algorithm invention
and verification as a
specific variant of
sytematic theory exploration
and propose the "lazy
thinking paradigm" for
inventing and verifying
algorithms automatically;
i.e.,
for a given predicate
logic specification of the
problem in terms of a set of
operations (functions and
predicates), the method
produces an algorithm that
solves the problem together
with a correctness proof for
the algorithm.
In the ideal case,
the only information to be
provided by the user
consists of the formal
problem specification and a
complete knowledge base for
the operations that occur in
the problem specification.
Roughly, the method proceeds
as follows:
 The method tries out, one
after the other,
various algorithm
types that are stored in a
library of algorithm types
for the given domain. An
algorithm type is a
predicate logic formula that
describes an algorithm
(recursively) in terms of
unspecified subalgorithms
together with a proof method
appropriate for (recursive)
proofs of properties of the
algorithm.

For a chosen algorithm type,
the proof method is called
for the correctness theorem.
Typically, this proof will
fail because nothing is
known about the unspecified
subalgorithms.

From the failing proof
situation, by a conjecture
generating algorithm,
lemmata
are generated that
would enable the prover to
complete the proof
successfully. The lemmata
will describe certain
requirements on the
subalgorithms. These lemmata
are added to the knowledge
base and the proof
of
the correctness theorem is
attempted again. Now, the
proof will
get over the failing
situation and will either
succeed or will fail at some
later situation

This procedure is iterated
until the proof of the
correctness theorem goes
through (or some time limit
is exhausted).
After termination, the
following will be true:
Under the assumption that
all ingredient subalgorithms
satisfy the requirements
described in the lemmata
generated, the main
algorithm satisfies the
problem specification
In this stage, there are two
possibilities:

Either, in the
initial knowledge,
algorithms are available
that satisfy the
requirements for the
subalgorithms described in
the lemmata
and we are done, i.e.
a correct algorithm has been
synthesized for the initial
problem and its correctness
proof has been generated.

Or subalgorithms that
satisfy the requirements can
be synthesized by another
application of the method in
a next round of the
procedure.
We illustrate the method and
its implementation by case
studies carried out in our
Theorema system.
The
distinctive features of our
algorithm synthesis method,
as compared to other
methods, are:

the use of algorithm
type libraries

the crucial role of
failing proofs and
conjecture generation from
failing proofs

the decomposition of
theory exploration and, in
particular, algorithm
invention and verification
into layers

the naturalness of
the approach, which makes it
attractive both for complete
or partial automation in
systems for formal
mathematics and for usage as
a strategy for human
invention and teaching

Towards
Reusable and Shareable
Courseware: Topic Mapsbased
Digital Libraries
The effectiveness of
instructors and efficiency
of learners engaged in
openlearning and teaching
tasks in Webbased course
support environments depend
crucially on the support
they receive. Two main
groups of related problems
concern findability
of learning resources and
reusability and shareability
of digital educational
repositories. A
solution to these problems
is only viable if the
digital content of the
repositories is
standardsbased.
We address standardization
of elearning repositories not only from a technological perspective
but also from a knowledge
perspective. The latter
entails using
specialized domain
ontologies for
description and
classification of the
digital library content.
The
focus of my talk is on a
general framework that
facilitates
the building of ontologyaware
digital course libraries
intended to provide
trusted
reference information
coupled with support for
resource location and domain
comprehension. The
framework
utilizes the advantages of
conceptbased and
standardsbased content
organization.
The
new ISO standard, Topic
Maps, will be used as a
means to organize and
retrieve information in the
digital course libraries,
which will guarantee their
shareability, reusability,
mergeability, and
interoperability. The
research related to my talk is
aimed at developing
conceptbased,
ontologyaware courseware in
support of learning tasks in
Webassisted or independent
learning courses. Such
courseware implies
organizing learning content
around a wellstructured
network of domain concepts
and relationships (with
sound ontological
commitment) which permits
using that network for
classification, description,
and indexing of learning
resources.

Intelligent
Agents on the Web
Intelligent agents and
multiagent systems
represent a new paradigm of
distributed and
interconnected computation
of autonomous entities.
Nowadays, an increasing
number of software systems
are being viewed in terms of
autonomous agents in which
the software components act
more like
"individuals" or
agents, rather than just
"parts".
Intelligent agents offer
powerful mechanisms for
quickly modeling, creating
and deploying complex
applications that readily
adapt to real world needs.
The
objective of this tutorial
is to give an overview of
the existing agent and
multiagent systems
applications on the Web.
Three broad categories of
agents are covered: agents
for ecommerce, information
agents, and agents for
elearning. The tutorial
surveys several noteworthy
existing systems and
applications and, for some
of them, gives insight on
technicalities of those
systems.
The
tutorial is intended for
researchers and PhD students
who are interested in
finding more about specific
application domains of this
new computational paradigm,
but also for software
developers and managers who
are interested in the
application potential of an
emergent technology.

Constructing
Origami on the Internet
Providing a service on the
Internet is a next step
towards a knowledgebased
society.
We
would like to have services
of large scale numeric and
symbolic computing, theorem
proving, data mining etc.
offered by "knowledge
providers" on the
Internet.
In this talk I will
report our continued work on
distributed constraint
solving with the goal to
make contributions in
realizing such a
knowledgebased society. We
are developing, as an
application of our
constraint functional logic
programming system, a
webbased computer Origami
site, where interested
mathematicians, computer
scientists and Origami
artists visit and perform
computer origami
construction.
The computer origami
poses interesting research
and developments on
algorithms of symbolic
algebraic constraint solving
and computational geometry,
and on tools for serverside
systems programming. I will
discuss our findings in this
research and developments. I
also plan to give
demonstration of computer
origami. 
Model Checking: from Hardware to Software
In contrast to hardware which is finitestate and based on relatively few ample principles, software systems generally give rise to infinite state
spaces, and are described in terms of programming languages involving rich
semantical concepts. The challenges of software verification can be
addressed only by a combined effort of different communities including,
most notably, model checking, theorem proving, symbolic computation,
static analysis, compilers, and abstract interpretation. We focus on a
recent family of tools which use predicate abstraction and theorem proving
to extract a finite state system amenable to model checking.
A brief history of the
Krylov methods
Abstract: The talk covers two
main areas. The first is the
need to solve
linear simultaneous equations
and we give two simplified
examples of the
sort of sparse systems that
the methods are designed to
solve. The second is
a short description of the
principal differences between
typical iterative
methods and Krylov methods,
and a characterisation of the
Krylov methods.
The talk is aimed at the
nonmathematician and not
even knowledge of matrix
algebra is assumed.





Copyright (C) 20012003 Department of Computer Science, email: synasc03@info.uvt.ro
