Broadly my project interests can be divided in the areas of artificial intelligence, logic-related topics, and economics. Furthermore I have a particular interest in software tools, e.g., tools for manipulating/administrating digital pictures. Research wise I am interested in reasoning , heuristic search, and economic models.
I currently do not supervise MSc summer projects.
Software
1. Text administration in the large
AI related projects
2. Modelling of a Simple Eco-System
3. From Planning to Automated Programming
4. Emotion-Driven Language Generation
Reasoning and Logic Related Projects
7. A Model Generator for First-Order Logic
9. Modelling Fallacies of Human Reasoning
Economics
10. Computing solutions in pillage games
Course Suitability: Any
Brief Description: When you write a big piece of text then it is
important to keep track of the different decisions. This applies for
scientific texts such as a final year project just as for a novel. For
instance, if you introduce a particular notation (or a person in a
novel with a particular name) then you do that chronologically before
you refer to it. If you later decide to change the notation (or the
name) then you have to do it consistently. Likewise there are many
other aspects of the inner logic of a text, which must be observed
since otherwise the text will be inconsistent. For small texts this is
easy to achieve, for large ones not. The task is to write a system
that supports the management of consistency in texts.
Languages and/or other Software: any, XML
Prerequisite:
None
Course Suitability: Any
Brief Description: Many systems show stability only when the rules of
the game are well-balanced. For instance, a carnivore species may be
so successful that they eat up all their prey and then starve. In this
project a simulated environment (may be ecological or economical)
should be built and tests should be performed to see under which
conditions the environment is stable.
Languages and/or other Software: Any, although investigation in the right type of language may pay off
Prerequisite:
none
Course Suitability: Knowledge in AI
Brief Description: Planning has been studied in AI since the
1970's. There are also approaches to extend it to automated program
construction. The idea is to use planning techniques and to generate
a program which satisfies a Hoare tripe {A}P{B}. The idea in this
project would be to use planning techniques and generate simple
programs which satisfy the specification. Unlike other approaches the
program has to be guaranteed to do what it is supposed to do.)
Languages and/or other Software: e.g., Lisp, Prolog, ...
Prerequisite:
none
Course Suitability: Knowledge in AI
Brief Description: There are different ways to say the same thing. Whether we are in good temper or bad temper, happy or angry can make a big difference how we phrase a request, for instance. In this project these differences shall be investigated and a system shall be implemented, which can generate speech acts which depend on the mood of an agent.
Languages and/or other Software: an AI language (Lisp, Prolog, or Pop11
Prerequisite:
Artificial Intelligence, Natural Language Processing
Course Suitability: Knowledge in machine learning useful
Brief Description: The idea to this project is initiated by a talk in
the departmental seminar by Edmund Furse, there is a corresponding
paper with the title "A model of imitation learning of algorithms
from worked examples". The main idea is to present to a computer a
couple of examples and the computer is then able to generalise to a
general program. In this project, related ideas should be developed
and applied in a restricted application domain. A possible application
domain could be the generation of Emacs macros from examples. (The
editor Emacs has a macro-facility that allows to program a macro by
showing a single example. In all further examples the steps from the
first example are exactly taken over, in particular no generalisation
takes place. In this project the macro definition facility should be
extended so that it is possible to show different examples and the
macro created that way is a generalisation of all examples
presented.)
Languages and/or other Software: e.g., Pop11, Emacs Lisp
Prerequisite:
none
Literature: [Edmund Furse] A model of
imitation learning of algorithms from worked examples.
Course Suitability: AI
Brief Description: Many heuristics are subject to the so-called
no-free-lunch theorem, that is, they are good in some parts of the
search space but bad in others. Other heuristics are beneficial over
the whole class of all problems (of a particular kind). Typically
tests are difficult to perform since the class of all problems (of a
particular kind) may be very big (even infinite). The task is to come
up with a testing framework for heuristics, be they based on sampling
or testing the full class for small parameters.
Languages and/or other Software: Any, preferably an AI language
Prerequisite:
good knowledge in logic useful
Course Suitability: any, knowledge in logic required
Brief Description: Roughly speaking a model generator is a system that tries to generate
a finite model for a given set of first-order formulae. This is
essentially done by systematically searching in the space of possible
models, given by universes and interpretations. At first the system
tries whether there is a one-element universe, if not whether there is
a two-element one and so on. In order to be efficient in a huge search
space, choices have to be made in an intelligent way in order to
recognise dead ends as early as possible. Furthermore possibly
existing symmetries should be considered in order to minimise the
search effort. In this project a model generator is to be designed and
implemented.
Languages and/or other Software: Any
Prerequisite:
good knowledge in first-order logic
Literature: John Slaney.
FINDER: Finite domain enumerator-system description.
In Alan Bundy, ed., Proc. of the 12th CADE, Nancy 1994,
p. 798-801. Springer Verlag.
Course Suitability: any, knowledge in logic required
Brief Description: In this project should be tested whether proofs can be found by mining and composing them from existing proofs of related theorems. Typically humans learn how to find proofs by solving many related problems. Is it possible to do this on a shallow level of understanding and generate something which looks like a proof and then filter of all proof attempts those which are proofs? If you want to find out then this is the right project for you.
Prerequisite:
interest in web search, knowledge of first-order logic
Course Suitability: any, in particular AI
Brief Description: The development of classical logic was at least partially motivated by
the attempt to model aspects of human reasoning. However, classical
logic is not adequate to model human deduction (and is surely even
less appropriate for modelling other reasoning modes). One of the
problems is that in classical logic quite common and generally
accepted schemata of reasoning like modus ponens (from "A" and
"A implies B" it is possible to conclude "B") are
equivalent to less familiar one (like from "not B" and
"A implies B" it is possible to conclude "not
A"). However, applying the latter one causes much more
problems to human beings and an argument built up on the second form
is much likely to be faulty than one built up on modus ponens.
Such effects have been studied in detail by Braine et al. [Braine78], [Braine,Reiser,Rumain84] and Johnson-Laird, Byrne [Johnson-Laird,Byrne91].
In the proposed project the most recent literature has to be found and
studied, then it is aimed to develop from the cognitive model of the
reasoning process found there a prototype that can reproduce some of
the phenomena. In particular it would be interesting to investigate
the increase of error frequentness of human deduction under time
pressure.
Languages and/or other Software: CommonLisp, Pop11, or Prolog are particular well-suited
Prerequisite:
Some knowledge about classical logic is useful.
Literature: [Braine78]
Martin D.S. Braine.
On the relation between the natural logic of reasoning and standard logic.
Psychological Review, 85(1):1-21, 1978.
[Braine,Reiser,Rumain84]
Martin D.S. Braine, B.J. Reiser, and B. Rumain.
Some empirical justification for a theory of natural propositional logic.
Psychological Learning Motivation,
18(1):313-371, 1984.
[Johnson-Laird,Byrne 91] Philip Nicholas
Johnson-Laird and Ruth M.J. Byrne.
Deduction. Lawrence Erlbaum Associates Ltd., Hove, UK, 1991.
Course Suitability: any, good maths knowledge useful
Brief Description: Games are used in economics to describe
mathematically complicated interactions. Pillage games are a
particular cooperative game form in which agents can form coalitions and based on the power of a coalition take possessions from other coalitions.
In this project, a simulations for such a pillage game should be written. It should be expored to which degree the solution concept, the so-called stable set, can be computed for concrete situations.
Languages and/or other Software: Any programming language.
Prerequisite:
good knowledge of maths, interest in economics
Literature: Manfred Kerber, Colin Rowat.
Stable sets in three agent pillage games.
available online. June 2009.
