also: Adjunct Associate Professor, Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia
An internationally leading researcher in Artificial Intelligence,
especially in constraint-based methods for the planning problem
and other combinatorial decision-making and reasoning problems,
with over 23 years of research experience in both fundamental and
applicative research in artificial intelligence.
The goal of Dr. Rintanen's research is automating complex decision-making and improving human performance in high-level cognitive tasks.
Applications of Dr Rintanen's research have been found in
discrete and hybrid systems control (planning), monitoring, and diagnosis,
in applications such as the Smart Grid (intelligent electricity networks)
and the construction and management of complex large-scale information
A main objective is to understand, automatically, what happens in
a complex system (monitoring, diagnosis, state estimation),
what could happen (contingency analysis), and
what actions to take to avoid problems and to recover from fault situations
Dr Rintanen obtained his PhD degree in Computer Science from the Helsinki University
of Technology in 1997, held research and teaching positions at
the universities of Ulm and Freiburg between 1997 and 2005,
including obtaining the German professor level research and teaching
qualifications (Venia Legendi, Habilitation) at the University of
Freiburg in 2005, and
worked in Australia from January 2006 until September 2012, as a Principal
Researcher at National ICT Australia until March 2011 in various roles
as a deputy program leader, project leader of multiple projects,
and the leader of the Planning and Diagnosis group, and
as an Adjunct Associate Professor at the Australian National University
(Canberra) and at Griffith University (Brisbane).
Further details in CV.
Office hours for Spring 2016: Tuesday and Thursday 9:00-10:30
Internationally recognized expert on Artificial Intelligence, with
leading expertise in
Factored search methods for state-space search problems, especially
based on model-finding and logic-based data structures.
Application of combinatorial methods to
diagnosis, decision making,
probabilistic reasoning and learning,
synthesis of plans, controllers and policies
Representation of high-level system specifications, as needed for
high-level synthesis of complex knowledge-intensive systems.
Supervisory control of discrete and hybrid systems, such as electricity
networks and other utility infrastructure.
I have several topics for M.Sc. and PhD thesis (but currently no funding
to hire anyone.) If you have your own funding (or you are already
at Aalto and doing some other degree than PhD),
please contact to discuss topics.
Some sample topics are listed below, but I am also interested in discussing
Automated software synthesis
The project is investigating the synthesis of software systems from high-level
formal specifications, targeting the development and management of large knowledge-intensive information systems.
Several thesis topics are available: knowledge representation for software synthesis, workflow and process synthesis, event processing and event recognition, architectures for intelligent software systems.
Proof complexity of state-space reachability problems
Reachability problems are one of the leading application of modern algorithms
for the propositional satisfiability problem (SAT). The project investigates
the properties of proofs of unreachability in order to establish connections
between SAT-based and other search methods for reachability problems, including
Binary Decision Diagrams and other symbolic data structures.
Resolution proofs systems in SAT solvers
The project investigates the functioning of SAT solvers from the point of view
of the resolution derivations implicitly generated during the solver's search.
The properties of the proofs are investigated as well as the possibilities of
changing standard SAT solver search strategies to obtain proofs more efficiently.
Domain-specific control of the Conflict-Driven Clause Learning algorithm
CDCL is the leading systematic algorithm for solving the propositional satisfiability
problem (SAT), a basis of solving many hard problems in Computer Science. The project
investigates the implementation of domain-specific decision rules for the most
important applications of SAT.
scheduling for close-to-optimal snow-plowing
The goal of the project is to produce a scheduler for finding good quality
routes for fleets of snow-plowing equipment.
First, a full-information scheduler is developed.
Later, the scheduler is extended to handle uncertainty for example
with respect to completion times and forecasts for snowfall.
Weighted model-counting and #SAT
Algorithms for the model-counting problem are investigated, in connection
with important applications from probabilistic reasoning.