- Artificial Intelligence
- Machine Learning and Data Mining, especially for Streaming Big Data
- Knowledge Representation
- Rational Agents
- Planning and Non-standard Logics
- Data-driven fault detection methods
I have first became interested in Artificial Intelligence and especially Machine Learning during my Master studies, because it appeared to be the ultimate solution to all the computational problems. Since then I have spent most of my career trying to understand the actual limitations of those methods. During my PhD studies I have primarily focused on rich knowledge representations and connection between learning and non-standard logics. More recently I am focusing on self-organising and unsupervised approaches. My main interests lie in the data-driven modelling and knowledge representation.
Coming to IS-lab at Halmstad University, I have joined a research track that focuses on knowledge discovery in large and distributed data streams, including unsupervised modelling. An example is the case of heavy-duty vehicle diagnostics, where ground truth is sparse or even unavailable. The goal is to develop new, or adapt existing, Machine Learning and Data Mining techniques to make them suitable for handling massive streams of data in a distributed and self-organising manner. In the spirit of "aware systems" that form the foundation of CAISR environment, my research concerns systems that can be human-, situation- and self-aware.
One way to achieve this goal is through discovery of interesting patterns and relations, where the "interestingness" can be treated as a metric and quantitatively measured. In this respect we are evaluating of both the data and the extracted knowledge. In many applications it is not feasible to store all the data, and therefore a preliminary decision needs to be made as to what are the most useful subsets to use in further analysis. We aim for interestingness metrics that are suitable for evaluating partial results in distributed environments. An important feature, however, is that they should be adaptable to different tasks and domains, as well as work for both supervised and unsupervised learning.
Specifically on the topic of self-organisation and self-awareness, we have had successes in creating solutions that work well for specific application domains. The next step I am interested in is to obtain deeper understanding of fundamental concepts, allowing us to build a general theory on top of those successful application examples.
I am also interested in guiding learning process using (both structured and semi-structured) expert and historic knowledge. In particular, this can be done before the learning starts, but also later, as a way to evaluate results and have the user guide the process in an interactive way. I am working towards designing knowledge representation models that allow for efficient learning, while being flexible enough to capture different aspects of the data simultaneously.
It is evident from the exponential development in e.g. sensors, embedded computers, computing power and memory capacity: all those artifacts are becoming less and less self-centered and their value depends on their ability to fit their environment. We today see the result of this in, e.g., the automotive industry where modern cars are more complex than what the average repair-person can handle (and the "competence" gap is growing). The industry is therefore investing in automatic fault detection and diagnostics, where devices "know" more and more about themselves and their normal operation, providing aids for the maintenance and repair personnel. The same development is occurring in the health care sector, i.e. the technological equipment and the possibilities are more complex than the average care-giver can manage. It is of vital important to have systems that are "aware" and easy to manage.
Overall, I am interested in Artificial Intelligence because I want to make computers do things which are "easy", in addition to those that are difficult: to not only play chess, also soccer. I believe that the way to achieve this are interdisciplinary approaches. At the moment a lot of incredible AI solutions are being created, but there is not enough effort to combine them into a truly intelligent, complete systems. I am convinced that all truly reliable systems must be able to learn from experience, and thus I consider Machine Learning techniques to be necessary. However, they need to move away from some of the very strong underlying assumptions that work in the lab but are not possible to meet in real world. Finally, I am convinced that focus needs to be put on resource-bounded agents and systems. Idealised models such as omniscience are useful to understand basic principles, but we are now ready to move towards more realistic settings.
I am currently (co-)supervising eight PhD students: Elham Pirnia (industrial PhD student employed at Volvo Technology), Iulian Carpatorea, Yuantao Fan, Hassan Nemati, Pablo Del Moral, Ece Calikus, Awais Ashfaq and Shiraz Farouq.
Journal publications registered in DiVA
Mohamed-Rafik Bouguelia, Sławomir Nowaczyk, K. C. Santosh, Antanas Verikas (2017). Agreeing to disagree : active learning with noisy labels without crowdsourcing. International Journal of Machine Learning and Cybernetics.
Yuantao Fan, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson (2015). Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. Frontiers in Artificial Intelligence and Applications. 278, pp. 58-67
Rune Prytz, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Stefan Byttner (2015). Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Engineering applications of artificial intelligence. 41, pp. 139-150
Thorsteinn Rögnvaldsson, Stefan Byttner, Rune Prytz, Sławomir Nowaczyk, Magnus Svensson (2015). Wisdom of Crowds for Intelligent Monitoring of Vehicle Fleets.
Yuantao Fan, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson (2015). Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet. Procedia Computer Science. 53, pp. 447-456
Jerzy Stefanowski, Sławomir Nowaczyk (2007). An Experimental Study of Using Rule Induction Algorithm in Combiner Multiple Classifier. International Journal of Computational Intelligence Research. 3(4), pp. 335-342
Conference publications registered in DiVA
Iulian Carpatorea, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Johan Lodin (2017). Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers. Intelligent Systems Conference (IntelliSys 2017), London, United Kingdom, 7-8 September, 2017.
Yuantao Fan, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Eric Aislan Antonelo (2016). Predicting Air Compressor Failures with Echo State Networks. Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016.
Tove Helldin, Maria Riveiro, Sepideh Pashami, Göran Falkman, Stefan Byttner, Sławomir Nowaczyk (2016). Supporting Analytical Reasoning : A Study from the Automotive Industry. 18th International Conference, HCI International 2016, Toronto, Canada, July 17-22, 2016.
Xudong Teng, Yuantao Fan, Sławomir Nowaczyk (2016). Evaluation of Micro-flaws in Metallic Material Based on A Self-Organized Data-driven Approach. 2016 IEEE International Conference on Prognostics and Health Management, Carleton University, Ottawa, ON, Canada, June 20-22, 2016.
Hassan Mashad Nemati, Anita Sant'Anna, Sławomir Nowaczyk (2016). Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids. 2016 IEEE International Energy Conference (ENERGYCON), 4-8 April, Leuven, Belgium, 4-8 april, 2016.
Iulian Carpatorea, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Marcus Elmer, Johan Lodin (2016). Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data. IEEE 15th International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 18-20 December, 2016.
Hassan Mashad Nemati, Anita Sant'Anna, Sławomir Nowaczyk (2015). Reliability Evaluation of Underground Power Cables with Probabilistic Models. The 11th International Conference on Data Mining (DMIN'15), Las Vegas, Nevada, USA, July 27-30, 2015.
Yuantao Fan, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson (2014). Using Histograms to Find Compressor Deviations in Bus Fleet Data. The Swedish AI Society (SAIS) Workshop 2014, Stockholm, Sweden, May 22-23, 2014.
Iulian Carpatorea, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Marcus Elmer (2014). Towards Data Driven Method for Quantifying Performance of Truck Drivers. 28th Annual workshop of the Swedish Artificial Intelligence Society (SAIS), Stockholm, Sweden, May 22-23, 2014.
Hassan Mashad Nemati, Anita Sant'Anna, Sławomir Nowaczyk (2014). Overview of Smart Grid Challenges in Sweden. 28th annual workshop of the Swedish Artificial Intelligence Society (SAIS), Stockholm, Sweden, May 22-23, 2014.
Iulian Carpatorea, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Marcus Elmer (2014). APPES Maps as Tools for Quantifying Performance of Truck Drivers. The 10th International Conference on Data Mining, DMIN´14, July 21-24, Las Vegas, Nevada, USA.
Sławomir Nowaczyk, Rune Prytz, Thorsteinn Rögnvaldsson, Stefan Byttner (2013). Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data. 12th Scandinavian Conference on Artificial Intelligence, Aalborg, Denmark, November 20–22, 2013.
Rune Prytz, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Stefan Byttner (2013). Analysis of Truck Compressor Failures Based on Logged Vehicle Data. 9th International Conference on Data Mining, Las Vegas, Nevada, USA, July 22–25, 2013.
Stefan Byttner, Sławomir Nowaczyk, Rune Prytz, Thorsteinn Rögnvaldsson (2013). A field test with self-organized modeling for knowledge discovery in a fleet of city buses. 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013, Takamastu, Japan, 4-7 August, 2013.
Sławomir Nowaczyk, Stefan Byttner, Rune Prytz (2012). Ideas for Fault Detection Using Relation Discovery. The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS), 14–15 May 2012, Örebro, Sweden.
Mathias Haage, Jacek Malec, Anders Nilsson, Klas Nilsson, Sławomir Nowaczyk (2011). Declarative Knowledge-Based Reconfiguration of Automation Systems Using a Blackboard Architecture. 11th Scandinavian Conference on Artificial Intelligence, Trondheim, Norway, May 24-26.
Sławomir Nowaczyk (2009). A Knowledge-Based Approach to Modelling Planning Domains with Uncertainty for Resource Bounded Agents. 7th Conference on Computer Methods and Systems, Krakow, Poland, 26-27 November.
Sławomir Nowaczyk, Jacek Malec (2007). Learning to Evaluate Conditional Partial Plans. Planning to Learn Workshop, The 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) (ECML/PKDD 2007), September 17-21, 2007, Warsaw, Poland.
Jacek Malec, Anders Nilsson, Klas Nilsson, Sławomir Nowaczyk (2007). Knowledge-Based Reconfiguration of Automation Systems. 3rd IEEE International Conference on Automation Science and Engineering (IEEE CASE), Scottsdale, AZ, SEP 22-25, 2007.
Ola Angelsmark, Jacek Malec, Klas Nilsson, Sławomir Nowaczyk, Leonardo Prosperi (2007). Knowledge Representation for Reconfigurable Automation Systems. International Conference on Robotics and Automation (ICRA-07), Workshop on Semantic Information in Robotics, Rome, Italy, 10 April, 2007.
Sławomir Nowaczyk, Cristina Isvoranu, Saioa Zorita, Thaer Barri, Florentina Trif (2007). Mind the Gap! Bridging the Gap between Theory and Practice in Laboratory Assignments. Utvecklingskonferens LU, 27 september.
Sławomir Nowaczyk, Jacek Malec (2007). Relative relevance of subsets of agent's knowledge. Multi-Agent Logics, Languages and Organisations – Federated Workshops (MALLOW'007), Durham, UK, 3-7 September.
Sławomir Nowaczyk (2007). Knowledge Representation for Learning How to Evaluate Partial Plans. 24th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS-07), Borås, May 22-23.
Sławomir Nowaczyk, Jacek Malec (2007). An Architecture for Resource Bounded Agents. International Multiconference on Computer Science and Information Technology (IMCSIT’07), Wisła, Poland, October 15–17, 2007.
Sławomir Nowaczyk, Jacek Malec (2007). Inductive logic programming algorithm for estimating quality of partial plans. 6th Mexican International Conference on Artificial Intelligence, Aguascalientes, Mexico, November 4-10, 2007.
Sławomir Nowaczyk (2006). Partial planning for situated agents based on active logic. Workshop on Logics for Resource Bounded Agents, 18th European Summer School in Logic Language and Information (ESSLLI 2006), University of Málaga, Málaga, Spain, 31 July-11 August.
Sławomir Nowaczyk (2006). Learning and Planning of Situated Resource Bounded Agents. The 23rd Annual Workshop of the Swedish Artificial Intelligence Society Workshop, Umeå, Sweden, May 10-12.
Sławomir Nowaczyk (2006). Learning of agents with limited resources. 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06;Boston, MA, USA 16-20 July.
Jacek Malec, Sławomir Nowaczyk (2005). Deduction and Exploratory Assessment of Partial Plans. Workshop on Planning and Learning in A Priori Unknown or Dynamic Domains (IJCAI-05), Edinburgh, United Kingdom, 30 July - 5 Aug 2005.
Jerzy Stefanowski, Sławomir Nowaczyk (2005). On using rule induction in multiple classifiers with a combiner aggregation strategy. 5th International Conference on Intelligent Systems Design and Applications 2005, ISDA '05, 8-10 September 2005, Wroclaw, Poland.