Applied Artificial Intelligence Group
Head of the research group: Development Officer VAHUR KOTKAS, firstname.lastname@example.org
KEYWORDS: machine learning, automated commonsense reasoning, ontology based reasoning, AI methods in spatial data analysis, recommender systems, logic-based software systems
Topics and Competences
The Applied AI Group conducts research in application of AI methods in various fields and systems. We investigate applicability of machine learning, ontology based reasoning, automated theorem provers, knowledge discovery and other AI methods for solving digitalisation problems of different industrial and governmental stakeholders.
Our previous research has been concentrated on building software development methods and tools (e.g. CoCoViLa) with AI components, basically with program synthesis and ontology based knowledge representation components.
During a number of decades several software tools that facilitate AI techniques have been developed by the group. The following is a list of tools that are still in use or under deveopment:
- CoCoViLa – visual model-based software develpment environment - http://cocovila.github.io/
- WhiteDB – a lightweight NoSQL database library - http://whitedb.org/
- GKC – discussion tool on large knowledgebases – https://github.com/tammet/gkc
Currently we work on topics like application of AI methods in spatial data analysis, using machine learning for risk management in e-commerce and for public service delivery. The corresponding projects are listed as follows:
- Applied research for creating a cost-effective interchangeable 3D spatial data infrastructure with survey-grade accuracy
- Applied research for e-commerce EU VAT and duty declaration (as from 2021) digitalisation
- Machine learning and AI powered public service delivery
Research results 2019
- Logical-based discussion methods used in large knowledge bases have been developed. The goal of ongoing experimental research and development of a commonsense reasoning system is to build a world leading hybrid (machine learning plus logical reasoning) commonsense reasoner, to be used as a commonsense reasoning tool in various AI toolchains.
- A novel method has beed developed for estimating short-term energy-consumption using machine learning techniques.
- Approaches based on modeling and intelligent simulation for the design of pneumo-hydraulic systems for liquids are continuously developed.
- Methods to extract data about tourist destinations and tourist behavior from public, heterogeneous data sources and to create knowledge bases for tourist recommender systems.
- Tammet, Tanel. GKC: A reasoning system for large knowledge bases. Automated Deduction - CADE 27 : 27th International Conference on Automated Deduction, Natal, Brazil, August 27-30, 2019, Proceedings, 11716. Ed. Fontaine, Pascal. Cham: Springer, 538−549. (Lecture Notes in Artificial Intelligence; 11716).10.1007/978-3-030-29436-6_32.
- Spichakova, Margarita; Belikov, Juri; Nõu, Kalvi; Petlenkov, Eduard. Feature engineering for short-term forecast of energy consumption. Proceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) : Bucharest, Romania, 29 September - 2 October 2019. IEEE, [1−5].10.1109/ISGTEurope.2019.8905698.
- Koschel, Arne; Heine, Felix; Astrova, Irina. Harnessing cloud scalability to Hadoop clusters. Information Systems : 15th European, Mediterranean, and Middle Eastern Conference, EMCIS 2018, Limassol, Cyprus, October 4-5, 2018, Proceedings. Ed. Themistocleous, Marinos; Rupino da Cunha, Paulo. Cham: Springer, 59−71. (Lecture Notes in Business Information Processing; 341).10.1007/978-3-030-11395-7_6.
|Irina Astrova||Senior Research ScientistSchool of Information Technologies: Department of Software Sciences||CYBemail@example.com||6204219|
|Hele-Mai Haav||Senior Research ScientistSchool of Information Technologies: Department of Software Sciences||CYBfirstname.lastname@example.org||6204213|
|Mait Harf||Research ScientistSchool of Information Technologies: Department of Software Sciences||CYBemail@example.com||6204221|
|Priit Järv||Software Development SpecialistSchool of Information Technologies: Department of Software Sciences||ICT-416||Priit.Jarv1@taltech.ee|
|Kristiina Kindel||EngineerSchool of Information Technologies: Department of Software Sciences||CYBfirstname.lastname@example.org||6204219|
|Vahur Kotkas||Development OfficerSchool of Information Technologies: Department of Software Sciences||CYBemail@example.com||6204217|
|Rauni Lillemets||ResearcherSchool of Information Technologies: Department of Software Sciencesfirstname.lastname@example.org|
|Ago Luberg||LecturerSchool of Information Technologies: Department of Software Sciences||ICTemail@example.com|
|Riina Maigre||Research ScientistSchool of Information Technologies: Department of Software Sciences||CYBfirstname.lastname@example.org||6204240|
|Marta Olvet||GIS SpecialistSchool of Information Technologies: Department of Software Sciencesemail@example.com|
|Rene Pihlak||Early Stage ResearcherSchool of Information Technologies: Department of Software Sciences||ICTfirstname.lastname@example.org|
|Margarita Spitšakova||Software DeveloperSchool of Information Technologies: Department of Software Sciences||CYBemail@example.com||6204219|
|Tanel Tammet||ProfessorSchool of Information Technologies: Department of Software Sciences||ICTfirstname.lastname@example.org||6203457|
|Martin Verrev||Early Stage ResearcherSchool of Information Technologies: Department of Software Sciences||ICT-422||Martin.Verrev@taltech.ee|
|Sven Veskioja||EngineerSchool of Information Technologies: Department of Software Sciencesemail@example.com|