Adrian Groza

Associate Professor,

Techical University of Cluj-Napoca

Research Teaching


I am an associate professor in ISG (Intelligent Systems Group), Department of Computer Science, Technical University of Cluj-Napoca. Current work involves research on logic, argumentation knowledge presentation. More recently, my research has focused on: 1) applying argumentation for conflict resolution in ensemble learning, 2) the distinction between argument and explanation in dialogue, 3) the distinction between human reasoning and reasoning of logical-agents, 4) analysing arguments conveyed by people in public arena regarding climate change. Previous research interests lie in the areas of non-monotonic logics, legal reasoning, safety assurance of software systems by means of argumentation, description logics, and their applications in artificial intelligence.

Recent Activity

General Co-chair at MIKE2018

I co-organised with Rajendra Prasath the 6th Int. Conf. on Minining Intelligence and Knowledge Exploration, 20-22 December 2018, Cluj-Napoca, Romania. The proceedings have been published by Springer in LNAI.

Invited Lecture at Engineering Summer University

ESA, 15-29 July 2018, Cluj-Napoca, Romania. Lectures on Semantic Web and Epistemic Games

Lectures at Technical University of Košice

TUKE, I had lectures on Description Logics, Epistemic Logic, and Taking decisions under the CEEPUS programme, May, 2018.


Research Grants


Increasing understanding on climate change through public discourse analyse and stakeholders modelling, Sep-Dec 2016, EEA-Grant


Collaborative Recommendation System in the Tourism Domain Using Semantic Web Technologies and Text Analysis in Romanian Language, Dec. 2013- Mai 2014, UEFSCDI (with Recognos).


Improving transportation using Car-2-X communication and multi agent systems, Oct 2013- Sep 2014,


Using Argumentation for justifying safeness in complex technical systems, Romania-Argentina Bilateral Contract, July 2013-June 2015.


Ontology enrichment and evaluation


Automating Online Dispute Resolution for B2B using multi-agent systems, TD7, CNCSIS-534, 2007-2008


DBLP GoogleScholar ORCHID ResearchGate



Artificial Intelligence

Artificial Intelligence

I follow the fist part from the Russell and Norvig, AIMA, 3rd edition: Intelligent agents, Solving problems by searching, Behind classical search, Adversarial search, Constraint satisfaction problems, Logical agents, First order logic, Inference in First Order Logic, Classical Planning, Planning and acting in real world, Common sense reasoning with event calculus.

Intelligent Systems

I follow the fist part from the Russell and Norvig, AIMA, 3rd edition: Quantifying uncertainty, Probabilistic reasoning, Probabilistic reasoning over time, Making simple decisions, Makinng complex decisions, Learning from examples, Knowledge in learning, Learning probabilistic models, Reinforcement learning, Natural language processing, Natural language for communication

Knowledge-Based Systems

Models in First order logic, Semantic Web, Description logics, Inference in description logics, Ontology Engineering, Epistemic games with Dynamic Epistemic Logic, Software verification with Computational Tree Logic, Fuzzy logic


Artificial Intelligence
1. The state of the art: Turing test, acting humanly, thinking humanly, acting rationally, applications, explainable AI;
2. Intelligent agents: agents and environments, rational agents, structure of agents;
3. Solving problems by searching: uninformed search, informed search, A* search, heuristic functions;
4. Beyond classical search: local search, hill climbing, simulated annealing, local beam search, genetic algorithms, searching with non-deterministic  actions, searching with partial observation;
5. Adversarial search:  games, and-or search trees, min-max, alpha-beta pruning, imperfect-real time decisions, stochastic games, partially observable games, state of the art game programs;
6. Constraint satisfaction problems(CSPs): defining CSP problems, constraint propagation, node consistency, arc consistency, path consistency, local search for CSPs, heuristics for CSPs;
7. Logical agents: knowledge-based agents, propositional logic (PL), theorem proving, reasoning in PL, satisfiability, Davis-Putnam algorithm, modelling in PL, solving logical puzzles in PL;
8. First order logic (FOL): syntax and semantics of FOL, knowledge engineering in FOL,
solving logical puzzles in FOL;
9. Inference in FOL:
unification and lifting, forward and backward chaining, resolution in FOL, theorem proving, finite models finding in FOL;
10. Classical planning: planning as a state-space search, planning graphs, partial planning, planning domain definition language, planning in situation calculus, heuristics for planning; solving planning puzzles;
11. Planning and acting in the real-world: time, schedules, resources, minimum slack algorithm, hierarchical planning, conformant planning, contingent planning, multi-agent planning;
12. Knowledge representation: event calculus (EC), commonsense reasoning, prediction, abduction and postdiction in EC, modelling patterns in EC, event monitoring, reasoning about commitments in EC
13. Multi-agent systems: Beliefs, desires, intentions, AgentSpeak programming language, Jason, goals, events, alternative plans, cooperation and coordination, concurrent actions;

1. Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. 3rd edition, Pearson 2013.
2. van Benthem J, van Ditmarsch H, van Eijck J, Jaspars J. Logic in Action, 2016.
3. Rossi, Francesca, Peter Van Beek, and Toby Walsh, eds. Handbook of constraint programming. Elsevier, 2006.
4. Ghallab, Malik, Dana Nau, and Paolo Traverso. Automated planning and acting. Cambridge University Press, 2016.
5. Mueller, Erik T. Commonsense reasoning: an event calculus based approach. Morgan Kaufmann, 2014.
6. Bordini, Rafael H., Jomi Fred Hübner, and Michael Wooldridge. Programming multi-agent systems in AgentSpeak using Jason. Wiley, 2007.
7. Millington, Ian, and John Funge. Artificial intelligence for games. 3rd edition, CRC Press, 2019.
8. A. Groza, R.R. Slavescu, A. Marginean. Introduction to Artificial Intelligence, U.T. Press, 2018.

Intelligent Systems
1. Quantifying uncertainty: acting under uncertainty, inference using full joint distributions, variable independence, Bayes rule, probabilistic puzzles
2. Probabilistic reasoning I: Bayesian networks (BNs), global and local semantics, constructing BNs, Markov blanket, d-separation algorithm, deterministic nodes, noisy-or;
3. Probabilistic reasoning II: exact inference (enumeration, variable elimination), approximate inference (rejection sampling, likelihood weighting), markov chains;
4. Probabilistic reasoning over time I: time and uncertainty, inference in temporal models;
Probabilistic reasoning over time II:hidden Markov models, Kalman filters, dynamic Bayesian networks, Viterbi algorithm;
6. Making simple decisions: utility theory, utility functions, decision networks, the value of information, cognitive biases;
7. Making complex decisions: sequential decision problems, value iteration, policy iteration, partially observable Markov Decision Processes, game theory, mechanism design;
8. Supervised learning: regression and classification, decision trees, regression trees, learning probabilistic models, Naive Bayes, learning with hidden variable, support vector machines, ensemble learning
9. Artificial neural networks: perceptrons, multilayer perceptrons, backpropagation, deep learning (DL), convolutional neural networks, recurrent neural networks; applications of DL
10. Knowledge in learning: explanation-based learning, learning using relevance information, inductive logic programming, version spaces, explainable AI (XAI)
11. Unsupervised learning: datamining, cluster analysis, partitional clustering, k-means, bisecting k-means, hierarchical clustering, cluster similarity;
12. Unsupervised learning:  frequent itemset generation, rule generation, compact representation of frequent itemsets,  sequential pattern mining
13. Reinforcement learning (RL): passive RL, active RL, policy search, applications of RL;
14. Natural language processing: machine comprehension, augmented grammars and semantic representation, text classification, AI ethics.

1. Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. 3rd edition, Pearson 2013.
2. Finn Verner Jensen and Nielsen, Thomas Dyhre, . Bayesian networks and decision graphs. 2nd edition, Springer, 2016.
3 Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. Introduction to data mining. Pearson Education, 2016.
4. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521, no. 7553 (2015): 436-444.

Knowledge-Based Systems

1. Logical games in First Order Logic (FOL):  minefinder, minefield, friends puzzles, ladies and tigers, models in FOL; discourse checking in FOL;
2. Description Logics (DLs): Semantic Web, ontologies, families of DLs, modelling in DL, models in DL,  solving logical puzzles  in DL;
3. Reasoning in DL: tableaux algorithms, converting DL2FOL;
4. Ontology engineering:
5. Applications of DL:
modelling in DL, machine learning for the semantic web, semantic search, medical and legal ontologies, ontology design patterns;
6. Epistemic reasoning: epistemic logic (EL), common knowledge, public announcement, dynamic epistemic logic, modelling in EL, solving epistemic puzzles;
7.  Model checking: formal verification, modelling and reasoning about systems, computational tree logic, protocol verification
8.  Expert systems: rule-based reasoning, efficient reasoning, Rete algorithm,  conflict resolution strategies, engineering efficient rule-based systems;
9. Fuzzy reasoning: fuzzy logic (FL), membership functions, fuzzy sets, mandami rules, fuzzification, defuzzification methods, application of FL;
10. Non-monotonic reasoning: defeasible logic, argumentative semantics
11. Answer Set Programming.


1. Baader, Franz, Diego Calvanese, Deborah McGuinness, Peter Patel-Schneider, and Daniele Nardi, eds. The description logic handbook: Theory, implementation and applications. Cambridge university press, 2003.
2. Ianni, Giovambattista, Domenico Lembo, Leopoldo Bertossi, Wolfgang Faber, Birte Glimm, Georg Gottlob, and Steffen Staab, eds. Reasoning Web. Semantic Interoperability on the Web: 13th International Summer School 2017, London, UK, July 7-11, 2017, Tutorial Lectures. Vol. 10370. Springer, 2017.
3. Antoniou, Grigoris, and Frank Van Harmelen. A semantic web primer. 3rd edition, MIT press, 2012.
4. Bühmann, L., Lehmann, J., & Westphal, P. (2016). DL-Learner - A framework for inductive learning on the Semantic Web. Journal of Web Semantics, 39, 15-24.
5. Haarslev, Volker, Kay Hidde, Ralf Möller, and Michael Wessel. "The RacerPro knowledge representation and reasoning system." Semantic Web 3, no. 3 (2012): 267-277.
6. A. Groza. Ontology Engineering with RacerPro - An Activity Based Approach, U.T. Press, 2014, ISBN 978-973-662-991-4
7. Discourses on Social Software, Van Eijck and Verbrugge (eds.), Amsterdam University Press, 2009;

8. van Benthem J, van Ditmarsch H, van Eijck J, Jaspars J. Logic in Action, 2016.
9.  Logic in Computer Science- Huth, Michael RA, and Mark D. Ryan. "Modelling and reasoning about systems." Cambridge University Press 2000; (ch. 3).
10. Engelbrecht, Andries P. Computational intelligence: an introduction. John Wiley & Sons, 2007 (ch 20,22,22).

Diploma projects


Master projects




Research contracts

Available PhD. topics

Available Diploma & Master Projects

Current Diploma & Master Students


Former Diploma Students

Former Master Students



Making sense of arguments posted on online debate sites using subjective logic and textual entailment


Argument mining in medical documents


An ontology selection and ranking system based on analytical hierarchy process


Tanslating the Goal Structuring Notation (GSN)  into description logic in order check the GSN model for consistency


Ontology enrichment and evaluation


Agent-based mediation of conflicting medical information using textual entailment and fuzzy argumentation


26-28 Baritiu Street
Cluj-Napoca, 500025, room 21

+40 (264) 401446