2024

• Thórisson K.R., Basílio S., Carvalho G., Mayer, C. U., Burger, B., Gudjónsson S., Hlynsson K., Sigfusdóttir I.D., Mayer C., Ólafsson S. & Torrent M.A. (2024). SocialInsight: An Agent-Based Simulation Framework for Susbstance Abuse Prevention in Youth — A Proof-of-Concept. IIIM Technical Report IIIMTR-20240401. Reykjavik, Iceland: Icelandic Institute for Intelligent Machines.
• A. Sheikhlar & K.R. Thórisson (2024). Causal Generalization via Goal-Driven Analogy. Proc. Artifi. General Intel., 165-175. Kurzweil Best Paper Award. Springer LNAI 14951.
• Eberding, L., J. Thompson & K. R. Thórisson (2024). Argument-Driven Planning & Autonomous Explanation Generation. Proc. Artif. General Intel., 73-83. AGI Society Best Paper Prize. Springer LNAI 14951.
• Thórisson & G. Talevi (2024). A Theory of Foundational Meaning Generation in Autonomous Systems, Natural and Artificial. Proc. Artif. General Intel., 188-198. Springer LNAI 14951.

2023

• Thórisson K.R., Basílio S., Carvalho G., Mayer, C. U., Burger, B., Gudjónsson S., Hlynsson K., Sigfusdóttir I.D., Mayer, C. & Ólafsson S. (2023). Agent-Based Simulation Framework for Susbstance Abuse Prevention in Youth: A Proof-of-Concept (v.0.8). IIIM Technical Report IIIMTR-20230901. Reykjavik, Iceland: Icelandic Institute for Intelligent Machines.
• Carvalho G., Basílio S., Úlfarsson B., Vigfúsdóttir Th. H. & Thórisson K. R. (2023). Artificial Data Generation in Psychotherapy: Modeling and Predicting Patient Progress. IIIM Whitepaper, July 2023. Reykjavik, Iceland: Icelandic Institute for Intelligent Machines.
• K. R. Thórisson, H. Rörbeck, J. Thompson & H. Latapie (2023). Explicit Goal-Driven Autonomous Self-Explanation Generation. Proc. Artificial General Intelligence, 286-295
• K. R. Thórisson, A. Vallentin & H. Latapie (2023). Addressing the Unsustainability of Deep Neural Networks with Next-Gen AI. Proc. Artificial General Intelligence, 296-306
• L. M. Eberding & K. R. Thórisson (2023). Causal Reasoning over Probabilistic Uncertainty. Proc. Artificial General Intelligence, 74-84

2022

• K. R. Thórisson & H. Minsky (2022). The Future of AI Research: Ten Defeasible ‘Axioms of Intelligence’. Proc. Machine Learning Research, 192:5-21
• A. Sheikhlar, K. R. Thórisson, J. Thompson (2022). Explicit General Analogy for Autonomous Transversal Learning. Proc. Machine Learning Research, 192:48-62
• P. Rikhardsson, K. R. Thórisson, G. Bergthorsson & C. Batt (2022). Artificial intelligence and auditing in small- and medium-sized firms: Expectations and applications. A.I. Magazine, 43(3):323-336

2021

• K. R. Thórisson (2021). The ‘Explanation Hypothesis’ in General Self-Supervised Learning. Proc. Machine Learning Research, 159:5-27
• L. M. Eberding, M. Belenchia, A. Sheikhlar and K. R. Thórisson (2021). About the Intricacy of Tasks. Proc. Artificial General Intelligence, AGI-21, 65-74
• A. Sheikhlar, L. M. Eberding and K. R. Thórisson (2021). Causal Generalization in Autonomous Learning Controllers. Proc. Artificial General Intelligence, AGI-21, 228-238
• M. Belenchia, K. R. Thórisson, L. M. Eberding and A. Sheikhlar (2021). Elements of Task Theory. Proc. Artificial General Intelligence, AGI-21, 19-29

2020

• N.-M. Aliman, P. Elands, W. Hürst, L. Kester, K. R. Thórisson, P. Werkhoven, R. Yampolskiy & S. Ziesche (2020). Error-Correction for AI Safety. Proc. Artificial General Intelligence, 12-22
• K. R. Thórisson (2020). Seed-Programmed Autonomous General Learning. Proc. Machine Learning Research, 131:32-70
• K. R. Thórisson (2020). Discretionarily Constrained Adaptation Under Insufficient Knowledge & Resources. Part I: Introductory Commentary, Special Issue: On Defining Artificial Intelligence. Journal of Artificial General Intelligence, 11(2):7-12
• D. Monett, C. W. P. Lewis & K. R. Thórisson (2020). Introduction to the JAGI Special Issue “On Defining Artificial Intelligence”:
  Commentaries and Author’s Response. Editorial, Special Issue: On Defining Artificial Intelligence, Journal of Artificial General Intelligence, 11(2):1-4
• A. Sheikhlar, K. R. Thórisson & L. Eberding (2020). Autonomous Cumulative Transfer Learning. Proc. Artificial General Intelligence, 306-316
• L. Eberding, K. R. Thórisson, A. Sheikhlar & S. P. Andrason (2020). SAGE: Task-Environment Platform & For Evaluating a Broad Range of Learners. Proc. Artificial General Intelligence, 72-82

2019

• K. R. Thórisson, J. Bieger, X. Li & P. Wang (2019). Cumulative Learning. Proc. Artificial General Intelligence (AGI-19), Shenzhen, China, Aug. 6-9, 198-209

2018

• K. R. Thórisson & A. Talbot (2018). Cumulative Learning With Causal-Relational Models. Proc. Artificial General Intelligence, Prague, 227-238
• K. R. Thórisson & A. Talbot (2018). Abduction, Deduction & Causal-Relational Models.  IJCAI-18 Workshop on Architectures for Generality, Autonomy & Progress in AI, International Joint Conference on Artificial Intelligence, Stockholm, Sweden
• J. Bieger & K. R. Thórisson (2018). Task Analysis for Teaching Cumulative Learners. Proc. 11th International Conference on Artificial General Intelligence (AGI-18), 21-32
• J. Bieger & K. R. Thórisson (2018). Requirements for General Intelligence: A Case Study in Trustworthy Cumulative Learning for Air Traffic Control. IJCAI-18 Workshop on Architectures for Generality, Autonomy & Progress in AI, International Joint Conference on Artificial Intelligence, Stockholm, Sweden

2017

• K. R. Thórisson & D. Kremelberg (2017). Understanding & Common Sense: Two Sides of the Same Coin?
  Proc. 10th International Conference on Artificial General Intelligence (AGI-16), Melbourne, Australia, 201-211
• K. R. Thórisson & D. Kremelberg (2017). Do Machines Understand?  Understanding Workshop, 10th International Conference on Artificial General Intelligence (AGI-17), August 18, Melbourne Australia
• J. Bieger, K. R. Thórisson (2017). Evaluating Understanding.
  IJCAI Workshop on Evaluating General-Purpose Inteligence, August 20, Melbourne, Australia
• J. Bieger, K. R. Thórisson & B. R. Steunebrink (2017). The Pedagogical Pentagon: A Conceptual Framework for Artificial Pedagogy. Proc. 10th International Conference on Artificial General Intelligence (AGI-17), August 15-18, Melbourne Australia, 212-222
• K. R. Thórisson (2017). Architectures for Generality & Autonomy: Which Methodology? IJCAI Workshop on Architectures for Generality & Autonomy, August 19, Melbourne, Australia

2016

• G. Pereira (2016). A Multi-Layer Non-Newtonian Model of Cardiovascular Inflammation. J Biomed Eng Med Devic, 1(3)1-6
• K. R. Thórisson, D. Kremelberg, B. R. Steunebrink, E. Nivel (2016). About Understanding. In B. Steunebrink et al. (eds.), Proc. 9th International Conference on Artificial General Intelligence, 106-117
• B. R. Steunebrink, K. R. Thórisson, D. Kremelberg, J. Schmidhuber (2016). Growing Recursive Self-Improvers. In B. Steunebrink et al. (eds.), Proc. 9th International Conference on Artificial General Intelligence (AGI-16), New York City, July 16-19
• K. R. Thórisson, J. Bieger, T. Thorarensen, J. S. Sigurdardottir & B. R. Steunebrink (2016). Why Artificial Intelligence Needs a Task Theory – And What It Might Look Like. In B. Steunebrink et al. (eds.), Proc. Artificial General Intelligence, 118-128

2015

• Bieger, J., Thórisson, K. R. & Wang, P. (2015). Safe Baby AGI. In J. Bieger, B. Goertzel & A. Potapov (eds.), Proceedings of Artificial General Intelligence (AGI-15), 46-49, Springer-Verlag, Berlin, Germany.
• Mallett, J. (2015). General Disequilibrium: the Hidden Conflict between Fractional Reserve Banking and Economic Theory. Cosmos+Taxis, Studies in Emergent Order and Organization, Vol. 2, Issue 2, pp. 18-33.
• Mallett, J. (2015). Threadneedle: An Experimental Tool for the Simulation and Analysis of Fractional Reserve Banking Systems. Eastern Economic Association. Article presented at the Eastern Economic Association’s 41st Conference in 2015.
• Nivel, E., Thórisson, K. R., Steunebrink, B. R. & Schmidhuber, J. (2015). Anytime Bounded Rationality. Proc. Artificial General Intelligence, 121-130.
• Perkin, S., Garrett, D., and Jenssen, P. (2015). Optimal Wind Turbine Selection Methodology: A Case-study for Búrfell, Iceland. Renewable Energy, 75(3):165–172.
• Thórisson, K. R., Bieger, J., Schiffel, S. & Garrett, D. (2015). Towards Flexible Task Environments for Comprehensive Evaluation of Artificial Intelligent Systems and Automatic Learners. In J. Bieger, B. Goertzel & A. Potapov (eds.), Proc. Artificial General Intelligence

2014

• Bieger, J., Thórisson, K. R., & Garrett, D. (2014). Raising AI: Tutoring Matters. In B. Goerzel, L. Orseau, & J. Snaider (Eds.), Proceedings of Artificial General Intelligence (AGI-14) (pp. 1–10) Quebec, Canada: Springer-Verlag.
• Garrett, D., Bieger, J., & Thórisson, K. R. (2014). Tunable and Generic Problem Instance Generation for Multi-objective Reinforcement Learning. Proceedings of the 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, Orlando, USA.
•  Garrett, D., &Thórisson, K. R. (2014, March 9th). Machine learning for improving adaptivity of artificial knees to environmental conditions IIIM Technical Report IIIMTR-2014-09-03. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.
• Gudjonsson, D. S. (2014). Exploring the Potential Macroeconomic Impacts of Branch Banking Practice (Unpublished master’s thesis). Reykjavik University, Reykjavík, Iceland.
• Helgason, H. P., Thorisson, K. R., Garrett, D., & Nivel, E. (2014). Towards a General Attention Mechanism for Embedded Intelligent Systems. International Journal of Computer Science and Artificial Intelligence, 4(1):1–7
• Nivel, E., Thórisson, K. R., Steunebrink, B. R.., Dindo H., Peluzo, G., Rodriguez, M., … Chella, A. (2014). Bounded Seed-AGI. In B. Goerzel, L. Orseau, & J. Snaider (Eds.), Proce. Artificial General Intelligence, 85–96
• Nivel, E., Thórisson, K. R., Steunebrink, B. R., Dindo, H., Peluzo, G., Rodriguez, M., et al. (2014). Autonomous Acquisition of Natural Language. In A. P. dos Reis, P. Kommers, & P. Isaías (Eds.), Proceedings of the IADIS International Conference on Intelligent Systems & Agents 2014 (ISA-14), Lissbon, Portugal, July 15–17 (pp. 58–66) (Recipient of the Outstanding Paper Award at the Intelligent Systems & Agents conference).
• Valgardsson, G. S., Fornari, F., Thórisson H. Th., & Thórisson, K. R. (2014). Living shadows developer documentation v. 1.0. IIIM Technical Report IIIMTR-2014-01-001. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.

2013

• Kristjánsson, Th., & Thórisson, K. R. (2013). The ethics of artificial intelligence: Risks & responsibilities. IIIM Technical Report IIIMTR-2013-12-002. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.
• Ólafsdóttir, G. F., Thórisson H. Th., & Valgardsson, G. S. & Thórisson, K. R. (2013). An Agent-Based Methodology for Simulating Human Physiological Processes. IIIM Technical Report, IIIMTR-2013-12-001. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.
• Helgason, H. P., Thórisson, K. R., & Nivel, E. (2013). Predictive Generative Heuristics for Decision-Making in Real-World Environments. Proceedings of Artificial General Intelligence (AGI-13), Beijing, China
• Nivel, E., & Thórisson, K. R. (2013).Towards a Programming Paradigm for Control Systems With High Levels of Existential Autonomy. Proceedings of Artificial General Intelligence (AGI-13), Beijing, China
• Steunebrink, B. R., Koutnik, J., Thórisson, K. R., Nivel, E., & Schmidhüber, J. (2013). Resource-Bounded Machines are Motivated to be Effective, Efficient and Curious. Proceedings of Artificial General Intelligence (AGI-13) (recipient of the Kurzweil Prize)
• Jonsdottir, G.R., & Thórisson, K. R. (2013). A Distributed Architecture for Real-Time Dialogue and On-Task Learning of Efficient Cooperative Turn-Taking. In M. Rojc and N. Campbell (Eds.), Speech, gaze and affect (Ch. 12, pp. 293–324). Boca Raton, Florida, USA: Taylor & Francis
• Thórisson, K. R. (2013). Reductio ad Absurdum: On Oversimplification in Computer Science and its Pernicious Effect on Artificial Intelligence Research. Proceedings of Artificial General Intelligence (AGI-13), Formal MAGIC workshop on formalization in artificial intelligence, Beijing, China, July 31st.
• Garrett, D. (2013). Tunable instance generation for many-task reinforcement learning. IIIM Technical Report IIIMTR-2013-01-002. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.

2012

• Thórisson, K. R., Nivel, E., Sanz, R., & P. Wang (2012). Editorial: approaches and assumptions of self-programming in achieving artificial general intelligence. Journal of Artificial General Intelligence, 3(3), 1–10
• Dindo, H., La Tona, G., Pezzulo, G., Nivel, E., & Thórisson, K. R. (2012). Simulation and anticipation as tools for coordinating with the future. Proc. of Biologically-Inspired Cognitive Architectures 2012.
• Helgason, H. P., Nivel, E., & Thórisson, K. R. (2012). On attention mechanisms for AGI architectures: A design proposal. Proceedings of Artificial General Intelligence, Oxford University, December 2012. (Recipient of the Kurzweil Prize)
• Helgason, H. P., & Thórisson, K. R. (2012). Attention Capabilities for AI Systems. Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2012), Rome, July 2012.
• Mallett, J. (2012). A system analysis of the Basel capital accord’s regulatory control over the behaviour of modern banking systems. Systems Conference(SysCon) 2012 IEEE
• Thórisson, K. R. (2012). A New Constructivist AI: From Manual Methods to Self-Constructive Systems. In B. Goertzel and P. Wang (Eds.), Theoretical foundations of artificial general intelligence. Amsterdam, the Netherlands: Atlantis Press.
• Mallett, J. (2012, August). What are the limits on Commercial Bank Lending? Advances in Complex Systems. Retrieved from http://www.worldscientific.com/doi/pdf/10.1142/S0219525912500750 (paywall) http://arxiv.org/abs/0904.1426 (open access)
• Thórisson, K. R., & Helgason, H. P. (2012). Cognitive architectures and autonomy: A comparative review. Journal of Artificial General Intelligence, 3(2), 1–30

2011

• Mallett, J. (2011a). Analysing the behaviour of the Textbook Fractional Reserve Banking model as a Complex Dynamic System. Eighth International Complex Systems Conference, June 2011. Retrieved from http://necsi.edu/events/iccs2011/papers/60.pdf
• Mallett, J. (2011b). When the textbook is wrong – observations on flaws in the textbook models of the banking system and implications for economic theory. 13th Conference of the Association for Heterodox Economics. Retrieved from http://www.hetecon.net/documents/ConferencePapers/2011Refereed/Mallet_ AHE2011036R.pdf
• Dindo, H., Chella, A., La Tona, G., Vitali, M., Nivel E., & Thórisson, K. R. (2011). Learning problem solving skills from demonstration: An architectural approach. In Proceedings of Artificial General Intelligence 2011.
• Mallett, J. (2011c, January). Modelling the textbook fractional reserve banking system: Emergent results of artificial economies. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-21108-9_5
• Olafsdottir, G.F. (2011). A methodology for simulating biological cell systems with cellular automata: The case of the human heart. IIIM Technical Report IIIMTR-2011-04-001. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.

2010

• Gislason, O., Jonsdottir, G. R., Thorisson, H. Th. & Thórisson, K. R. (2010). Multiparty Turntaking Using a Cognitive Model of Multimodal Dialogue Skills. Video published in Intelligent Virtual Agents ’10.
• Thórisson, K.R., Gislason, O., Jonsdottir, G. R., Thorisson, H. Th. (2010).A Multiparty Multimodal Architecture for Realtime Turntaking. Proc. Intelligent Virtual Agents ’10.
• Jonsdottir, G. R., & Thórisson, K. R. (2010). A distributed realtime dialogue architecture for dynamically learning polite human Turntaking. IIIM Technology Report IIIMTR-2010-10-002. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.
• Wang, P. (2010). Four Basic Questions about Artificial Intelligence. IIIM Technical Report IIIMTR-2010-05-001. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.
• Mallet, J. (2010). What are the limits on commercial bank lending? IIIM Technical Report IIIMTR-2010-09-001. Reykjavík, Iceland: Icelandic Institute for Intelligent Machines.