Prof. Hussein A. Abbass

School of Engineering and Information Technology
University of New South Wales - Canberra - Australia

Email: h D abbass A adfa D edu D au


DP160102037: User-task co-adaptation for effective interactive simulation environments

Project Overview

Funding Organisation

Australian Research Council - Discovery Scheme

Funding Years


Chief Investigators

  1. Hussein Abbass, UNSW-Canberra
  2. Kathryn Merrick, UNSW-Canberra

Partner Investigators

  1. Kay Chen Tan, National University of Singapore
  2. Anastasios Bezerianos, National University of Singapore, University of Patras, Greece

Associated Research Fellows

  1. Dr. Heba El-Fiki

Associated Research Students

  1. Mr. Leo Ghignone, Ph.D., On Going
  2. Mr. Nima Salimi, Ph.D., On Going
  3. Ms. Min Wang, Ph.D., On Going
  4. Ms. Xuejie Liu, Ph.D., On Going

Project Summary

This project aims at creating novel adaptive algorithms to automatically discover those user and task features that vary together to smartly adapt users and simulation tasks to work together harmoniously, seamlessly and effectively. By bringing the user naturally inside the simulation as a task's component, users can improve their experience while task performance is simultaneously optimized. Intended scientific outcomes include novel dynamic user-task profiling algorithms, new adaptive algorithms for user-task co-adaptation, and high impact publications. Prospective practical outcomes include robust and highly effective simulation environments, strong research training, and personnel with superb skills for the Australian knowledge economy.

Project Publications

Journal Papers

  1. Xuejie, L., Merrick K., and Abbass H.A. (to appear) Towards Electroencephalographic Profiling of Player Motivation: A Survey, IEEE Transactions on Cognitive and Developmental Systems. doi:10.1109/TCDS.2017.2726083
  2. Goh S.K., Abbass H.A., Tan K.C., Al-Mamun A., Wang C., Guan C. (2017) Automatic EEG Artifact Removal Techniques by Detecting Influential Independent Components, IEEE Transactions on Emerging Topics in Computational Intelligence, 1(4), 270 - 279. doi:10.1109/TETCI.2017.2690913
  3. Abbass, H. A., Leu, G., & Merrick, K. (2016). A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data. IEEE Access, 4, 2808-2830. doi:10.1109/access.2016.2571058. [Open Access Full Paper available for Download for Free]

Conference Papers

  1. Yang Z., Merrick K., Abbass H. (2017) Multi-task deep reinforcement learning for continuous action control, International Joint Conference on Artificial Intelligence, IJCAI, Melbourne Australia. 10.24963/ijcai.2017/461
  2. Bowley, S. J., & Merrick, K. (2017) A ‘Breadcrumbs’ Model for Controlling an Intrinsically Motivated Swarm Using a Handheld Device. In Australasian Joint Conference on Artificial Intelligence (pp. 157-168). Springer, Cham. doi:10.1007/978-3-319-63004-5_13
  3. Wang M; Abbass H; Hu J (2016) Continuous Authentication Using EEG and Face Images for Trusted Autonomous Systems, The 2016 Privacy, Security and Trust Fourteenth Annual Conference, Auckland, New Zealand, 12 – 14 December 2016. doi:10.1109/PST.2016.7906958
  4. Liu X; Merrick K; Abbass H (2016) Designing Artificial Agents to Detect the Motive Profile of Users in Virtual Worlds and Games, IEEE Symposium Series on Computational Intelligence, Athens, Greece, 06 - 09 December 2016. doi:10.1109/SSCI.2016.7850036
  5. Wang M; Abbass H; Hu J; Merrick K (2016) Detecting Rare Visual and Auditory Events from EEG Using Pairwise-Comparison Neural Networks, The 8th International Conference on Brain-Inspired Cognitive Systems, China, presented at The 8th International Conference on Brain-Inspired Cognitive Systems, China, 01 - 01 January 2016. doi:10.1007/978-3-319-49685-6_9
  6. Abdelfattah S; Merrick K; Abbass H (2016) Eye Movements as Information Markers in EEG Data, IEEE Symposium Series on Computational Intelligence, Athens, Greece, 06 - 09 December 2016. doi:10.1109/SSCI.2016.7850043
  7. Abdelfattah S; Merrick K; Abbass H (2016) Theta-Beta Ratios Are Prominent EEG Features for Visual Tracking Tasks, Human Factors and Ergonomics Society International Meeting, Washington DC, 19 - 23 September 2016. doi:10.1177/1541931213601005
  8. Goh S.K., Abbass H.A., Tan K.C., Al-Mamun A., Guan C., Wang C.C. (2016) Multiway analysis of EEG artifacts based on Block Term Decomposition. Proceedings of the International Joint Conference on Neural Networks. 2016-October: 913-920. doi:10.1109/IJCNN.2016.7727296

Outreach Activities

National Science Week Display

On 12-13 August 2016, UNSW-Canberra ran interactive displays at National Science Week. UNSW-Canberra had four displays from PEMS, Cool Aeronautics, Trusted Autonomy and the UAV Team. The Trusted Autonomy group offered an interactive robotics environment controlled through gestures (hand movements) where two humans played against each other, with each human attempting to control a team of two robots, navigating them while avoiding the opposing team.