13 papers to be presented by DoC members at international conferences

20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS21) and the 30th International Joint Conference on AI (IJCAI21)

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing an internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS21 received 612 full paper submissions, of which 152 have been accepted (25% acceptance rate).

IJCAI is the premier conference for the international AI community to communicate the advances and achievements of AI research. IJCAI21 received 4204 full paper submissions, of which 587 have been accepted (13.9% acceptance rate).

The accepted papers by members of the department for the two conferences (in alphabetical order of the first authors) are:

AAMAS (Main track):

• R. Chandan, D. Paccagnan and J. R. Marden; "Tractable Mechanisms for Computing Near-Optimal Utility Functions".
• P. El Mqirmi, F. Belardinelli and B. G. León; "An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors".
• S. Lauren, F. Belardinelli and F. Toni; "Aggregating Bipolar Opinions".

AAMAS (Demo track):

• A. Dejl, P. He, P. Mangal, H. Mohsin, B. Surdu, E. Voinea, E. Albini, P. Lertvittayakumjorn, A. Rago and F. Toni; "Argflow: A Toolkit for Deep Argumentative Explanations for Neural Networks" (student project).

IJCAI (Main track):

• B. Batten, P. Kouvaros, A. Lomuscio, Y. Zheng; "Efficient Neural Network Verification via Layer-based Semidefinite Relaxations and Linear Cuts".
• F. Belardinelli, S. Knight, A. Lomuscio, B. Maubert, A. Murano, S. Rubin; "Reasoning about agents that may know other agents’ strategies".
• L-W Cai, W-Z Dai, S. H. Muggleton; "Abductive Learning with Ground Knowledge Base".
• W-Z Dai, S. H. Muggleton; "Abductive Knowledge Induction From Raw Data".
• P. Henriksen, A Lomuscio; "DEEPSPLIT: An Efficient Splitting Method for Neural Network Verification via Indirect Effect Analysis".
• P Kouvaros, A. Lomuscio; "Towards Scalable Complete Verification of ReLU Neural Networks via Dependency-based Branching".
•  M. Law, A. Russo, K. Broda, E. Bertino; "Scalable Non-observational Predicate Learning in ASP”.
• S. Tuli, R. Bansal, M. and R. Paul; "TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators".

IJCAI2021 (Survey Track):

• K. Cyras, A. Rago, E. Albini, P. Baroni and F. Toni; "Argumentative XAI: A Survey".