@inproceedings{dodampegama_collaborate_2023,
 abstract = {This paper describes an architecture for ad hoc teamwork, i.e., to enable an agent to collaborate with other agents “on the
fly". State of the art frameworks for ad hoc teamwork often pursue a data-driven approach, using a large labeled dataset of
prior observations to model the behavior of other agents and to determine the ad hoc agent’s behavior. These models are
computationally expensive to learn, lack transparency, and make it difficult to recognize and adapt to previously unseen
changes. In a departure from existing work, we introduce an architecture for ad hoc teamwork that performs non-monotonic
logical reasoning with prior commonsense domain knowledge and models that are learned and revised rapidly from limited
examples to predict the behavior of other agents. In addition, the architecture enables the agent to provide relational
descriptions as on-demand explanations of its decisions and beliefs in response to different types of questions. We evaluate
the architecture’s capabilities in two benchmark multiagent collaboration domains: Fort Attack and Half field Offense, in
comparison with data-driven and knowledge-driven baselines.},
 author = {Dodampegama, Hasra and Sridharan, Mohan},
 booktitle = {Proceedings of the 21st International Workshop on Non-Monotonic Reasoning co-located with the 20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023) and co-located with the 36th International Workshop on Description Logics (DL 2023), Rhodes, Greece, September 2-4, 2023},
 editor = {Sauerwald, Kai and Thimm, Matthias},
 pages = {32--42},
 publisher = {CEUR-WS.org},
 series = {CEUR Workshop Proceedings},
 shorttitle = {Collaborate and Explain on the Fly},
 title = {Collaborate and Explain on the Fly: Nonmonotonic Logical Reasoning and Incremental Learning for Ad Hoc Teamwork},
 url = {https://ceur-ws.org/Vol-3464/paper4.pdf},
 urldate = {2024-12-11},
 volume = {3464},
 year = {2023}
}