In goal recognition problems, the goal is to identify the goal of an observed agent as quickly as possible before they reach their goal. This problem can be applied to a number of applications ranging from software personal assistants and robots that anticipate the needs of humans; intelligent tutoring systems that recognize sources of confusion or misunderstanding in students through their interactions with the system; and security applications that recognize terrorists plans.
In goal recognition design problems, the objective is to identify the best ways to modify the underlying environment of the agents in such a way that they are forced to reveal their goals as early as possible. This problem is typically applicable in the same goal recognition applications as long as the underlying environment can be modified. For example, in intelligent tutoring systems, it is possible to modify the sequence or type of questions asked so that students’ misunderstanding can be identified sooner. Similarly, in security applications, it may be possible to block some paths so that an attacker’s plan can be recognized earlier.
Our current research is on both the algorithmic back-end, where the goal is to enrich the models so as to better capture more realistic problem characteristics and develop efficient algorithms to solve them, as well as the user interface front-end, where the goal is to develop visualization interfaces that allow our methods to be adapted to specific applications and evaluated with human users.
Improving Client Experience Through Goal Recognition and Explainable Assistance in Adaptive Systems.
J.P. Morgan Chase Bank (2022 – 2023).
CAREER: Decentralized Constraint-based Optimization for Multi-Agent Planning and Coordination.
National Science Foundation (2016 – 2021).
Integrated Computational and Cognitive Workflows for Improved Security and Usability.
- Christabel Wayllace and William Yeoh. “Stochastic Goal Recognition Design Problems with Suboptimal Agents.” In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 9953-9961, 2022.
- Christabel Wayllace, Sarah Keren, Avigdor Gal, Erez Karpas, William Yeoh, and Shlomo Zilberstein. “Accounting for Partial Observability in Stochastic Goal Recognition Design: Messing with the Marauder’s Map.” In Proceedings of the European Conference on Artificial Intelligence (ECAI), pages 2394-2401, 2020.
- Christabel Wayllace, Sunwoo Ha, Yuchen Han, Jiaming Hu, Shayan Monadjemi, William Yeoh, and Alvitta Ottley. “DRAGON-V: Detection and Recognition of Airplane Goals with Navigational Visualization (Demonstration Track).” In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 13642-13643, 2020.
- Christabel Wayllace, Ping Hou, and William Yeoh. “New Metrics and Algorithms for Stochastic Goal Recognition Design Problems.” In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 4455-4462, 2017.
- Christabel Wayllace, Ping Hou, William Yeoh, and Tran Cao Son. “Goal Recognition Design with Stochastic Agent Action Outcomes.” In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 3279-3285, 2016.