Gita Sukthankar

Quadcopters are well-suited to act as videographers and are capable of shooting from locations that are unreachable for a human. Our project evaluated the performance of two vantage point selection strategies: 1) a reactive controller that tracks the subject's head pose and 2) combining the reactive system with a POMDP planner that considers the target's movement intentions.

Autonomous Quadcopter Videographer Quadcopters are well-suited to act as videographers and are capable of shooting from locations that are unreachable for a human. Our project evaluated the performance of two vantage point selection strategies: 1) a reactive controller that tracks the subject's head pose and 2) combining the reactive system with a POMDP planner that considers the target's movement intentions. Video: Normative Agent Architectures for Modeling Human Health Habits Norms are an important part of human social systems, governing many aspects of group decision-making. Normative agent architectures extend on standard multi-agent systems by incorporating computational machinery for reasoning about social norms. Within the multi-agent research community, the study of norm emergence, compliance, and adoption has resulted in new architectures and standards for normative agents; however few of these models have been successfully applied to real-world public policy problems. We have developed two agent architectures, LNA (Lightweight Normative Architecture) and CSL (Cognitive Social Learners) for predicting trends in smoking cessation resulting from the UCF smoke-free campus initiative. Described in "Cognitive Social Learners: an Architecture for Modeling Normative Behavior" Rahmatollah Beheshti*, Awrad Mohammed Ali*, Gita Sukthankar. Proceedings of the AAAI Conference on Artificial Intelligence. 2015. pp. 2017-2023. Described in "A Normative Agent-based Model for Predicting Smoking Cessation" Rahmatollah Beheshti*, Gita Sukthankar. Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems. 2014. pp. 557-564 KPark is a mobile phone app for crowdsourcing parking availability on the UCF campus. This is an example of participatory sensing---a specialized form of crowdsourcing for mobile devices in which the users act as sensors to report on local environmental conditions, such as traffic, pollution, and wireless signal strength. In additional to crowdsourced data, KPark uses an agent-based simulation of transportation patterns on the UCF campus to improve the accuracy of predictions in cases where few users have submitted reports.

Multi-robot tasks can be complicated due to the need for tight temporal coupling between robots. To ameliorate this problem, we developed a paradigm for allowing subjects to configure a user interface for multi-robot manipulation tasks; using a macro acquisition system for learning combined manipulation/driving tasks.

This project focuses on the use of machine learning techniques to learn policies by demonstration and also to predict future player actions from log data. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator.