Aswin Raghavan

Solving Relational MDPs with Exogenous Events and Additive Rewards

We formalize a simple but natural subclass of service domains for relational planning problems with object-centered, independent exogenous events and additive rewards capturing, for example, problems in inventory control. Focusing on this subclass, we present a new symbolic planning algorithm which is the first algorithm that has explicit performance guarantees for relational MDPs with exogenous events. In particular, under some technical conditions, our planning algorithm provides a monotonic lower bound on the optimal value function. To support this algorithm we present novel evaluation and reduction techniques for generalized first order decision diagrams, a knowledge representation for real-valued functions over relational world states. Our planning algorithm uses a set of focus states, which serves as a training set, to simplify and approximate the symbolic solution, and can thus be seen to perform learning for planning. A preliminary experimental evaluation demonstrates the validity of our approach.

Aesop: A Visual Storytelling Platform for Conversational AI.

We present a new collaborative visual storytelling platform, Aesop, for direction and animation. Aesop consists of a language parser, human gesture monitoring, composition graphs, dialogue state manager, and an interactive 3D animation software. Aesop thus enables 3D spatial and temporal reasoning which are both essential for storytelling. Our key innovation is to enable conversational AI using both verbal and non-verbal communication, which enables research in language, vision, and planning.

Emergency Response Optimization using Online Hybrid Planning

This paper poses the planning problem faced by the dispatcher responding to urban emergencies as a Hybrid (Discrete and Continuous) State and Action Markov Decision Process (HSA-MDP). We evaluate the performance of three online planning algorithms based on hindsight optimization for HSAMDPs on real-world emergency data in the city of Corvallis, USA. The approach takes into account and respects the policy constraints imposed by the emergency department. We show that our algorithms outperform a heuristic policy commonly used by dispatchers by significantly reducing the average response time as well as lowering the fraction of unanswered calls. Our results give new insights into the problem such as withholding of resources for future emergencies in some situations.