2016/08/31 by Christopher H. Lin, Lin, Christopher H., Mausam Mausam +3
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Mobile Crowdsensing and Crowdsourcing #Optimization and Search Problems #Programming Languages (cs.PL) #Reinforcement Learning in Robotics
paper · pdf · doi:10.48550/arxiv.1608.08724
openalex publication_date 2016/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present POAPS, a novel planning system for defining Partially Observable Markov Decision Processes (POMDPs) that abstracts away from POMDP details for the benefit of non-expert practitioners. POAPS includes an expressive adaptive programming language based on Lisp that has constructs for choice points that can be dynamically optimized. Non-experts can use our language to write adaptive programs that have partially observable components without needing to specify belief/hidden states or reason about probabilities. POAPS is also a compiler that defines and performs the transformation of any program written in our language into a POMDP with control knowledge. We demonstrate the generality and power of POAPS in the rapidly growing domain of human computation by describing its expressiveness and simplicity by writing several POAPS programs for common crowdsourcing tasks.