A Programming Language With a POMDP Inside
This addresses the challenge for non-expert practitioners in fields like human computation by simplifying POMDP creation, though it appears incremental as it builds on existing POMDP and programming language concepts.
The authors tackled the problem of making Partially Observable Markov Decision Processes (POMDPs) accessible to non-experts by developing POAPS, a planning system that abstracts away POMDP details, resulting in an expressive programming language and compiler that transforms programs into POMDPs with control knowledge.
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.