Decision-making with Predictive State Representations (PSRs)
par/by Michal James
Toyota Research Labs
The ability to make decisions under uncertainty is a key component of
building AI agents for complex dynamical systems. In this talk, I present
decision-making algorithms for agents which model the environment using
Predictive State Representations (PSRs) and an extension called
memory-PSRs. PSRs and mPSRs require only the use of observable quantities
in their representation, and so have the advantage of being grounded and
verifiable in the agent's experience (as opposed to nominal-state based
models such as POMDPs). The decision-making algorithms presented are
extensions of POMDP algorithms which, in the case of mPSRs, take advantage
of structure in the mPSR representation to improve performance.