Research Summary Anticipating that innovation nurtures entrepreneurship, we began an extended case study of innovative start-up in the space industry. We quickly saw institutions imposed formidable barriers to implementing entrepreneurship from innovation. Curious about how, why and extent this situation, widened our other start-ups, CEOs existing businesses, incubator, a technology transfer of...

While POMDPs (partially observable markov decision problems) are a popular computational model with wide-ranging applications, the computational cost for optimal policy generation is prohibitive. Researchers are investigating ever-more efficient algorithms, yet many applications demand such algorithms bound any loss in policy quality when chasing efficiency. To address this challenge, we presen...

Bayesian sequential testing of multiple simple hypotheses is a classical sequential decision problem. But the optimal policy is computationally intractable in general, because the posterior probability space is exponentially increasing in the number of hypotheses (i.e, the curse of dimensionality in state space). We consider a specialized problem in which observations are drawn from the same ex...

Increasing attention has been paid to reinforcement learning algo rithms in recent years partly due to successes in the theoretical analysis of their behavior in Markov environments If the Markov assumption is removed however neither generally the algorithms nor the analyses continue to be usable We propose and analyze a new learning algorithm to solve a certain class of non Markov decision pro...

Psychologists are interested in developing instructional policies that boost student learning. An instructional policy specifies the manner and content of instruction. For example, in the domain of concept learning, a policy might specify the nature of exemplars chosen over a training sequence. Traditional psychological studies compare several hand-selected policies, e.g., contrasting a policy ...

We consider an approximation scheme for solving Markov Decision Processes (MDPs) with countable state space, finite action space, and bounded rewards that uses an approximate solution of a fixed finite-horizon sub-MDP of a given infinite-horizon MDP to create a stationary policy, which we call “approximate receding horizon control”. We first analyze the performance of the approximate receding h...

It is well-known that in Markov Decision Processes, with a total discounted reward, for instance, it is not always possible to explicitly find the optimal stationary policy f∗. But using the Value Iteration, a stationary policy fN such that the optimal discounted rewards of f∗ and fN are close, for the N -th iteration of the procedure, a question arises: are the actions f∗(x) and fN (x) necessa...