Probabilistic decision making system and methods of use
US8655822B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Mar 11, 2009 |
| Grant date | Feb 18, 2014 |
| Priority date | — |
| Expiry date | Dec 13, 2030 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q50/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of this invention comprise modeling a subject's state and the influence of training scenarios, or actions, on that state to create a training policy. Both state and effects of actions are modeled as probabilistic using Partially Observable Markov Decision Process (POMDP) techniques. The POMDP is well suited to decision-theoretic planning under uncertainty. Utilizing this model and the resulting training policy with real world subjects creates a surprisingly effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy. POMDP provides a more valid representation of trainee state and training effects, thus it is capable of producing more valid recommendations concerning how to structure training to subjects.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.