Abstract: Embodiments of this invention comprise modeling a team's state and the influence of training treatments, 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. Utilizing this model and the resulting training policy with teams creates an effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy.

Abstract: Embodiments of this invention comprise modeling a subject's state and the influence of training treatments, 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. Utilizing this model and the resulting training policy with subjects creates an effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy.

Abstract: Embodiments of this invention comprise modeling a subject's state and the influence of training treatments, 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. Utilizing this model and the resulting training policy with subjects creates an effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy.

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.

Abstract: A method for modeling a process comprising receiving a first formatted data input representing a process element and a second formatted data input representing a process element, executing a simulation and determining a measure representing the simulation of the process element given the entity element. In some embodiments, the process element represents a business process and the entity element represents a resource unit and the simulation comprises a temporal simulation. Some embodiments further comprise the measure being a completeness measure associated with a decay rate or a repair rate. Some embodiments also include the measure comprising an information product completeness measure. Some embodiments also include automatically determining the process element and the entity element. Systems, to include processor based embodiments having a computer program product to perform the methods are also disclosed.

Abstract: A motion-based system includes one or more passenger units, gimbaled about three axes, movably attached to arms or slots in a planar system extending radially from a central hub. The passenger units may be positioned along the arms any distance from the central hub thereby providing means for varying forces to be exerted thereon while maintaining a constant rotational speed. The mobile passenger units further provide means for loading and unloading subjects during operation of the system. The means includes passenger units being moved to the central hub location where they are disengaged from the rotating system and safely loaded and unloaded. Computers control the rotational speed of the system and the movements of the passenger units about at least three axes based on inputted or real-time data. The data can simulate real events, be arbitrarily developed or be based on real time events. The motion-based system has both training and amusement purposes.

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.

Abstract: The invention provides a barrel for a gun having a portion comprising a substantially transparent material. The barrel can be made of any substantially transparent material including a polycarbonate or glass, such as tempered glass. The barrel is coated with a substantially transparent material to add hardness, which provides scratch resistance, and prevent deterioration of the substantially transparent material. A barrel assembly is constructed by attaching the barrel to a gun housing, also called a barrel cage, comprised of a stiff material, for example metal such as aluminum. In another aspect of the invention, an expansion chamber is provided having a housing with a portion comprising a substantially transparent material, such as a polycarbonate or glass, such as, tempered glass. The housing is coated with a substantially transparent material to reduce scratching and absorption of pressurized gas into the transparent material.