Abstract: A method for adjusting the apparent brightness of a computer-generated hologram display is disclosed. The method comprises: receiving source data representative of a scene to be displayed as a hologram; determining hologram data to display a computer-generated hologram representing the scene; determining a scene energy based on the source data, the scene energy being quantised using a scale which is non-linear and which has a closer spacing between values in a mid-section of the scale than between values towards a minimum and a maximum of the scale; associating the scene energy with the hologram data; controlling a holographic display according to the hologram data and simultaneously controlling an output power of an illumination source of the holographic display according to the scene energy. A holographic display apparatus implementing the method is also disclosed.

Abstract: Adaptive agents are driven by rewards they receive based on the outcome of their behavior during actual game play. Accordingly, the adaptive agents are able to learn from experience within the gaming environment. Reward-driven adaptive agents can be trained at either or both of game-time or development time. Computer-controlled agents receive rewards (either positive or negative) at individual action intervals based on the effectiveness of the agents' actions (e.g., compliance with defined goals). The adaptive computer-controlled agent is motivated to perform actions that maximize its positive rewards and minimize is negative rewards.

Abstract: A mesh model may be divided into nontrivial meshlets that together form the mesh model, where each meshlet has a single associated render state and some meshlets have different respective render states. Cost metrics are assigned to respective render state transitions, where each render state transition comprises a transition between a different pair of render states. A render state can be anything whose modification in a renderer incurs a slowdown or cost in the renderer. The cost metrics may be provided to an optimization algorithm that automatically determines an optimal order for rendering the meshlets. That is to say, an order for rendering the meshlets that optimizes the cost of changing render states when rendering the meshlets.

Abstract: Adaptive agents are driven by rewards they receive based on the outcome of their behavior during actual game play. Accordingly, the adaptive agents are able to learn from experience within the gaming environment. Reward-driven adaptive agents can be trained at either or both of game-time or development time. Computer-controlled agents receive rewards (either positive or negative) at individual action intervals based on the effectiveness of the agents' actions (e.g., compliance with defined goals). The adaptive computer-controlled agent is motivated to perform actions that maximize its positive rewards and minimize is negative rewards.