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LCS sketch of person hears TV program.
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traversal of such primal sketches to address such con icts appears orders of magnitude more manageable, although not trivial either. Speci cally, to manage concrete computational complexity, the CRA <Self/> envisions LCS or equivalent primal sketches as the primary means for representing scene-level perceptions from multiple sensory-perception tracks over space and time. Conversations in a room may be realized as broadcast channels in which everybody can hear everything, or in a larger room such as a banquet hall where the intensity of the background conversations render only the nearest people as conversational partners. Each sensoryperception channel participates in the population of observations into the <Scene/>. All <Scenes/> encountered are remembered and during <Sleep/> cycles they may be merged or taken apart to overcome such problems, to enhance future behavior. Such sketches enable AACR software to deal more effectively with the complexities of scenes. In particular, a wearable algorithm need only examine the <Path/>s attached to <Person/> to determine what sounds it should hear. If there are ten objects in the room (a couple of chairs, some toy trucks, two doors and a window, a vase full of owers, and some wall hangings), then in a logic system, there are (10 factorial) 72.5 million inferences to be made about the way sound impinges on an object. Since the number of inferences grows exponentially with the number of objects, complicated scenes with many predicates require substantial space time computing resources. Although suitable for CWN and potentially for AACR <Sleep/> cycles, such logic formulations appear to be less suited to real-time behavior support than LCS primal sketches. Consider the problem of navigating an AACR through a cityscape that is full of WLAN enclaves, viewers of weak TV stations, and the like, which the AACR s private WLAN must avoid (e.g., in the Genie use case). Once at home, the AACR docks wirelessly into its own Home RF environment, which is the goal of the transit from work to home. This is not unlike guiding a submarine through a mine eld to the home port. The penalties for making mistakes in the radio domain may not be as immediate, but they could be severe. The mine eld domain consists of a perception space, an action space, and a set of interactions including successful navigation and the consequences of striking a mine. The perception and action spaces are as follows:
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simpli ed mine eld perception action spaces Perception Space Condition primitives (16) Sensors: sonar [7] and infrared [6] Range and bearing to goal Current speed and steering rates Action Space Movement primitives (2) Controls: throttle and wheel Speed ( 4 to 12 in./s) Steering rate ( 30 to 30 /s)
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The process of evolutionary computation [283] derives both reactive and symbolic control laws from a control genome that includes neural networks and symbolic primitives that may be composed into control laws. In a simulated environment [53], a robot is placed at an origin with random pose, with random placement of obstacles and goal, and with random obstacle density. Noise, including Gaussian noise, false positives, and false negatives are added as populations proceed toward the goal. The simulations are massively parallel, enabling a population to reach the goal in time t(i, x). The population is thus pruned, and the survivors are randomly mutated and combined to yield a next population of more t individuals. When a suf cient performance has been reached or simulation time has been exceeded, the most effective control law is downloaded. The primal sketch of the mine eld is illustrated in Figure 11-2, with the size of detected mines re ecting the uncertainty of the position of the mine. In the mine eld application, the entity has only an incomplete map of the scene, relying on local sensors for discovery of both the mines and the goal. The AACR application of Sun s mine eld avoidance control schema to interference minimization requires representation of the <Self/> in an appropriate primal sketch. A GPS-based estimate of the position of the <Self/> in an urban scene has the drawback that GPS often glitches in such locations. A visually capable automobile that reads road signs or an AACR that reads the metropolitan BlueTooth <Location/> service at each street intersection could navigate by GPS aided by a computational model of user movement plus a sketch based on streets and street intersections. The associated primal sketch could refer to places with the city of Metropolis in terms of street intersections:
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