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<Scene/> with time of observation. There are myriad uncertainties and frame alignment challenges for a <Scene/> integration algorithm to restart a <Scene/> after it has been lost for a threshold period of time (e.g., a minute, to prevent thrashing hypothesized new scenes when GPS is aky). 11.1.1.1 Scenes and Microworlds The CRA does not specify linkages across scenes, but postulates microworlds and linkages among microworlds, with some CRA <Self/> exemplars. To preserve LCS, a new scene may be formed when a new <Sketch/> needs to be generated; for example, because the <Place/> has changed, the <Self/> has no anchor points, the <Self/> senses movement along a <Path/>, or the con guration of an <interior/> has changed through actions like rearranging the furniture. Thus, <Home/> would be a microworld with each <Room/> and separate space as a <Scene/> with an associated LCS <Sketch/>. 11.1.1.2 Time Structures Perception Into Observations and <Paths/> Perception at a speci ed time is an <Observation/>, asserted into the current <Scene/> as it is perceived. If suf cient time passes, a watchdog timer terminates reasoning over unrecognizable features linked to the <Scene/> to manage combinatorial explosion. In a sequence <Vision Time = 38394.5066235451> <Tree/> <Grass/> </Vision> and <Vision Time = 38394.5067583321> <House/> </Vision> the time tag enables the cognition system to construct a space time path with trees, grass, and a house. If the AACR knew from the in-car BlueTooth that it had just been in a car, it can construct an embryonic <Path/>: Expression 11-2 Embryonic Path From Car to House
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<Path> <Place> Car </Place> <Time t = 0> <Scene ID = <Owner s-car/>/> </Time> <Time t = 1> <Tree/> <Grass/> </Time> <Time t = 2> <House/> </Time> <Where /> </Path> The path has not yet ended, so it terminates with the concept <Where />, asserting that the end of the path is not known. Such RXML paths can be traversed in either forward or reverse directions and when something is recognized to complete a loop, the AACR knows the <Path/> along the loop. By counting the reoccurrence of such paths, the AACR learns user paths through simple reinforcement. In the primal loop <Home/> <Car/> <Work/> <Car/> <Home/>, there are many small loops, for example, from car to <Grass/>, <House/> and back. 11.1.1.3 Novelty Suggests Scene Boundary Hypotheses If the perception system detects strong <Novel/> features of a known <Class/> of <Scene/>, a new <Scene/> may be asserted as a KO for subsequent AML
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to balance the combinatorial explosion of recognition processing against isochronous interaction with the <User/>. Expression 11-3 Owner Enters Home for the First Time
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<Scene ID = 2> <Vision Time = 2/11/2005 12:20:59/> <Novel> <Thing> <Chair> <Position> <Distance> 4.5 <Meters/> </Distance> </Position> <Color> Brown </Color> </Chair> <Floor> <Position> <Distance> 2 <Meters/> </Distance> </Position> </Floor> <Couch> <Position> <Distance> 3 <Meters/> </Distance> </Position> <Color> Brown </Color> </Novel> </Thing> <Unrecognized> <Blob> 3242 <Color> RGB . . . </Blob> </Unrecognized> </Vision> <Speech Time = 2/11/2005 12:20:59> <Transcript> hi honey . . . I m home </Transcript> <LCS> <Thing> honey </Thing> <State> I m </State> <Place> home </Place> </LCS> <Speaker ID = <User> Joe </User> </Speech> <Place> <Hypothesis> <home/> 0.25 </Hypothesis> </Place> </Scene> A ood of <Novel/> <Thing/>s of known types reinforces the hypothesis that the <Scene/> has changed, for a new <Scene ID/>. Although still computationally intensive, vision algorithms can detect, recognize, and track known types of objects in such visual scenes to yield a <Sketch/> of <Novel/> but recognizable new <Thing/>s. As the <User/> moves through the scene, some previously unrecognized <Blob/>s may be recognized, causing the recognized <Thing/> to be asserted and the <Novel/> <Blob/> to be retracted. As a new owner proceeds to different rooms of the house, new <Wall/>, <Picture/>, <Door/>, <Window/>, and related interior <Things/> and <People/> will be sensed. Over time, with reinforcement and with <Sleep/> cycles, the AACR with evolved UKOs and UDHs for aggregating <Scenes/> repeatedly experienced over time may converge on <Home/> <Scenes/> populated with <Things/> and transited by <People/> and other animate entities like pets, some of which the AACR will assist with information access via typical conversations that reoccur in the scene. Awareness can be thought of as a process of mutually resolving noisy and con icting sensory perceptions across auditory, visual, and navigation sensors into a data structure that describes a very complex scene in terms computationally workable for use cases such as a LCS <Sketch/>. Scenes and the microworlds that they describe vary over time, yet, the iCR must generate plans, make decisions, and take appropriate information access actions on
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