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Expression 11-12 Nominal Plan for Transit Home <User> <Plan> <Place> Work <Time> 5 pm </Time> </Place> <Place> <Service> <Cellular> <Time> 5 pm <Interval> 10 minutes </Interval> </Time> </Cellular> </Place> <Place> <MIMO> <Time> 5:10 pm <Interval> 20 minutes </Interval> </Time> </MIMO> </Place> <Place> <Cellular> <Time> 5:30 pm <Interval> 10 minutes </Interval> </Time> </Cellular> </Place> </Plan> </User> When the <User/> is delayed, the Value(Depart work, Arrive Home) increases with respect to the original start time of 5 pm, initially extending an hour until the <User/> actually departs the of ce, moving the <Plan/> to 6 pm. The <User/> talks to a colleague in the parking lot, further extending the plan. With TDL in this policy veri cation mode, the initial estimate of an immediate departure is updated to Value(Depart of ce) = 68 minutes, up from zero for this particular instance. The next time the <User/> says he will depart at 5 pm, the Value(Depart of ce) could re ect either the nominal plan Value(Depart of ce) = 0, for an immediate departure (k = 0, or no aggregation of experience), or it could re ect the most recent departure of 68 minutes delay (k = 1, no credit for planning), or it could re ect some fraction 0 < k < 1 of the old plus the new, updating the policy to re ect experience modulated by a degree of relevance. This is a direct application of the theory of TDL. A CR can use <Scene/> features to deal with such discrepancies between a policy and experience. 11.2.4.3 Context for Temporal Differences Since the AACR continuously examines sensory-perception channels, it need not be limited to the manipulation of k as the only controllable feature in TDL. Instead, since the temporal plan of Expression 11-12 sets the framework for the TDL algorithm, the TDL and <Plan/> may be integrated. Activity 2 of Table 11-4 itself de nes a <Place/> with <Extent/> in the <Time/> dimension. That activity entailed an increase of Value(Depart work) from 0 to 68 minutes. Although increasing context sensitivity has the potential to increase QoI to the user, it also has the possibility of contributing to a combinatorial explosion of hypotheses and therefore must be undertaken with steps that avoid combinatorial explosion, for example, by relegating analysis of such patterns to <Sleep/> cycles or to a CWN. 11.2.4.4 Avoiding Combinatorial Explosion in TDL TDL can t be implemented as-is for large problems, however. For example, in games with few states like tic-tac-toe, all moves from all states can be computed and remembered. For even relatively simple games like checkers
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(played on an 8 8 board, each square of which may be occupied by black or red or unoccupied), the number of board states is 265 or 3.69 1019. Today s memory limit for a laptop is about 1 billion (109) states (2 GB of memory), which is ten orders of magnitude too small. The generation of such states occurs in the <User/> domain when CRA <Self/> templates are interpreted as schema-schema. An important way to mitigate combinatorial explosion is the aggregation of information over time, setting relatively high thresholds for UDH pattern detection algorithms to spawn UKOs. An alternative to unacceptably large numbers of states is to use partial data or side information to assess the likely quality or value of a state. A mathematical heuristic is a state evaluation algorithm that runs quickly to completion and that bounds the cost of reaching the goal from a state. The classic AO* algorithm uses such mathematical heuristics to perform branchand-bound partitioning of large state spaces to eliminate from further consideration those that are unlikely to get to a goal more quickly than the best current plan [196]. In UDH and RDH such mathematical heuristics can manage combinatorial explosion of TDL. Reinforcement may be normalized or not. For example, CR1 counts reinforcement by integer values, remembering that the <User/> listened to WTOP for news once this hour, while listening to traf c and weather four times. TDL with nonzero k and Q-learning normalize the reinforcement to 1.0. Learning about WTOP as either a news channel or a weather and traf c channel may be accomplished via either of the following: CR1 Q (Preferences (news, 2), (weather, 4), (traf c, 4)) (Reinforcement (news, 0.2), (weather, 0.8), (traf c, 0.8))
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Whether normalized or not, these metrics re ect uncertainty about the <User/> in the environment. The technologies for estimating certainty, uncertainty, and degree of belief are informed to some degree by the theory of probability, but there are many open issues. Nevertheless, the robust AACR must deal effectively with degrees of certainty and uncertainty as well as degrees of belief and other fuzzy aspects of the <User/> domain. Explicit supervision by <User/> or CWN also manages uncertainty.
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