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exploration and addresses the question of how best to mix exploration and exploitation [92]. The AACR mix depends on <User/> and <Scene/>. De ning a Mix of Exploitation and Exploration Suppose an AACR can generate a plan to perform one and only one of the following actions at a time: Task# 0 1 Action Be quiet and do nothing unless Owner clearly asks for a known <Action/> Perform an <Action/> using its associated parameters: (a) Ask about default parameter values of an <Action/> (b) Ask about user-speci c <Action/> parameters Ask about potentially relevant <Actions/>, or Ask about <Observations/> of the <Scene/> (a) Ask about <Novel/> <Observations/> (b) Ask about reinforced <Observations/>
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The question of which <Action/> to try in uences the acceptability of the CR to the user. We want the <User/> to like the CR. Each task has a value, for example, de ned in Q-learning by Quality(state, action). An incremental update rule ascribes a new value to the action in the given state, Quality(state, action) as a function of the reinforcement of the anticipated action, plus the expected value of the other possible actions from that state. Qk +1 = (1 ( s, a )) Qk ( s, a ) + ( s, a ) f ( s, a, ( max b (Qk ( s, b)))) (11-1)
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If the maximum value of Q(s, b) dominates, then the decision favors action b, an exploration of insuf ciently reinforced alternative actions. Thus, if the CR makes an observation, for example, that WXYZ is an Oldies station, the CR can just be quiet, Action-0, or it could perform some other action. If the CR is quiet and does nothing, then the inference that Owner likes Oldies does not occur. If the CR always makes this choice, then reinforcement from the environment accrues as the Owner repeatedly tunes to WXYX, but the link to Oldies is implicit rather than explicit. Suppose the CR has an initial learning fraction of (s, a) = 0.5. Reinforcement accrues for positive responses. If the value of Action-0, saying and doing nothing, is reinforced because of the lack of negative reinforcement, then the CR will just sit there and not try anything else. To overcome this class of side effect, exploration is randomized. Randomness for Exploration Randomization is an arti cial mechanism to assure that behaviors are attempted so that the estimated value of the behavior from a given state rep-
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resents reinforcement. One way of exploring alternatives is to cycle randomly through the candidate behaviors for some period of time or number of occurrences, computing the new value after an evaluation time T. An alternative would be to try each in turn some number of times N. By forcing behaviors to occur, the relationship between candidate actions and the environment has a degree of correlation. Unfortunately, the <User/> may not be thrilled with the promise of exponentially effective behavior over in nite time if the AACR is perceived to be untrainable at the outset. Thus, an approach to getting more <User/>-centric behavior early in a training process would be to initialize the UDHs by asking the new Owner questions that extract broad strategies as early in the training process as possible. The CR could ask metalevel questions such as Would you prefer it if I talk a lot or keep quiet If the <Owner/> replies, That depends, . . . then the AACR could randomize behavior to discover the limits. In particular, in the algorithm of Equation 11-1, initializing all of the values high and providing small incremental reinforcement causes the system to search, exploring rather than exploiting success because alternatives seem better than initial values. Conversely, initial values of zero reduce exploration, driving decisions toward generating plans that use whatever tasks are rst given positive reinforcement. In the ML literature, there are different methods for dealing with tasks that are performed continually versus episodic tasks (see [92] for the algorithms and mathematical analyses). 11.2.4 Extensibility Through Temporal Reasoning
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RL includes the temporal difference algorithm, which is helpful in its own right in the implementation of <User/>-domain skills. Following [92], one may learn by noticing differences of the state of the environment over time. This requires algorithms that employ time as a universal index, that compute equality versus differences, and that associate time, equality over time, and differences over time in a way that autonomously extends the AACR s knowledge and skill. The CRA formalizes time as a schema-schema from which a temporal reasoning strategy may be bootstrapped. <Time/> as a Schema-Schema RXML expresses space time as fundamental, with both space and time as abstractions that cannot be independently experienced, but to which one can refer as a dimension of experience. Expression 11-10 Space and Time Are Subsets of Space Time
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<Place> <Space-time/> <Space/> <Time/> </Place> <Place> <Space-time/> <! Space-time is the fundamental domain . . . > <Space/> <! Space is an abstract subset of space-time . . . >
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