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similar items may participate in a variable value relationship where one acts as a label for the other (for corresponding but dissimilar-match binding). If a speci c action is accurately matched and bound to the associated feedback, scoring yields reinforcement learning (RL). Successful reinforcement autonomously annotated (symbolically or with additional types of scores) facilitates metalevel retrieval, adaptation, and bootstrapped learning. The retrieval, binding, using, and scoring of relevant experience generalizes CBR [117] for the CRA. Established reinforcement learning (RL) algorithms produce an adaptation policy from actual or simulated experiences. RL methods include time difference (TD) [92], dynamic programming (DP) [118], Monte Carlo methods [92], and Q-learning [119]. All RL methods associate rewards, values, or quality with a state or state action pair, creating a policy that speci es a preferred action for each possible state. For example, Q-learning estimates the quality of candidate action a in state s via Q(s, a): Qk + 1 = (1 (s, a )) Qk (s, a ) + (s, a ) f (s, a, (max b Qk (s, b ))) (4-4)
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The weight (1 (s, a)) determines the degree of exploitation of current knowledge, while f shapes the search for new knowledge at the rate (s, a). As the number of iterations approaches in nity, Q approaches the optimal dynamic-programming policy with probability 1 [120]. Q-learning applies to call-admissions control in simulated networks [121] and robot control [122], among others. Call-admissions control has well-known mathematical structure and the possible states and actions are known in advance. Similar algorithms for such well structured domains appear in state space automatic control [123], xed-point maps [124], Kuhn Tucker optimality, and GAs [74]. These methods may not apply readily to open domains like <User/>-speci c jargon or a change in daily commuting pattern. Open domains are relatively unstructured, constantly admitting novelty, and thus somewhat out of reach of classical RL, automatic control, and optimization. In the AACR <User/> domain, the primary measure of goodness is whether the inconsistent and ckle user thinks the CR is good or not. Computational ontologies and structured dialogs assist in adapting RL to AACR. The technology is brittle, so the architecture must accommodate apparent suboptimality and contradiction, tracking in space time the <User s/> changing needs and QoI patterns. Such AML applications are embryonic, so the text characterizes candidate technologies, approaches, and research issues, not pretending to offer closedform solutions. The plan decide act components of iCR in Figure 4-12 naturally align to RL, and the CRA does not preclude classical RL methods. However, although the properties of RL suf ce for closed domains like games [125] and avoiding undersea mines [53], they are not well understood for open domains. For example, Q-learning often falls off cliffs in the cliff-walking problem using greedy methods to discover penalties associated with moves. One AACR
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Refine & Enhance Effectors Experience Environment Authority iCR Act R-Memory Novelty Observe Message Orient Match & Bind Response Decide Novelty Plan Match, Bind, Match, Score & BindReinforce Annotate & Score
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Formal messages are interpreted in the CRA.
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equivalent of falling off a cliff is the violation of regulatory policy, so the FCC may not appreciate classical Q-learning. Other methods like SARSA [92] that do not violate policy converge less rapidly, perhaps causing user frustration over the time spent learning simple preferences. Therefore, the sequel tailors classical RL methods to iCR towards AML without unacceptable penalties in <User/> acceptance or <RF/> regulatory viability. If the iCR structures its behavior to obtain feedback, then it is engaging in active reinforcement learning. Game playing programs that look ahead to reachable states from a given board state actively learn the bene t of the situation action pair. Otherwise, a RL algorithm may learn the relationship between action and bene t without modeling the environment, instead using the actual environment as the model, for passive RL. Formal, informal, and CBR feedback assists RL in <User/> and <RF/> domains to develop, re ne, and apply acquired knowledge. 4.5.2 Formal Reinforcement
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One may envision in Figure 4-12 a path of reinforcement that minimizes learning errors. Formal reinforcement employs formal language for reinforcement from a validating authority. Formal languages avoid error sources of natural language (e.g., ambiguity) particularly with computational complexity of Chomsky s Level 2, context-free language parsed by push-down automata (PDA), or Level 1 nite state language parsed by nite state machine (FSM). Formal languages for RL feedback include KQML [126], DAML/OIL/ OWL [127], JADE [128], and RKRL [145]. A KQML-like query to validate a <Signal/> learned by <Histogram/> might be as follows: Expression 4-20 KQML-like Request to Validate New Knowledge
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