THE serMODEL OF MACHINE LEARNING FOR iCR in Java

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THE serMODEL OF MACHINE LEARNING FOR iCR
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automates dynamic spectrum management through positive and negative reinforcement of iCR-synthesized behaviors, not requiring all cases to be preprogrammed. 2.4.2.1 Waveform serModels Consider stimuli that indicate the need for a given waveform. The serModel forms pairs of attributes of waveforms with attributes of the user s information needs. Augmenting the RXML self-description of <GSM/> of Expression 2-9 yields the following: Expression 2-10 Waveforms Have RF and User-Centric Stimuli
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<Waveform><GSM><serModels> <RF>5<Stimulus>GSM-control-channel-active </Stimulus> <Response>Send (Registration-request) Pay (Airtime) </Response> </RF> <User><Stimulus><AND>Desires (User, Speak (Remote-entity)) <NOT>VoIP</NOT></AND></Stimulus> <Response>Initiate-GSM-voice-call</Response></User> </serModels> Expression 2-10 associates the chip set GSM control channel to the registration request explicating air-time charges. The association of connection cost from the <User/> value domain enables this cost-aware iCR to defer the connection until <User/>-sensory-perception indicates a need strong enough to pay for GSM. The serModel re ects the <User/> value that GSM is appropriate when the user wants to speak by voice to a remote entity and VoIP is not possible. The iCR may reason over this data structure to plan a response to initiate a voice call over GSM. The serModels re ect the goal-oriented nature of iCR support to users as well as the causality in radio networks in a way that is readily extensible via AML to optimize the <User/> QoI metric. The user s expressions for Speak are grounded to <Speak/> for the RXML above. In some bands, like marine VHF, speech may be the only available mode. GSM s richer set of capabilities, including Short Message Service (SMS), EDGE, and cell-phone email, may be related to QoI via RXML as above. As new services arise with increasing frequency, iCR serModels learn <User/> preferences for snapshots, video clips, video-phone, chat rooms, and the like as a function of user <Scene/>. The serModels expressed in RXML readily extend to new services relating GoS, QoS, and QoI to <User/> preferences. The serModel thus encapsulates the waveform as a means to an end rather than an end in itself.
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<RF/> invoked within <srModel/> appears self-referential but since <RF/> refers to the outside world, not to the <RF/> structure of the <Self/>, there is no logical inconsistency when referents are accurately grounded.
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2.4.2.2 serModels in CBR As introduced previously, pattern matching with machine learning organized as case-based reasoning (CBR) could implement causal relationships. CBR remembers the stimuli, recognizes similarity between <Scenes/> past and present, binds contextually similar referents, and offers to apply the adapted prior serModel-encapsulated experience to the present <Scene/>. CBR-based serModels are the basis for AML implemented in Java in CR1 in the conpanion CD. In one vignette, someone says, Let s call Grandma on our new CWPDA immediately before the rst GSM call is placed. The CWPDA observes this and Hi, Grandma as the salutation of the call. Its serModel associates the phone number that the new user dialed into a tentative phone number listing under the name <Grandma >. The salutation reinforces the association of that <Telephone-number/> and <Grandma/>, leading to a question from the CWPDA to make the association permanent. The stimulus speech segment Let s call Grandma and the response <Dialing/> the <Telephone-number/> are encapsulated by CBR as a serModel by the successful reinforcement by the <User/>. Subsequently, the CWPDA observes in the speech sensor-perception channel the phrase Let s call Uncle Charlie. CBR binds the phrase Let s call in the Uncle Charlie and Grandma scenes as a pending <Phone-call/> event. CBR binds <Grandma> in the prior serModel to <Uncle Charlie >. As a response, it looks up Uncle Charlie s number in the phone book, placing it in the default <Telephone-number/> to <Dial/>, with the text Uncle Charlie in the eld indicating the name to call. The new CWPDA user would save time if the anticipated call were accurate. If not, the new user dials the correct number, and the CBR is negatively reinforced if the user dialed a different number, turning the attempt into a further learning experience. The interaction of such positive and negative reinforcements is a chronic AML research area, so it will take time for serModels to be fully understood and effectively employed in practical devices. For the detection of speech precursors to phone calls to be practical, the <iCR-platform/> needs high quality acoustics and accurate speech recognition, still at the cutting edge of speech research. 2.4.3 Planning
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The serModels suggested above explicate causal links among stimuli and responses. Beginning at least with means ends analysis [59] and the classic problem of the monkey and the bananas [89], computer scientists have addressed such planning problems in a myriad of ways [290]. The serModel may be thought of as an interactive macro-operator discovery method in which the <User/> performs a sequence of steps that achieve a goal and the <Self/> encapsulates that sequence in a serModel as a means end relationship. This isn t to say iCRs should not embed a full planning system like the open procedural reasoning system (OPRS), for example [60]. As the com-
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