REINFORCEMENT, EXTENSION, AND CONSTRAINT DISCOVERY in Java

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Refine & Enhance Effectors Experience Environment User iCR Act R-Memory Novelty Observe Transcript Orient Match & Bind Response Decide Novelty Plan Match, Bind, Match, Score & Bind Interpret Annotate & Score
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FIGURE 4-13
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User reinforcement entails interpreting an errorful transcript.
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Thus formal reinforcement to Q-learning may proceed from iCR to an <Authority/> by a mutually agreed formal language to be assimilated as illustrated in Figure 4-13. 4.5.3 Reinforcement Via Natural Language
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Validation from a user requires skill with natural language (NL). Unlike formal languages, NL challenges radio engineering and AACR technology development. Most NL systems embed AML to some degree. As AACR evolves, the mix of preprogrammed versus autonomously acquired NL skills may shift toward AML for reduced cost of tailoring language to users. During the last ten years, NL technology has matured signi cantly. Commercial products like IBM s ViaVoice [66] recognize spoken language, creating errorful transcript hypotheses with as high as 75% to as low as 5% word error rates. Commercial language translation systems convert text among English, Japanese, Chinese, Russian, Arabic, and the Romance Languages with high reliability. VoiceXML [222] generates spoken language dialogs, driving any of a variety of speech-synthesis software tools. This section introduces a strategy for adapting these tools to AACR evolution that focuses on (1) overcoming the error rates inherent in current NL technology and (2) facilitating the insertion of NL components as the technology matures. To develop the role of NL in AACR, let us return to the <User/> domain and the map of the world. From a dictionary of English words <Self/> knows the two labels Land and Ocean as parts of Earth. An iCR can use the dictionary for reinforcement from the user as well, for example, substituting the dictionary de nition for a new word in a veri cation dialog such as: So the light blue means the solid part of the surface of the earth The user reinforces this expression for <Land/>. If the <User/> asks, How did you know that the CR responds, From the dictionary [Britannica 2003, noun]. For such a dialog, the errorful transcript must be suf ciently accurate to invoke <Land/>. If the CR infers the verb form of land instead, it could ask:
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So the light blue means to set or put on shore from a ship The <User/> negatively reinforces that de nition of land. The dialog designer is on thin ice at this point. While a few users may enjoy training an AACR, many will want to shut it off. Failures like this might steer the AACR toward a metalevel strategy to infer less deeply, ask fewer questions, and focus on simpler tasks. In the RF example, the <Self/> could ask a radio-aware user (e.g., a Ham radio operator) whether signal means indication or one of the more radio-speci c de nitions (all from the Britannica on-line dictionary):
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Signal a: an object used to transmit or convey information beyond the range of human voice b: the sound or image conveyed in telegraphy, telephony, radio, radar, or television c: a detectable physical quantity or impulse (as a voltage, current, or magnetic eld strength) by which messages or information can be transmitted
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Some users might not care, while others like Hams might prefer their own de nitions at odds with the dictionary. Such is the nature of technical domains. Therefore, iCRs may assert prior knowledge of <RF> <Signal/> </RF> in their interactions with users, expert or otherwise, to avoid being taught inappropriately. VoiceXML could mediate the following dialog: CR: You said that high values of the power spectral density represent signal correct User: (Says or types) Yes. CR: The term signal is a technical term in radio. To me, it means a detectable electromagnetic quantity by which messages or information can be transmitted. When using this term, I will refer to <Signal/> as s.i.g.n.a.l in written form and as signal-in-space in verbal form, OK If the user says anything but Yes, then the iCR may explain that there really are few alternatives for dictionary con icts on concepts with substantial a priori knowledge. Early adopters like Ham radio operators may enjoy such dialogs, while many users are confused, annoyed, and otherwise disenchanted with such dialogs. Thus, AML must continually detect <User/> attitude toward interaction. Positive reinforcement of training opportunities reinforces a strategy of interactive knowledge re nement, while negative reinforcement steers it away from aggressive user-domain learning with greater focus on simpler RF-domain tasks with more formal CWN interaction and less user NL interaction. The dynamic adaptation of strategies remains a research challenge in applied cognitive science. As illustrated in Figure 4-13, reinforcement from a user via NL ows from the microphone sensor through speech interpretation to yield the errorful transcript.
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