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Using Barcode Control SDK for Java Control to generate, create, read, scan barcode image in Java applications. Mutual Ontology Mutual grounding is particularly critical for human computer collaboration [313]. Suppose the user asks the PDA: Will it rain today The PDA starts to play back the latest NOAA weather broadcast. It has inferred <Rain/> as a weather state, and it knows (e.g., from the CRA <Self/>) that NOAA weather broadcasts are a source of <Weather/> <Information/>. <Place> <Time> <Earth> <Land/> <Sea/> <Weather/> </Weather> </Time> </Place> <Information> <Weather> <Rain/> <Shine/> <Snow/> <Severe/> <Radio> <Broadcast> NOAA <News/> </Broadcast> </Radio> </Weather> </Information> The user says, No, I do not want to hear the NOAA broadcast, I want you to tell me the bottom line. Has the PDA successfully communicated with the user It seems not. Success in such human computer communications depends on precise mutual grounding, the alignment of the conceptual semantics of the two cognitive entities. Initial AACR products may get away with playing the latest NOAA weather when asked such a question, but advanced iCRs would analyze the NOAA weather broadcast themselves. In this NOAA dialog, the <User/> wanted a yes or no answer to the question, not a lot of data from which to draw a conclusion. The CRA <Self/> includes primitives from which the AACR could have created the following LCS-annotated sentence and drawn the primal sketch of Figure 11-9: <Question> <Place> <Time> Will <Future/> <Action> <Weather> it rain <Rain/> </Weather> </Action> today <Today/> </Question> In Figure 11-9, the type of question is inferred from its structure. Other types of question are rhetorical, requiring no answer; some ask for an explanation. Words like how generate an <Explanation/> goal, while will
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Primal sketch of question about rain.
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generates a <Yes/No/> reply goal. Such a semantic sketch could be converted to a database query against the most recent transcript of NOAA weather, yielding either a crisp yes or no, or a NOAA-like answer: 50 percent chance of rain in the counties of . . . . Mutual grounding is often mediated by the grounding of internal symbols to concrete counterparts in the external environment via sensory perception and volitional action systems in which the cognitive entity explores the environment in order to ground a symbol. The user extends his palm through the open screen door, feels the wetness, and says It is raining. The CR on his belt looks at the concentric circles in what it infers to be a puddle in the lawn and infers It is raining. Although the user and AACR rely on different perception systems, both cognitive entities established a well-de ned internal state <Weather> <Rain/> </Weather> through interacting with the same external experience. They also use mutually mapped internal representations of that perception, <Rain/> for the AACR which translates to rain in the speech synthesizer. Grounding Errors Grounding errors can include the following: 1. Abstraction Failure: Failure of one entity to accurately express an internal state. 2. Communications Failure: Lack of access of an intended recipient to the expression. 3. Misinterpretation of a perceived expression. 4. Disagreement: The unwillingness of each to align to the other. Metrics have been described which express an agent s inclination toward a communications act to correct a detected grounding error [313]. Stateconfusion matrices compare the internal states of two agents that are supposed to be sharing identical internal states. Such matrices characterize the success of communications, but do not diagnose the underlying agent agent or human machine communications failure. Additional metrics might (1) express the relative importance of internal states with respect to the task at hand, and (2) express the agent s ability to know its own internal state. Most internal states of conventional software-de ned radios (SDRs) are not computationally accessible to another SDR. Although internal states of cognitive radios would be accessible, metalevel states might not be. Predicates and reasoning over metastates seem to be needed to quantify whether agents have the capabilities required for grounding. An AACR that learns from its user constitutes a collaborative problem solving domain with the CRA <Self/> as a metalevel problem solving framework. Within this framework there may be different forms of con ict in the alignment of primal sketches. Sometimes the alignment of the sketches is the root of the problem.
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