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This chapter concludes with the architecture implications of the sensory-perception technologies (see Table 10-2). Interface 25 conveys speech and vision from user sensory perception to the cognition cycle. User speech is augmented in the perception subsystem and formatted into LCS expressions readily integrated by the cognition subsystem. Similarly, the visual scene is abstracted through the recognition of <Places/>, <Things/>, and <Paths/> in the visual scene, characterizing their presence and actions by LCS expressions for ease of integration in the Observe phase of the cognition cycle.
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10.1. Consider developing AACR vision in a realistic microcosm, such as that of model vehicles. ExtremeTech (www.extremetech.com) along with many other Internet (www.plantaco.com) and local hobby market suppliers offer mobile
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TABLE 10-2 AACR N-Squared Diagram for Speech Vision Sensory-Perception Action From To User speech User vision Environment user location Environment user acceleration Other sensors Speech synthesis User SP Phrases Scenes Scene reference points State change Features N/A Environment N/A N/A N/A Sys Apps Commands Attention Controls (pause/ resume) Interactive games Specialized Commands SDR N/A N/A N/A Cognition 25 PA 25 PA 26 PDC Effectors 31 32 33 PEM
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scale model cars, tanks, aircraft, tractors, and other specialty vehicles. These vehicles generally have little computational intelligence, but offer affordable mobility platforms for low cost autonomous vehicles. Since model vehicles entail a self-contained microcosm, they offer a manageable self-contained environment for experimentation with cognition technology. Acquire a digital camera for a model automobile and program it to guide the car around obstacles in the living room to the kitchen and back to you. 10.2. Web sites for the DARPA Grand Challenge (e.g., CMU s) illustrate the high end of ground vehicle technology. Between these extremes are vision-based control systems based on small optical elds such as the 5 gram Ladybug sensor from Centeye [256]. If you or your company would have liked to participate in a DARPA Grand Challenge, acquire a GPS receiver, cameras, and a commercial off-road vehicle like the John Deere Gator and develop your own off-road software. 10.3. What robotic subsystems, subsystems, or software could readily be adapted to AACR from GRACE Acquire a low cost computer vision subsystem. There are many min-Cams available for laptop computers. Use the API to capture isolated images. Download one of the contemporary machine vision systems from Universit t Kaiserslautern or Hamburg, Carnegie Mellon, or elsewhere on the Web. Extract blobs from your vision system using this software. De ne the technical challenges in recognizing your laboratory, home, and place of work autonomously. Write the code to differentiate outdoors from indoors based on blob and edge detection. Try the same problem using colors and light levels instead of shapes. Some PDAs include built-in cameras. How well suited are such devices as subsystems to be added to AACR De ne a migration path from PDA to vision-capable AACR. 10.4. Continuing in the spirit of GRACE, acquire an automatic speech recognition software system. Without teaching it, characterize its ability to recognize conversational speech by conducting a 5 minute conversation about the stock market with someone in its presence. A day later conduct the same 5 minute
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conversation with a different person. Characterize the error covariance between the two conversations. Are the errors and successes speaker dependent Move the platform to a different setting, such as to a crowded living room with the TV on and people talking. Conduct the same 5 minute conversation with the ASR system operating. De ne enhancements to the microphones, acoustic signal processing, and ASR system that the ASR s API or training capability enables. Make the hardware and con guration changes that you de ne. Train the ASR system to your voice and conduct the previously mentioned tests. Characterize the word- and phrase-level error rates. Give the AACR system a name and during the 5 minute conversation, ask the AACR for help at least three times, using its name. 10.5. Download a speech synthesis system and integrate it with your emerging AACR now consisting of laptop, vision, and speech recognizer. Write high order language script that enables it to nd a RXML open tag <. . .>, not <. . ./> as a keyword in CRA <Self/>, and to read the contents delimited by the tag out loud. 10.6. Write glue code that will patch the ASR output to the speech synthesis system so that if the ASR system recognizes a question of the form Computer, what does X mean it will look up <X> in CRA <Self/> and read the contents out loud. Test it with questions about radio. Characterize the kinds of errors this crude question answer system makes. Ask it increasingly complex questions and note the ways in which it breaks. De ne methods to resolve these problems. Write code that enables the AACR to remember the sentences that precede the request for query. Give the AACR data needed for the query. What is needed to link the information to the query Use the techniques of embedded inference or PROLOG to mitigate the problems by planning dialogs in which the AACR recognizes that it must ask you for more information in order to successfully complete the query.
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