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Clairvoyant machine

Coordination. speech recognition. T-T-S synthesis. CM project. Cognitive Robotics. Talking heads. AI. Robotics. Cognitive science. Graphics. Lingu-bots. Mind-bots. VR avatars. Knowledge modeling. Info-retrieval. Clairvoyant machine. CM: why?

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Clairvoyant machine

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  1. Coordination speech recognition T-T-S synthesis CM project Cognitive Robotics Talking heads AI Robotics Cognitive science Graphics Lingu-bots Mind-bots VR avatars Knowledgemodeling Info-retrieval Clairvoyant machine CM: why? Test-bed for many technologies developed in our School.

  2. CM machine: what? What should it be? talking head with TTS + speech recognition; lingu-bot able to have some dialog; first simulation than robot; not too ambitious at the beginning, with potential for growth 20 questions game – non-trivial if not restricted to animals/minerals or plants, may start from simple domains. Other games of this sort? Trivia games? Reading books? PC assistant? UltraHal is on the market. MIT Start system, web-based question answering system. Perhaps best for science?

  3. CM: how? Quick and dirty: put together ready-made components for Cyberworlds in 2 weeks, for Science Museum for Christmas? V1: Haptek virtual friend + speech recognition + play 20Q over the Internet ? Feed text scripts to Haptek, work out scripts that correlate words with emotions and head movements. Requires: javascript + VB programming Challenge: integration of different techniques. Haptek + speech recognition may be commercial. Game may be on the Internet, responses are read loud and entered via speech.

  4. CM: evolution. Add more games, answers to questions about science, geography, movies etc (filtering MIT Start information). Add your own elements: heads + graphics + lips synchro + speech synthesis etc, whenever we can improve CM. Put it on robot, add camera, recognition of face/sex, ex. say ‘Hallo pretty girl”, or ‘Hello Wlodek’ etc. Add bot to talk about NTU, museum, or other specialized subject: lingubots from Kiwi logic? Add your own 20-Q game, there are many realizations, theories of semantic memory may be tested this way. Michalski had a machine learning system discovering rules from examples, touring eight U.S. Museums of Science.

  5. CM: AI + cognitive aspects Development of mind-bots: bots are stupid, do not reason, have problems with understanding simple sentences. Use ontologies + OpenCyC NL to understand more. Mindbots: based on more sophisticated model of mind. Use SOAR-NL, for problem solving, language & learning. Use ACT-R to model user’s mind, good for tutorials. Developmental approach: Ai baby mind Alan and Hal ? In a robot?

  6. ??? Links to bots and more are at: http://www.ntu.edu.sg/home/aswduch/ai-ml.html#Agent These notes: http://www.ntu.edu.sg/home/aswduch/ref/CM1.ppt

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