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Kant, Ravi
- Ascertaining Cognition Abilities of 1st Generation Cognition Robot using Bayesian Models
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1 RKA Technologies and Consultants Pvt. Ltd., Kanpur,, IN
1 RKA Technologies and Consultants Pvt. Ltd., Kanpur,, IN
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Indian Journal of Automation and Artificial Intelligence, Vol 1, No 2 (2013), Pagination: 44-50Abstract
There are number of challenges involved in programming cognition into a robot, e.g. getting the robot to learn about its architecture, being aware of the things around it, acquiring knowledge by itself, and most importantly carrying out trivial tasks like responding to flash lights or running away from the fire are the kinds of activities the robots should be able to do on its own. Given that a large set of variables involved in performing trivial tasks are presented to it, could it derive the relationships between them? Using Bayesian or in other words the belief networks a model was developed to ascertain the level of cognition skills acquired. The 1st generation cognition robot based upon a popular atmega32 microcontroller was designed, as a platform to carry out a number of artificial intelligence experiments. Experiments were aimed at relating, mere three sensors with those of stimuli and drawing up a Bayesian network with relevant weights. By repetitive subjection to stimuli, the robot was able to build the network as desired. Further, to ascertain the cognition abilities, 20 relations that formed a branch in the network, were queried multiple of times to find if they conform to the correct response. Data showed that, probability of occurrences of a particular branch being true has a regression fit of 0.73 with the desired response, suggesting that indeed the robot has acquired certain level of cognition.Keywords
Bayesian/ Belief Networks, Cognition, AVR Microcontroller, Robot CognitionReferences
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