By Ahmed M. H. Abdel-Fattah, Tarek Besold (auth.), Joscha Bach, Ben Goertzel, Matthew Iklé (eds.)
This e-book constitutes the refereed court cases of the fifth overseas convention on synthetic basic Intelligence, AGI 2012, held in Oxford, united kingdom, in December 2012. The 34 revised complete papers awarded including four invited keynote lectures have been rigorously reviewed and chosen from eighty submissions. The papers are written by means of prime scientists serious about examine and improvement of AI platforms owning common intelligence on the human point and past; with a unique specialize in humanoid robotics and AGI, cognitive robotics, creativity and AGI, the long run evolution of complicated AGIs, and the dynamics of AGI aim systems.
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Additional resources for Artificial General Intelligence: 5th International Conference, AGI 2012, Oxford, UK, December 8-11, 2012. Proceedings
Connell et al. Visual Object Naming While colors, sizes, positions, and pointing can be used to draw attention to specific objects, in some cases it is more convenient to give objects names. One can then simply say “Give me the WD-40” and have the robot figure out which object this is. Of course to do this, the robot must know that “WD-40” is a valid object. It must also know what the object looks like in order to find it. To teach the robot new nouns like this, we use a simple speech pattern: “NP is called X”.
2 Probabilities for Noisy Reasoners Designing systems to minimise noise is costly, both in computational time and computational power. A better approach may be to design probabilistic reasoning systems to include an expectation of random error. To do this we can use the equations described previously, but with corrective estimates of the amount of random variation to which the reasoner is suceptible. Suppose the reasoner is suceptible to a known rate of noise d: that is, the reasoner knows that in the long run every X bits read from memory will contain dX bits whose read value is incorrect.
Conclusion We have described how Eli, our speech-based robot manipulator, selects and moves objects around on a table. We explained how the language parsing works, how objects are found, and how human gestures are detected. The robot is also able to answer questions about the scene in front of it and resolve ambiguities in any commands it receives. In addition it can be taught the names of objects and use these labels to access information in remote databases. Finally, it is also possible to “program” the robot by teaching it new named action sequences.
Artificial General Intelligence: 5th International Conference, AGI 2012, Oxford, UK, December 8-11, 2012. Proceedings by Ahmed M. H. Abdel-Fattah, Tarek Besold (auth.), Joscha Bach, Ben Goertzel, Matthew Iklé (eds.)