Hello everyone, I am defining my Phd subject which will revolve around Neuro-symbolim. The goal would be to make an agent to learn as much knowledge as it can from its environement, and I found the opencog and the atomspace graph very interesting for the implementation.
My questions might be too vague, but maybe somebody can provide an answer or at least some guiding ideas. How can we use the opencog system to store increasingly more complex concepts of the environment an agent is exploring ? Can the concepts be casted as atoms ? How would the relations between atoms be constructed ? Is there already a solution in the opencog ecosystem to store concepts in a hierarchical or composed manner ? (I am thinking for instance of the Option framework, where long sequences of actions are stored (as RL algos) and guided by a meta-policy, can this framework be "easily" implemented with opencog?). Hope my questions are a bit clear. I am reading a lot of papers now and it sometimes difficult to grasp all the concepts and be articulated when talking about them. Thanks! Aymeric -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/f6de7a4a-d91c-4acb-9cf3-41dbdf27d6f2n%40googlegroups.com.
