Epstein Secrets

hema execution in the first place. The problem of credit assignment thus becomes a problem of creating appropriate heuristics to guide inference of ImplicationLinks of the form described above. Assignment of credit is then implicit in the calculation of truth values for these links. The difficulty is that the pr

routines supplied to it by an external source (e.g. another AI system such as OpenCog) 2. Utilizing one of OpenCogPrime's cognitive processes (the "Fishgram" frequent subhyper- graph mining algorithm) to recognize patterns in sets of DeSTIN states, and then recording these patterns in OpenCogPrime's Atom

ms an action and achieves a goal. There is code to create implications based on these conjunctions. After finding many conjunctions, it can produce ImplicationLinks based on some of them. Here is an example where the Al-controlled virtual robot discovers how to get energy. (ImplicationLink (AndLink (Evaluatio

a greedy search, so it can quickly find many patterns. In contrast, algorithms like MOSES are designed to find a small number of the best patterns. Fishgram works by finding a set of objects that have links in common, so it will be most effective if the AtomSpace has a lot of raw data, with simple patter

intelligent system explicitly represents in its knowledge base, and expends some resources trying to achieve. Goal Nodes (which may be Nodes or, e.g. ImplicationLinks) are used for this purpose in OpenCog. e Goal-Driven Learning: Learning that is driven by the cognitive schematic i.e. by the quest of figuring out

rations) Extension: In PLN, the extension of a node refers to the instances of the category that the node represents. In contrast is the intension. Fishgram (Frequent and Interesting Sub-hypergraph Mining): A pattern mining algorithm for identifying frequent and/or interesting sub-hypergraphs in the Atoms