OpenNARS for Applications v0.9.1

This is a new release with new features:

• Ability to do temporal compounding (sequences, temporal implications) among derived events using attention-based selection as Tony Lofthouse proposed.
• Layered goal Priority Queue for better balanced resource allocation among goals of different derivation depth, taken from Robert Wuensche's 20NAR1.
• Ability to learn and execute compound operations as suggested by Pei Wang, whereby output arguments can be the input arguments of other operations.
• Relational frame theory Python experiments contributed by Robert Johansson, including Word Sorting Task, Identity Matching and an experiment for compound conditioning and usage of equivalence.
• New command *opconfig as suggested by Adrian Borucki to print the current operations which are registered, and their babbling configuration.
• 50ms input delay in NAR.py eliminated by using the Subprocess standard Python module instead of the Pexpect library.

The new experimental language learning ability (mutual and combinatorial entailment according to Relational Frame Theory) was left out to allow for a mature release, it will be back once stable, likely in v0.9.2.