Epstein Secrets

reinforcement learning mode, to learn small programs that will guide the virtual parrot in achieving its goals (where its goals are managed via the OpenPsi motivational system inside OpenCog, loosely inspired by Joscha Bach's MicroPsi system). • Use of ECAN to learn associational relationships between

arning (MOSES) Learns procedures governing beha 'or. Provides procedures to seed PLN learning. Action Selection (Psi) Selects procedures from the AtomSpace based on Goals in the AtomSpace and declarative and attentional knowledge related to these procedures and goals N AtomSpace Central knowledge st

reinforcement learning mode, to learn small programs that will guide the virtual parrot in achieving its goals (where its goals are managed via the OpenPsi motivational system inside OpenCog, loosely inspired by Joscha Bach's MicroPsi system). • Use of ECAN to learn associational relationships between

beha 'or. Provides procedures to seed PLN learning. Action Selection (Pd) AternSpace Selects procedures from the Central knowledge store of the AtomSpace based on Goals in the e--pi virtual parrot mind. storing AtomSpace and declarative and declarative procedural, attentional, attentional knowledge

reinforced with repletion, and weaker with no repetition. • The use of several algorithms all functioning at the same time: "cognitive synergy" • OpenPsi—inspired by Joscha Bach's MicroPsi Project: the use of motivation tanks that can be restored or depleted: energy, water, integrity, Affiliation, cer

p the architecture of the human mind, Open Cog also programed three game characters that push past traditional algorithms: Specifically using: • AtomSpace, a neural memory system. A database of concepts or 'atoms'. • MindAgents • Atoms are linked via algorithms—links get reinforced with repletion, a