Epstein Secrets

the comparison of MOSES, PLN and ECAN with other roughly comparable algorithms from the literature (e.g. MOSES vs. Genetic Programming, PLN versus Markov Logic Networks, ECAN versus attractor neural nets). We have chosen not to include this sort of comparison in the core of the proposed work, because it would increa

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

the comparison of MOSES, PLN and ECAN with other roughly comparable algorithms from the literature (e.g. MOSES vs. Genetic Programming, PLN versus Markov Logic Networks, ECAN versus attractor neural nets). We have chosen not to include this sort of comparison in the core of the proposed work, because it would increa

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

can be assigned to these expressions. by making explicit use of the notions of conditional random fields (or rather, a certain special case, termed Markov Logic Networks). Conditional random fields, or Markov networks, are a certain mathematical formalism that provides the most general framework in which entropy max

chActSchema and SpeechActTriggers) corresponding to fine-grained types of speech act • Dialogue control guided by general cognitive control system (OpenPsi, running within Open Cog) • SpeechActSchema and SpeechActTriggers, in some cases, will internally consult proba- bilistic inference, thus supplying