Epstein Secrets

onal and episodic knowledge. Action Orchestrator Runs learned procedures and sends atomic actions to the virtual world to guide parrot behavior. Probabilistic Logic Networks (PIN) Generalizes. extends. combines and improves procedures learned by MOSES. Procedures learned by PLN can be sent back to MOSES to seed new le

the core of the CogPrime Artificial General Intelligence design, an integrative cognitive architecture that is partially implemented in the current OpenCog software system. The proposed project would explore and attempt to validate the cognitive synergy hypothesis, via utilizing the OpenCog system to co

the current OpenCog software system. The proposed project would explore and attempt to validate the cognitive synergy hypothesis, via utilizing the OpenCog system to control a virtual parrot in a game world, and studying how the parrot performs on virtual-world implementations of various "animal-level

on involves a probability distribution with a mean of tv. The precise semantics of the strength values associated with OpenCog Atoms is described in Probabilistic Logic Networks (see Chapter 34). Please note, though: This notation does not imply that the only data retained in the system about the distribution is the single

ledge and intuition regarding CogPrime. For example, beyond the ideas presented here, there is a body of work on the OpenCog wiki site, and then the OpenCog codebase itself. More extensive introductory remarks may be found in Preface of Part 1, including a brief history of the book and acknowledgements

sign. No doubt it will evolve in some respects as implementation progresses; one of the principles kept in mind during the design and development of OpenCog was modular- ity, enabling substantial modifications to particular parts of the framework to occur without requiring wholesale changes throughout th

theory of language formed via combining ideas from three sources: Hudson's Word Grammar 11hu190, HudOiaj, Sleator and Temperley's link grammar. and Probabilistic Logic Networks. Reflecting its origin in these three sources, we have named the new theory PROWL grammar, meaning PRObabilistic Word Link Grammar. We believe PROW

k for Natural Language Comprehension Now we move forward in the pipeline from syntax toward semantics. The NL comprehension framework provided with OpenCog at its inception in 2008 is RelEx, an English-language se- mantic relationship extractor, which consists of two main components: the dependency extra

rammar terms, this corresponds loosely to (S (NP The cat) (VP chased (NP a snake)) EFTA00624530 384 44 Natural Language Comprehension but the OpenCog linguistic pipeline makes scant use of this kind of phrase structure rendition (which is fine in this simple example; but in the case of complex sen

oing something (perhaps something in the physical world, or perhaps an abstract mental action). The key SMEPH Edge types, using language drawn from Probabilistic Logic Networks (PLN) and elaborated in Chapter 34 below, are as follows: • ExtensionallnheritanceEdge (ExtInhEdge for short: an edge which, linking one Vertex or

us to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its personal thoughts into the mental language of another OpenCog instance. This translation process may be quite subtle, sin

representation, with an explicit (uncertain) logical aspect as well as an explicit subsymbolic aspect more analogous to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its persona

doing something (perhaps something in the physical world, or perhaps an abstract mental action). The key SMEPH Edge types, using language drawn from Probabilistic Logic Networks (PLN) and elaborated in Chapter 34 below, are as follows: e ExtensionalInheritanceEdge (ExtInhEdge for short: an edge which, linking one Vertex or E

us to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its personal thoughts into the mental language of another OpenCog instance. This translation process may be quite subtle, since

d representation, with an explicit (uncertain) logical aspect as well as an explicit subsymbolic aspect more analogous to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its persona