Titlebook: Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal,Contextual and Causal Inference; Ben Goertzel,Nil Geisweiller,Cassio Penna

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Representing and Reasoning on Contextual Knowledgetionships are contextual innature. Contextuality must be explicitly taken into account to make real-world inferencetractable. This is one of the issues with which formal-logic-based AI has traditionally hadthe most difficulty. In this chapter we briefly explore the notion of contextual inference and

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Scalable Spatiotemporal Logical Knowledge Storagelogical knowledge, we nowturn to the question of how large amounts of logical knowledge can pragmatically be stored.This chapter presents a brief and relatively nonmathematical interlude before we plungeinto the more technical topics of mining patterns in logical knowledge stores, and carryingout in

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Mining Patterns from Large Spatiotemporal Logical Knowledge Storesor. One can carryout reasoning toward various goals. And another important question is how to find “unknownunknowns” – patterns in the knowledge base that are surprising and interesting yetunexpected. This quest goes by multiple names – data mining, pattern mining, informationexploitation, and so fo

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Book 2011solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.

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Ben Goertzel,Nil Geisweiller,Lucio Coelho,Predrag Janicic,Cassio Pennachinue. That, I have argued, is what epistemic paternalism is. Moreover, I suggested towards the end of Chapter 2 that it is not part of its definition that there is anything objectionable about epistemic paternalism. But by the same logic, it also is not part of the definition that practising epistemic

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