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

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1877-3273 ral or contextual logic.The book breaks new research ground The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – quer
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Introduction,structure of much of the human world, and we will briefly touch on someof these here as well. But for the purposes of this book, when we mention “real-worldreasoning” or RWR, we’re mostly talking about uncertainty, spacetime, cause, context andscalability.
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Mining Patterns from Large Spatiotemporal Logical Knowledge Storesthat are surprising and interesting yetunexpected. This quest goes by multiple names – data mining, pattern mining, informationexploitation, and so forth. Whatever you call it, it’s a difficult challenge because in anylarge dataset, the number of possible patterns to search through is mind-boggling.
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Introduction,x informationabout real-world situations. Specifically we conceive “real-world reasoning” here mainlyas “massively scalable reasoning involving uncertainty, space, time, cause and context.” Ofcourse there are other important aspects to reasoning about the real world we live in, e.g.the hierarchical
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Knowledge Representation Using Formal Logiccontextual knowledge, wemust discuss representation of temporal knowledge (as well as, to a certain extent, spatialknowledge) . . . and before that, we must address knowledge representation in general.
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Representing and Reasoning On Spatial Knowledge representing and reasoningabout space, but, this is not innate to software programs so we need to explicitlythink about how to make our software systems space-friendly. From a logic point of view,space and time can in principle be treated just like any other sorts of relationships – but thisturns o
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