By Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Visit Amazon's Ari Heljakka Page, search results, Learn about Author Central, Ari Heljakka,

This booklet describes Probabilistic common sense Networks (PLN), a unique conceptual, mathematical and computational method of doubtful inference. Going past earlier probabilistic ways to doubtful inference, PLN encompasses such rules as induction, abduction, analogy, fuzziness and hypothesis, and reasoning approximately time and causality. The e-book presents an outline of PLN within the context of alternative techniques to doubtful inference. issues addressed within the textual content include:

- the uncomplicated formalism of PLN wisdom representation
- the conceptual interpretation of the phrases utilized in PLN
- an indefinite likelihood method of quantifying uncertainty, offering a basic strategy for calculating the "weight-of-evidence" underlying the conclusions of doubtful inference
- specific PLN inference ideas and the corresponding truth-value formulation used to figure out the power of the belief of an inference rule from the strengths of the premises
- large-scale inference strategies
- inference utilizing variables
- indefinite chances related to quantifiers
- inheritance in response to homes or patterns
- the Novamente Cognition Engine, an software of PLN
- temporal and causal good judgment in PLN

Researchers and graduate scholars in synthetic intelligence, desktop technology, arithmetic and cognitive sciences will locate this novel point of view on doubtful inference a thought-provoking integration of principles from numerous different strains of inquiry.

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**Additional info for Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference**

**Example text**

On the other hand, our approach is consistent with probability theory but introduces measures of association and pattern-intensity as additional concepts, and integrates them into the overall probabilistic framework of PLN. Philosophically, one may ask why a pattern-based approach to intensional inference makes sense. Why, in accordance with Cox’s axioms, isn’t straightforward probability theory enough? The problem is – to wax semi-poetic for a moment – that the universe we live in is a special place, and accurately reasoning about it requires making special assumptions that are very difficult and computationally expensive to explicitly encode into probability theory.

Inference can be used to modify count values as well as strength values, which cov- 34 Probabilistic Logic Networks ers the case where entities are inferred to exist rather than observed to exist. And in an architecture incorporating natural language processing, one can utilize “semantic mapping schemata,” which translate perceived linguistic utterances into sets of Atoms, and which may explicitly update the confidence components of truth values. ” An important question there is: What process learns these cognitive schemata carrying out semantic mapping?

But in practice we have found that a handful of distributional forms seem to suffice to cover commonsense inferences (beta and bimodal forms seem good enough for nearly all cases; and here we will give only examples covering the beta distribution case). Because the semantics of indefinite probabilities is different from that of ordinary probabilities, or imprecise probabilities, or for example NARS truth values, it is not possible to say objectively that any one of these approaches is “better” than the other one, as a mathematical formalism.