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It is argued that 4-valued paraconsistent truth values (called here "p-bits")can serve as a conceptual, mathematical and practical foundation for highlyAI-relevant forms of probabilistic logic and probabilistic programming andconcept formation.  First it is shown that appropriate averaging-across-situations andrenormalization of 4-valued p-bits operating in accordance with ConstructibleDuality (CD) logic yields PLN (Probabilistic Logic Networks)strength-and-confidence truth values. Then variations on the Curry-Howardcorrespondence are used to map these paraconsistent and probabilistic logicsinto probabilistic types suitable for use within dependent type basedprogramming languages.  Zach Weber's paraconsistent analysis of the sorites paradox is extended toform a paraconsistent / probabilistic / fuzzy analysis of concept boundaries;and a paraconsistent version of concept formation via Formal Concept Analysisis presented, building on a definition of fuzzy property-value degrees in termsof relative entropy on paraconsistent probability distributions.  These general points are fleshed out via reference to the realization ofprobabilistic reasoning and programming and concept formation in the OpenCogAGI framework which is centered on collaborative multi-algorithm updating of acommon knowledge metagraph.

Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation