No, because it's mostly a musing from me, although something I've been thinking about for a long time. I mention it mostly in the unlikely hope that someone runs with it and implements it, cause it'd be something I'd love to use but probably never have the time to make.
Hrm. Complete was probably the wrong word. First order logic is complete in the mathematical sense that it can be used to represent and solve any problem, but the issue with AI / AGI is one of practicality. First order logic suffers from term explosion on any non-toy problem, leading to the need for higher order logics (of which there are many), and/or integration with other kinds of systems to manage that complexity. First order logic is also not probabilistic, and dealing well with uncertainty is a hallmark of natural language. Most AGI logic systems are not first order logic, although you can implement FOL in such systems. So better summarized my research question is basically "Practical AI systems need tools beyond what first order logic provides to represent thought. I wonder what would result from applying these extra tools to a logical conlang?"
My intuition is that the right way to approach this is to ask "What would a conlang based on probabilistic graphical models look like?" The reason being that PGMs are the underlying theory for most modern approaches to concept representation and knowledge databases. Examples of PGMs are Bayes nets, Markov random fields, and restricted Boltzmann machines, aka the state of the art in modern neural networks. PGMs also underlay to a more limited degree many probabilistic concept network formulations, including the atomspace of OpenCog.
So what would a PGM-based conlang look like? I'm not sure actually, but I can tell you it probably wouldn't look much like Loglan / Lojban. It'd be easy to represent any Lojban sentence in this hypothetical PGM language, using the "first order dialect," but the converse is probably not generally true.
Hrm. Complete was probably the wrong word. First order logic is complete in the mathematical sense that it can be used to represent and solve any problem, but the issue with AI / AGI is one of practicality. First order logic suffers from term explosion on any non-toy problem, leading to the need for higher order logics (of which there are many), and/or integration with other kinds of systems to manage that complexity. First order logic is also not probabilistic, and dealing well with uncertainty is a hallmark of natural language. Most AGI logic systems are not first order logic, although you can implement FOL in such systems. So better summarized my research question is basically "Practical AI systems need tools beyond what first order logic provides to represent thought. I wonder what would result from applying these extra tools to a logical conlang?"
My intuition is that the right way to approach this is to ask "What would a conlang based on probabilistic graphical models look like?" The reason being that PGMs are the underlying theory for most modern approaches to concept representation and knowledge databases. Examples of PGMs are Bayes nets, Markov random fields, and restricted Boltzmann machines, aka the state of the art in modern neural networks. PGMs also underlay to a more limited degree many probabilistic concept network formulations, including the atomspace of OpenCog.
So what would a PGM-based conlang look like? I'm not sure actually, but I can tell you it probably wouldn't look much like Loglan / Lojban. It'd be easy to represent any Lojban sentence in this hypothetical PGM language, using the "first order dialect," but the converse is probably not generally true.