Epistemic reasoning, logic programming, and the interpretation of questions

Reasons are given to support the claim that strong connections exist between linguistic theories on question semantics and work within AI and logic programming. Various ways of assigning denotations to questions are compared, and it is argued that the earlier, "naive" version is to be preferred to more recent developments. It is shown that it is possible to use the chosen denotation to give a model-theoretic semantics for the concept of "knowing what", from which a relationship between "knowing what" and "knowing" can provably be derived. An application to logic programming is described, which allows formal reasoning about what a logic database "knows", this being in a sense a generalization of the Closed World Assumption. Finally, a "knows-what" meta-interpreter in Prolog is demonstrated, and proved to be sound and complete for a certain class of database programs

Modeling of Phenomena and Dynamic Logic of Phenomena

Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, problem or theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes of the mind and natural evolution. This paper provides a formal description of DLP by specifying its syntax, semantics, and reasoning system. We also outline links between DLP and other logical approaches. Computational complexity issues that motivate this work are presented using an example of polynomial models