Chance Discovery and Analysis of Data via Multi-Agent Logics

We study applications of mathematical logic to Information Sciences in theirs particular part - Chance Discovery (which is a popular area in Knowledge Representation and CS). Main used tool is multi-agent logic based at modal-like temporal logic. In particular, we consider more thin case when the time is not supposed to be transitive. The semantics of our logical approach is based at relational models for modelling computational processes and analysis of databases (with incomplete information, for instance, with information forgotten in the past, etc). We assume that the agent's accessibility relations may have lacunas; agents may have no access to some potentially known and stored information. Satisfiability and decidability issues are in focus of research. We find algorithms solving satisfiability problem. Illustrating examples are given and application areas are suggested. (C) 2019 The Authors. Published by Elsevier B.V.