Ramification analysis using causal mapping

摘要

To operate in the real-world, intelligent agents constantly need to absorb new information, and to consider the ramifications of it. This raises interesting questions for knowledge representation and reasoning. Here we consider ramification analysis in which we wish to determine both the likely outcomes from events occurring and the less likely, but very significant outcomes, from events occurring. To formalize ramification analysis, we introduce the notion of causal maps for modelling “causal relationships” between events. In particular, we consider existential event classes, for example presidential-election, with instances being true, false, or unknown, and directional events classes, for example inflation, with instances being increasing, decreasing or unchanging. Using causal maps, we can propagate new information to determine possible ramifications. These ramifications are also described in terms of events. Whilst causal maps offer a lucid view on ramifications, we also want to support automated reasoning, to address problems of incompleteness, and to represent further conditions on ramifications. To do this, we translate causal maps into default logic, and use the theory and automated reasoning technology of default logic. In this paper, we provide a syntax and semantics for causal mapping, and a translation into default logic, and discuss an integration of the approach with langauge engineering.