CAUSIM: A rule-based causal simulation system

Researchers have felt that expert performance must also rest on knowledge of deep models which relate underlying causal variables to observable facts. Simulation based upon causal knowledge is an important method to infer possible consequences from given situations. This paper presents a rule-based causal simulation system called CAUSIM, which basically offers two kinds of simulation: backward simulation and forward simulation. Backward simulation is used to infer the instant behavior of specific attributes, whereas forward simulation is taken to arrive at possible overall scenarios. In addition, CAUSIM invokes constraint rules which describe incompatible behavior and values among related variables before applying simulation rules in order to obviate the inconsistencies between the simulation result and existing facts. The strength of CAUSIM lies in the capability of performing both qualitative and quantitative causal simulation in an integrated environment.