Though the solution procedure was transferred in both of these cases, the system still had no way of knowing if the transferred solution was adequate for the new problem. In the tumor problem, in order for the agent to determine if the tumor was destroyed and the patient was still alive, it needed some causal knowledge. By causal we mean knowledge of how things in a system change as they interact. Pre- and post-conditions are a straightforward way to represent this, but it is difficult to imagine what ``visual'' pre- and post-conditions might look like. Visual representations alone cannot enable evaluation of the solution.
Galatea represents causal knowledge with production rules, implemented in ACT-R [Anderson and Libiere1998]. We have no theoretical commitment to production rules or ACT-R. One production rule identifies a body part as dead if there is a thick line representing a ray going through it. Another rule identifies the tumor being killed if enough radiation is hitting it. If the tumor is dead and the body is alive, a final production fires that identifies the problem as being solved.
When the tumor problem is first encountered (when it only consists of a single simage), Galatea is unable to infer through the productions that the problem is solved in the initial state. When the solution is transferred from the fortress, the rules confirm that the problem has been solved.