Abduction machines – summary of progress

All six generations of abduction machines described in this book are attempts to answer the question of how to organize knowledge and control processing to make abductive problem solving computationally feasible. How can an intelligent agent form good composite explanatory hypotheses without getting lost in the large number of potentially applicable concepts and the numerical vastness of their combinations? What general strategies can be used? Furthermore, it is not enough simply to form the best explanation, which already appears to be difficult, but an agent needs to be reasonably sure that the explanation chosen is significantly better than alternative explanations, even though generating all possible explanations so that they can be compared is usually not feasible.

Thus it seems that we are in deep trouble. Logic demands that an explanation be compared with alternatives before it can be confidently accepted, but bounded computational resources make it impossible to generate all of the alternatives. So it seems, tragically, that knowledge is impossible! Yet we are saved after all by a clever trick; and that trick is implicit comparison. A hypothesis is compared with alternatives without explicitly generating them all. One way to do this, as we have seen, is by comparing parts of hypotheses. By comparing hypothesis-part h1 with hypothesis-part h2, all composite hypotheses containing h1 are implicitly compared with all composites containing h2. Another way to implicitly compare hypotheses is to rely on a hypothesis generator that generates hypotheses in approximate order of most plausible first.