Dynamic Rule Generalization

Table of Links

Abstract and 1 Introduction

2 Background

3 Symbolic Policy Learner

3.1 Learning Symbolic Policy using ILP

3.2 Exception Learning

4 Rule Generalization

4.1 Dynamic Rule Generalization

5 Experiments and Results

5.1 Dataset

5.2 Experiments

5.3 Results

6 Related Work

7 Future Work and Conclusion, Limitations, Ethics Statement, and References

4.1 Dynamic Rule Generalization

In this paper, we introduce a novel algorithm to dynamically generate the generalized rules exploring the hypernym relations from WordNet (WN). The algorithm is based on information gain calculated using the entropy of the positive and negative set of examples (collected by EXPLORER). The illustration of the process is given in the Algorithm 1. The algorithm takes the collected set of examples and returns the generalized rules set. First, similar to the ILP data preparation procedure, the goals are extracted from the examples. For each goal, examples are split into two sets - E+ and E−. Next, the hypernyms are extracted using the hypernym-hyponym relations of the WordNet ontology. The combined set of hypernyms from (E+, E−) gives the body predicates for the generalized rules. Similar to the ILP (discussed above) the goal will be the head of a generalized rule. Next, the best-generalized rules are generated by calculating the max information gain between the hypernyms. Information gain for a given clause is calculated using the below formula (Mitchell, 1997) —

where h is the candidate hypernym predicate to add to the rule R, p0 is the number of positive examples implied by the rule R, n0 is the number of negative examples implied by the rule R, p1 is the number of positive examples implied by the rule R + h, n1 is the number of negative examples implied by the rule R + h, total is the number of positive examples implied by R also covered by R + h. Finally, it collects all the generalized rules set and returns. It is important to mention that this algorithm only learns the generalized rules which

are used in addition to the rules learned by ILP and exception learning (discussed in section 3).