Heuristic-Driven Theory Projection: An Overview.

This chapter provides a concise overview of Heuristic-Driven Theory Projection (HDTP), a powerful framework for computing analogies. The chapter attempts to illuminate HDTP from several different perspectives. On the one hand, the syntactic basis of HDTP is formally specified, in particular, restricted higher-order anti-unification together with a complexity measure is described as the core process to compute a generalization given two input domains (source and target). On the other hand, the substitution-governed alignment and mapping process is analyzed together with the transfer of knowledge from source to target in order to induce hypotheses on the target domain. Additionally, this chapter presents some core ideas concerning the semantics of HDTP as well as the algorithm that computes analogies given two input domains. Finally, some further remarks describe the different (but important) roles heuristics play in this framework.