Abstract

This chapter describes a model and an underlying theoretical framework for hybrid planning. Modern planning domain-description formalisms are based on purely sentential languages. Sentential representations produce problem encodings that often require the system to carry out an unnecessary amount of trivial deductions, preventing it from concentrating all the computational effort on the actual search for a plan and leading to a loss in performances. This chapter illustrates how techniques from the area of knowledge representation and reasoning can be adopted to develop more efficient domain-description languages. In particular, experimental evidence suggests that the adoption of analogical descriptions can lead to significant improvements in planning performance. Although often more efficient, however, analogical representations are generally less expressive than sentential ones. The hybrid approach proposed here provides a framework in which sentential and analogical descriptions can be integrated and used interchangeably, thereby overcoming the limitations and exploiting the advantages of both paradigms.

Citations