HUNTER GATHERER Applying Constraint Satisfaction Branch and Bound and Solution Synthesis to Computational Semantics

This work integrates three related AI search techniques and applies the result to processing computational semantics both in the analysis of source text to discover underlying semantics as well as in the planning of target text using input semantics We summarize the approach as Hunter Gatherer Branch and Bound and Constraint Satisfaction allow us to hunt down non optimal and impossible solutions and prune them from the search space Solution Synthesis methods then gather together all optimal solutions while avoiding exponential complexity Each of these three general AI techniques will be described We will look at how how they have been used to solve a variety of problems These general techniques were extended or used in novel ways in this project We will describe these extensions in detail and give examples of how they were applied to computational semantic processing A major contribution of this work will also be in showing how and why Natural Language is a prime candidate for applying these methods and how they can enable near linear time processing As part of this discussion we will demonstrate the important result that by converting Text Planning to a constraint satisfaction problem Means End type planning can be replaced by an e cient constraint based search through a complex tree Finally we will examine the results in the light of the Mikrokosmos Machine Translation project This project is a large scale Spanish English MT system implemented at New Mexico State University We will be able to evaluate the control mechanism presented here against a large corpus of sample texts In particular we will show that a search space in the billions or in some cases ga zillions can be reduced to a few thousand or less with a corresponding decrease in run time Research reported in this paper was supported in part by Contract MDA C from the U S Department of Defense