This dissertation presents the results of research on a planning formalism for a theory of natural-language generation that will support the generation of utterances that satisfy multiple goals.
Michael E Bratman, David J Israel, & Martha E. Pollack
An architecture for a rational agent must allow for means-end reasoning, for the weighing of competing alternatives, and for interactions between these two forms of reasoning.
In a previous paper [Moore, 1983a, 1983b], we presented a nonmonotonic logic for modeling the beliefs of ideally rational agents who reflect on their own beliefs, which we call "autoepistemic logic."
We first consider why one would want to use natural language to communicate with computers at all, looking at both general issues and specific applications. Next we examine what it really means for a system to have natural-language capability.
A preliminary version of QLISP is described. QLISP permits free intermingling of QA4-like constructs with INTERLISP code. The preliminary version contains features similar to those of QA4 except for the backtracking of control environments.
Because many artificial intelligence applications require the ability to deal with uncertain knowledge, it is important to seek appropriate generalizations of logic for that case.
This paper describes the basic strategies of automatic problem solving, and then focuses on a variety of tactics for improving their efficiency. An attempt is made to provide some perspective on and structure to the set of tactics.
This paper surveys some of the key problems that arise in defining a system of representation for the logical forms of English sentences and suggests possible approaches to their solution.
Much of commonsense knowledge about the real world is in the form of procedures or sequences of actions for achieving particular goals. In this paper, a formalism is presented for representing such knowledge using the notion of process.
