---
title: "A Model of Decidable Introspective Reasoning with Quantifying-In"
authors:
  - "Gerhard Lakemeyer"
doi: ""
url: "https://ijcai.org/proceedings/1991/74"
pdf: "https://ijcai.org/proceedings/1991/0074.pdf"
venue: "Proceedings of the Twelfth International Joint Conference on Artificial Intelligence"
year: 1991
pages: "492-496"
track: "Knowledge Representation"
keywords:
---

# A Model of Decidable Introspective Reasoning with Quantifying-In

Gerhard Lakemeyer

*Proceedings of the Twelfth International Joint Conference on Artificial Intelligence*, pages 492-496.

## Abstract

Since knowledge is usually incomplete, agents need to introspect on what they know and do not know . The best known models of introspective reasoning suffer from intractability or even undecidability if the underlying language is first-order . To better suit the fact that agents have limited resources, we recently proposed a model of decidable introspective reasoning in first-order knowledge bases (KBs). However, this model is deficient in that it does not allow for quantifying-in, which is needed to distinguish between knowing that and knowing who. In this paper, we extend our earlier work by adding quantifying-in and equality to a model of limited belief that integrates ideas from possible-world semantics and relevance logic.