---
title: "Deductive Retrieval Mechanisms for State Description Models"
authors:
  - "Richard Fikes"
doi: ""
url: "https://ijcai.org/proceedings/1975/16"
pdf: "https://ijcai.org/proceedings/1975/0016.pdf"
venue: "Proceedings of the Fourth International Joint Conference on Artificial Intelligence"
year: 1975
pages: "99-105"
track: "Representation Of Problems and Knowledge"
keywords:
---

# Deductive Retrieval Mechanisms for State Description Models

Richard Fikes

*Proceedings of the Fourth International Joint Conference on Artificial Intelligence*, pages 99-105.

## Abstract

This paper presents some programming facilities for modeling the semantics of a task domain and for describing the situations that occur in that domain as a task is being carried out. Each such de scription models a "state" of the task environment, and any given state can be transformed into a new state by the occurrence of an event that alters the environment . Such modeling systems are vital in many AI systems, particularly those that do question answering and those that do automatic generation and e xecution monitoring of plans. The modeling mechanisms described are basically extensions and modi~ i cations of facilities typically found in AI programming languages such as PLANNER, CONNIVER, and QA4. In particular, we discuss our usc of a 3 valu e d logic, generator functions to deduce answers to model queries, the saving and maintaining of derived results, and new facilities for modeling st a te chang e s produced by the occurrence of events.