---
title: "Combining Discrete and Continuous Process Models"
authors:
  - "Daniel S. Weld"
doi: ""
url: "https://ijcai.org/proceedings/1985/25"
pdf: "https://ijcai.org/proceedings/1985/0025.pdf"
venue: "Proceedings of the Ninth International Joint Conference on Artificial Intelligence"
year: 1985
pages: "140-142"
track: "Automated Reasoning"
keywords:
---

# Combining Discrete and Continuous Process Models

Daniel S. Weld

*Proceedings of the Ninth International Joint Conference on Artificial Intelligence*, pages 140-142.

## Abstract

Two notions of process have been used in programs that reason about change: discrete models, which represe nt changes as instantaneous, and con tinuou s models, which represent changes as processes th at act gradually over tim e. We desc ribe a tec hnique calleLi aggregation wh ich unili es the two types of models and allows both to be used when performing qualitative simulation . Aggregation is a technique f or recognizing cyc les of processes and generating a continuous process that is equivalen t to each cyc l e. A qualitative simul ator may start pr ed ictio n with a simple di sc rete model and use aggregation to generate a continuo us process model when discrete simulation bogs down in a cycle. Ag \Jregation thereby all ows a simu lator to switc h back and forth between differen t types of models depending on which type is most exped ient. To test these ideas, we have written a progra m, PEPTIDE, wh ich performs qualitative simulatio n in the domain of molecular genetics. The fl exibi lity of PEPTIDE 's agg regator allows the program to detec t cyc les wi thin cyc les and predict th e behavior of comp lex situations.