Qualitative reasoning about physical systems has become one of the most productive areas in AI in recent years, due in part to the 1984 special issue of Artificial Intelligence on that topic. My contribution to that issue was a paper entitled "Commonsense reasoning about causality: deriving behavior
Geometric reasoning with quantitative physical models
β Scribed by Yoshinori Okamota
- Book ID
- 104591610
- Publisher
- John Wiley and Sons
- Year
- 1994
- Tongue
- English
- Weight
- 779 KB
- Volume
- 25
- Category
- Article
- ISSN
- 0882-1666
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
This paper proposes a method of reasoning and of planning space and motion based on quantitative physical models.
First, the particleβcollection representation (PCR) is proposed as a method of representing objects, and the robust simulation method of rigidβbody dynamics using PCR is discussed.
Next, as a system which reasons and plans dynamic actions of rigid bodies based on this rigidβbody simulation, a planner (PCP) using physical simulation is proposed.
For the planning method of PSP, the action improvement method is proposed which carries out a plan by gradually improving an original plan.
To solve the problem of adjusting parameters for action in the action improvement method, the general parameter adjuster (GPA) is proposed. Moreover, the motion of bodies and carriage plannings by PSP are simulated. Furthermore, the robustness and extensibility of the PSP are investigated.
π SIMILAR VOLUMES
brief survey of the best results in gravitation theory and relativistic geometrical optics deriving from the differential geometry of generalized Lagrange spaces is given.