Modeling human intuitions about liquid flow with particle-based simulation

Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring--despite tremendous variability in their material and dynamical properties. Here we propose and test a computational model of how people perceive and predict these liquid dynamics, based on coarse approximate simulations of fluids as collections of interacting particles. Our model is analogous to a "game engine in the head", drawing on techniques for interactive simulations (as in video games) that optimize for efficiency and natural appearance rather than physical accuracy. In two behavioral experiments, we found that the model accurately captured people's predictions about how liquids flow among complex solid obstacles, and was significantly better than two alternatives based on simple heuristics and deep neural networks. Our model was also able to explain how people's predictions varied as a function of the liquids' properties (e.g., viscosity and stickiness). Together, the model and empirical results extend the recent proposal that human physical scene understanding for the dynamics of rigid, solid objects can be supported by approximate probabilistic simulation, to the more complex and unexplored domain of fluid dynamics.Comment: Under review at PLOS Computational Biolog

Implementation of molecular collection theory

Hayes, in his Naive Physics Manifesto, identified two alternate ontologies for reasoning about liquids, an ontology based on the notion of a contained substance and one based on the notion of a molecular collection. Qualitative Process theory, proposed by Forbus, lends itself easily to encoding the contained substance ontology. It does not, however, provide any mechanism to perform molecular collection reasoning. The primary objective of this research is to implement a mechanism for supporting molecular collection reasoning and evaluate its usefulness in various domains

Comparison of QPE and QSIM as Qualitative Reasoning Techniques

Qualitative reasoning predicts and explains the behavior of physical systems using the system's structure through modeling and simulation. There are several approaches to qualitative reasoning. Two of the most prominent software implementations are QPE (Qualitative Process Engine) by Forbus and QSIM (Qualitative Simulation) by Kuipers. A comparison of the two systems is done on the basis of representation and reasoning ability of physical systems. The standard examples in qualitative reasoning and examples in fatigue and fracture in metals are used in the comparison. The fatigue and fracture domain of study can serve as a prototype for other related models of material behavior. A thorough comparison of QSIM and QPE identifies future directions of qualitative reasoning development

Qualitative models for space system engineering

The objectives of this project were: (1) to investigate the implications of qualitative modeling techniques for problems arising in the monitoring, diagnosis, and design of Space Station subsystems and procedures; (2) to identify the issues involved in using qualitative models to enhance and automate engineering functions. These issues include representing operational criteria, fault models, alternate ontologies, and modeling continuous signals at a functional level of description; and (3) to develop a prototype collection of qualitative models for fluid and thermal systems commonly found in Space Station subsystems. Potential applications of qualitative modeling to space-systems engineering, including the notion of intelligent computer-aided engineering are summarized. Emphasis is given to determining which systems of the proposed Space Station provide the most leverage for study, given the current state of the art. Progress on using qualitative models, including development of the molecular collection ontology for reasoning about fluids, the interaction of qualitative and quantitative knowledge in analyzing thermodynamic cycles, and an experiment on building a natural language interface to qualitative reasoning is reported. Finally, some recommendations are made for future research

The use of momentum currents to improve qualitative reasoning models in the domain of mechanical oscillators

Qualitative reasoning programs use a modelling language to discover the function of a mechanical device and a theory of causality to explain the reason for the device's behavior. A library of reusable model pieces for linear mechanical oscillators was developed for use with a causality theory developed by Forbus called Qualitative Process Theory. Difficulties in extending the library to include the case of sliding friction motivate a reformulation of the model from sum of forces to rate of momentum flow. It is shown how to decide if a quantity is "substance-like" or conserved or both. Substance-like quantities produce currents which can carry energy. Examples of substance-like quantities are momentum, mass, entropy, and electric charge. Momentum can be made to flow if an object is deformed or if two objects in contact are in relative motion. The new ontology allows graceful extension to sliding oscillators with friction and to hanging weights and coupled oscillators

Reasoning About Fluids Via Molecular Collections

Hayes has identified two distinct ontologies for reasoning about liquids. Most qualitative physics research has focused on applying and generalizing his contained-liquid ontology. This paper presents a technique for generating descriptions using the molecular collection (MC) ontology, a specialization of his alternate ontology which represents liquids in terms of little "pieces of stuff " traveling through a system. We claim that MC descriptions are parasitic on the Contained-Stuff ontology, and present rules for generating MC descriptions given a Qualitative Process theory model using contained stuffs. We illustrate these rules using several implemented examples and discuss how this representation can be used to draw cornplex conclusions. I