Heuristic Induction of Rate-Based Process Models

Abstract This paper presents a novel approach to inductive process modeling, the task of constructing a quantitative account of dynamical behavior from time-series data and background knowledge. We review earlier work on this topic, noting its reliance on methods that evaluate entire model structures and use repeated simulation to estimate parameters, which together make severe computational demands. In response, we present an alternative method for process model induction that assumes each process has a rate, that this rate is determined by an algebraic expression, and that changes due to a process are directly proportional to its rate. We describe RPM, an implemented system that incorporates these ideas, and we report analyses and experiments that suggest it scales well to complex domains and data sets. In closing, we discuss related research and outline ways to extend the framework. Background and Motivation Reasoning about scientific domains is a high-level cognitive task that is widely viewed as requiring considerable intelligence. Over the past three decades, research on computational scientific discovery Much of the research in this area has focused on equation discovery, which has played a key role in the history of science and which has clear applications in many different disciplines. However, this task often arises in the early stages of a field's development, before theoretical knowledge supports the creation of explanatory models that account for observations in deeper terms. The task of explanatory model construction has received less attention from computational researchers, but there has been some progress on this topic. One important form of such tasks, known as inductive process modeling In the sections that follow, we review drawbacks of previous efforts on inductive process modeling, focusing on their computational expense and problems with parameter estimation. In response, we present a new approach to process model induction that adopts representational assumptions which let it carry out heuristic rather than exhaustive search through the space of model structures, making it both more efficient and more reliable. We also describe an implemented system that incorporates these ideas, and we report experimental studies that demonstrate its ability to identify relevant processes, handle noisy observations, and construct accurate models that relate many variables. We conclude by discussing our framework's relation to earlier work and proposing responses to its limitations. Critique of Process Modeling Research Research on inductive process modeling has made continual progress since its first appearance over a decade ago The first characteristic is that, even though process models are compositional in nature, current algorithms evaluate only complete model structures. This evaluation involves comparing a parameterized model's predictions with observed time series. Existing systems generate many model structures up to a given complexity level, then parameterize and evaluate each candidate in turn. This approach doe

Heuristic Induction of Rate-Based Process Models

This paper presents a novel approach to inductive process modeling, the task of constructing a quantitative account of dynamical behavior from time-series data and background knowledge. We review earlier work on this topic, noting its reliance on methods that evaluate entire model structures and use repeated simulation to estimate parameters, which together make severe computational demands. In response, we present an alternative method for process model induction that assumes each process has a rate, that this rate is determined by an algebraic expression, and that changes due to a process are directly proportionalto its rate. We describe RPM, an implemented system that incorporates these ideas, and we report analyses and experiments that suggest it scales well to complex domains and data sets. In closing, we discuss related research and outline ways to extend the framework

Flexible Model Induction through Heuristic Process Discovery

Inductive process modeling involves the construction of explanatory accounts for multivariate time series. As typically specified, background knowledge is available in the form of generic processes that serve as the building blocks for candidate model structures. In this paper, we present a more flexible approach that, when available processes are insufficient to construct an acceptable model, automatically produces new generic processes that let it complete the task. We describe FPM, a system that implements this idea by composing knowledge about algebraic rate expressions and about conceptual processes like predation and remineralization in ecology. We demonstrate empirically FPM's ability to construct new generic processes when necessary and to transfer them later to new modeling tasks. We also compare its failure-driven approach with a naive scheme that generates all possible processes at the outset. We conclude by discussing prior work on equation discovery and model construction, along with plans for additional research