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

Learning Multi-Tree Classification Models with Ant Colony Optimization

Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers). A classifier encodes the relationships between the input attribute values and the values of a class attribute in a given set of labelled cases and it can be used to predict the class value of new unlabelled cases. Decision trees have been widely used as a type of classification model that represent comprehensible knowledge to the user. In this paper, we propose the use of ACO-based algorithms for learning an extended multi-tree classification model, which consists of multiple decision trees, one for each class value. Each class-based decision trees is responsible for discriminating between its class value and all other values available in the class domain. Our proposed algorithms are empirically evaluated against well-known decision trees induction algorithms, as well as the ACO-based Ant-Tree-Miner algorithm. The results show an overall improvement in predictive accuracy over 32 benchmark datasets. We also discuss how the new multi-tree models can provide the user with more understanding and knowledge-interpretability in a given domain

Learning backward induction: a neural network agent approach

This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70% of the time in new games. Similarly to humans, the neural network agents are also found to suffer from subgame and truncation inconsistency, supporting the contention that they are appropriate models of general learning in humans. The agents were found to behave in a bounded rational manner as a result of the endogenous emergence of decision heuristics. In particular a very simple heuristic socialmax, that chooses the cell with the highest social payoff explains their behavior approximately 60% of the time, whereas the ownmax heuristic that simply chooses the cell with the maximum payoff for that agent fares worse explaining behavior roughly 38%, albeit still significantly better than chance. These two heuristics were found to be ecologically valid for the backward induction problem as they predicted the Nash equilibrium in 67% and 50% of the games respectively. Compared to various standard classification algorithms, the NNs were found to be only slightly more accurate than standard discriminant analyses. However, the latter do not model the dynamic learning process and have an ad hoc postulated functional form. In contrast, a NN agent’s behavior evolves with experience and is capable of taking on any functional form according to the universal approximation theorem.

On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

A Comparative Study on the Use of Classification Algorithms in Financial Forecasting

Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial forecasting is as a classification problem, where the goal is to find a model that represents the predictive relationships between predictor attribute values and class attribute values. In this paper we present a comparative study between two bio-inspired classification algorithms, a genetic programming algorithm especially designed for financial forecasting, and an ant colony optimization one, which is designed for classification problems. In addition, we compare the above algorithms with two other state-of-the-art classification algorithms, namely C4.5 and RIPPER. Results show that the ant colony optimization classification algorithm is very successful, significantly outperforming all other algorithms in the given classification problems, which provides insights for improving the design of specific financial forecasting algorithms

Dynamic Service Rate Control for a Single Server Queue with Markov Modulated Arrivals

We consider the problem of service rate control of a single server queueing system with a finite-state Markov-modulated Poisson arrival process. We show that the optimal service rate is non-decreasing in the number of customers in the system; higher congestion rates warrant higher service rates. On the contrary, however, we show that the optimal service rate is not necessarily monotone in the current arrival rate. If the modulating process satisfies a stochastic monotonicity property the monotonicity is recovered. We examine several heuristics and show where heuristics are reasonable substitutes for the optimal control. None of the heuristics perform well in all the regimes. Secondly, we discuss when the Markov-modulated Poisson process with service rate control can act as a heuristic itself to approximate the control of a system with a periodic non-homogeneous Poisson arrival process. Not only is the current model of interest in the control of Internet or mobile networks with bursty traffic, but it is also useful in providing a tractable alternative for the control of service centers with non-stationary arrival rates.Comment: 32 Pages, 7 Figure