The automotive industry was the first to promote the development of applications of model-based systems technology on a broad scale and, as a result, has produced some of the most advanced prototypes and products. In this article, we illustrate the features and benefits of model-based systems and qualitative modeling by prototypes and application systems that were developed in the automotive industry to support on-board diagnosis, design for diagnosability, and failure modes and effects analysis. Car manufacturers and their suppliers face increasingly serious challenges particularly related to fault analysis and diagnosis during the life cycle of their products. On the one hand, the complexity and sophistication of vehicles is growing, so it is becoming harder to predict interactions between vehicle systems, especially when failures occur. On the other hand, legal regulations and the demand for safety impose strong requirements on the detection and identification of faults and the prevention of their effects on the environment or dangerous situations for passengers and other people.

In general, modeling is a complex and creative task, and building qualitative models is no exception. One way of automating this task is by means of machine learning. Observed behaviors of a modeled system are used as examples for a learning algorithm that constructs a model that is consistent with the data. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. However, an important practical question is how do we construct qualitative models in the first place.

The 28th International Workshop on Qualitative Reasoning (QR-15) presented advances toward reasoning tractably with massive qualitative and quantitative models, automatically learning and reasoning about continuous processes, and representing knowledge about space, causation, and uncertainty. The technical track included two invited talks, 11 oral presentations, and 5 poster presentations.

In this editorial introduction to this special issue of AI Magazine on qualitative reasoning, we briefly discuss the main motivations and characteristics of this branch of AI research. We also summarize the contributions in this issue and point out challenges for future research. Successful application areas include autonomous spacecraft support, failure analysis and on-board diagnosis of vehicle systems, automated generation of control software for photocopiers, and intelligent aids for learning about thermodynamic cycles. Qualitative reasoning is thus relevant for researchers who are interested in important AI issues as well as for managers, developers, and engineers who are looking for potential industrial benefits of AI. A decade has passed since the publication of three collections of papers and a book covering the foundations of the field (Weld and de Kleer 1990), the status at that time (Faltings and Struss 1992; Williams and de Kleer 1991), and a comprehensive treatment of qualitative simulation (Kuipers 1994).

Algorithms for equilibrium computation generally make no attempt to ensure that the computed strategies are understandable by humans. For instance the strategies for the strongest poker agents are represented as massive binary files. In many situations, we would like to compute strategies that can actually be implemented by humans, who may have computational limitations and may only be able to remember a small number of features or components of the strategies that have been computed. We study poker games where private information distributions can be arbitrary. We create a large training set of game instances and solutions, by randomly selecting the information probabilities, and present algorithms that learn from the training instances in order to perform well in games with unseen information distributions. We are able to conclude several new fundamental rules about poker strategy that can be easily implemented by humans.

Algorithms for equilibrium computation generally make no attempt to ensure that the computed strategies are understandable by humans. For instance the strategies for the strongest poker agents are represented as massive binary files. In many situations, we would like to compute strategies that can actually be implemented by humans, who may have computational limitations and may only be able to remember a small number of features or components of the strategies that have been computed. We study poker games where private information distributions can be arbitrary. We create a large training set of game instances and solutions, by randomly selecting the private information probabilities, and present algorithms that learn from the training instances in order to perform well in games with unseen information distributions. One approach first clusters the training points into a small number of clusters and then creates a small decision tree based on the cluster centers. This approach produces low test error and could be easily implemented by humans since it only requires memorizing a small number of "if-then" rules.

When human experts build qualitative or quantitative models of complex systems, they use the function of the system as a guideline to decide what to model and how to model it, yet they do not often encode this functional knowledge directly. If qualitative and quantitative models contained this functional knowledge, our reasoning systems might use it as a heuristic or as a filter during the course of quantitative and qualitative simulation. Matthew Klenk (PARC) delivered a separate talk related to massive-scale model-based reasoning, describing the challenge of choosing initial conditions for simulation. Throughout the technical presentations on advances in qualitative simulation, we discussed the practicality of automatically transforming quantitative and qualitative models during the course of reasoning.

We develop a qualitative model of decision making with two aims: to describe how people make simple decisions and to enable computer programs to do the same. Current approaches based on Planning or Decisions Theory either ignore uncertainty and tradeoffs, or provide languages and algorithms that are too complex for this task. The proposed model provides a language based on rules, a semantics based on high probabilities and lexicographical preferences, and a transparent decision procedure where reasons for and against decisions interact. The model is no substitude for Decision Theory, yet for decisions that people find easy to explain it may provide an appealing alternative.

This paper investigates a purely qualitative version of Savage's theory for decision making under uncertainty. Until now, most representation theorems for preference over acts rely on a numerical representation of utility and uncertainty where utility and uncertainty are commensurate. Disrupting the tradition, we relax this assumption and introduce a purely ordinal axiom requiring that the Decision Maker (DM) preference between two acts only depends on the relative position of their consequences for each state. Within this qualitative framework, we determine the only possible form of the decision rule and investigate some instances compatible with the transitivity of the strict preference. Finally we propose a mild relaxation of our ordinality axiom, leaving room for a new family of qualitative decision rules compatible with transitivity.

In this paper, we describe an interactive environment for the representation, interpretation, and revision of explanatory biological models. We illustrate our approach on the systems biology of aging, a complex topic that involves many interacting components, and discuss our experiences using this environment to codify an informal model of aging. We close by discussing related efforts and directions for future research.