However, what are the application areas include autonomous spacecraft key research topics? There are the scientific disciplines support, failure analysis and on-board diagnosis such as physics and chemistry that develop of vehicle systems, automated generation theories, and there are engineering disciplines of control software for photocopiers, and intelligent aids for learning about thermodynamic that develop solutions that change the cycles. Qualitative reasoning is thus relevant physical world. Both use formal mathematical for researchers who are interested in important systems, as well as computer implementations, AI issues as well as for managers, to derive conclusions about natural and artificial developers, and engineers who are looking for pieces of the world. Does this approach potential industrial benefits of AI. not provide a systematic and formal way to A decade has passed since the publication of reason about the physical world? What remains three collections of papers and a book covering to be done for AI research in this area?

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. We describe the QUIN program that looks for qualitative patterns in numeric data and outputs the results of learning as "qualitative trees." We illustrate this using applications associated with systems control, in particular, the identification and optimization of controllers and human operator's control skill. We also review approaches that learn models in terms of qualitative differential equations.

We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive qualitative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations. The article then bridges to quantitative modeling, highlighting the benefits of qualitative reasoning-based approaches in the framework of system identification, and discusses open research issues.

Traditional approaches to ecological modeling, based on mathematical equations, are hampered by the qualitative nature of ecological knowledge. In this article, we demonstrate that qualitative reasoning provides alternative and productive ways for ecologists to develop, organize, and implement models. We present a qualitative theory of population dynamics and use this theory to capture and simulate commonsense theories about population and community ecology. Advantages of this approach include the possibility of deriving relevant conclusions about ecological systems without numeric data; a compositional approach that enables the reusability of models representing partial behavior; the use of a rich vocabulary describing objects, situations, relations, and mechanisms of change; and the capability to provide causal interpretations of system behavior.

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.

We argue that qualitative modeling provides a valuable way for students to learn. Two modelbuilding environments, VMODEL and HOMER/- VISIGARP, are presented that support learners by constructing conceptual models of systems and their behavior using qualitative formalisms. Both environments use diagrammatic representations to facilitate knowledge articulation. Preliminary evaluations in educational settings provide support for the hypothesis that qualitative modeling tools can be valuable aids for learning.

Aided Design of User Interfaces. (ICKEDS 2004). "Halpern presents a masterful, complete and unified account of the many ways in which the connections between logic, probability theory and commonsensical linguistic terms can be formalized. 'believed,' 'known,' 'default,' 'relevant,' "Presents a novel thesis--that the mind is a'independent,' and'preferred' are given rigorous program whose components are semantically semantical and syntactical analyses, and their meaningful modules--and explores it with a rich interrelationships explicated and exemplified. An array of evidence drawn from a variety of fields.

Complex electro-mechanical products, such as high-end printers and photocopiers, are designed as families, with reusable modules put together in different manufacturable configurations, and the ability to add new modules in the field. The modules are controlled locally by software that must take into account the entire configuration. This poses two problems for the manufacturer. The first is how to make the overall control architecture adapt to, and use productively, the inclusion of particular modules. The second is to decide, at design time, whether a proposed module is a worthwhile addition to the system: will the resulting system perform enough better to outweigh the costs of including the module? This article indicates how the use of qualitative, constraint-based models provides support for solving both of these problems. This has become an accepted part of the practice of Xerox, and the control software is deployed in high-end Xerox printers.

VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP. It draws from the experience gained in the early to mid 1990's on flaw selection strategies for POCL planning, and combines this with more recent developments in the field of domain independent planning such as distance based heuristics and reachability analysis. We present an adaptation of the additive heuristic for plan space planning, and modify it to account for possible reuse of existing actions in a plan. We also propose a large set of novel flaw selection strategies, and show how these can help us solve more problems than previously possible by POCL planners. VHPOP also supports planning with durative actions by incorporating standard techniques for temporal constraint reasoning. We demonstrate that the same heuristic techniques used to boost the performance of classical POCL planning can be effective in domains with durative actions as well. The result is a versatile heuristic POCL planner competitive with established CSP-based and heuristic state space planners.

TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal logic formulas in order to prune irrelevant parts of the search space. TALplanner recently participated in the third International Planning Competition, which had a clear emphasis on increasing the complexity of the problem domains being used as benchmark tests and the expressivity required to represent these domains in a planning system. Like many other planners, TALplanner had support for some but not all aspects of this increase in expressivity, and a number of changes to the planner were required. After a short introduction to TALplanner, this article describes some of the changes that were made before and during the competition. We also describe the process of introducing suitable domain knowledge for several of the competition domains.