In an attempt to automate industrial designing, researchers from Princeton University and Columbia University introduced a large dataset of 15 million two-dimensional real-world computer-aided designs -- SketchGraphs. Along with that to facilitate research in ML-aided design, they also launched an open-source data processing pipeline. Introduced during the International Conference on Machine Learning, SketchGraphs is aimed to train the artificial intelligence machine with this large dataset, in order to expertise it to assist humans in creating CAD models. In a recent paper, researchers revealed that each of the CAD sketches is represented with a geometric constraint graph and the understanding of the line and shape sequence in which the design was initially created. This will enable the predictions of what is going to be designed next.
Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design. Distinguished by relational geometry, parametric CAD models begin as two-dimensional sketches consisting of geometric primitives (e.g., line segments, arcs) and explicit constraints between them (e.g., coincidence, perpendicularity) that form the basis for three-dimensional construction operations. Training machine learning models to reason about and synthesize parametric CAD designs has the potential to reduce design time and enable new design workflows. Additionally, parametric CAD designs can be viewed as instances of constraint programming and they offer a well-scoped test bed for exploring ideas in program synthesis and induction. To facilitate this research, we introduce SketchGraphs, a collection of 15 million sketches extracted from real-world CAD models coupled with an open-source data processing pipeline.
Roughly 30 people attended this workshop. This article summarizes the papers presented at the workshop and highlights some of the questions and issues raised during the discussion. The task of selecting the best representation for solving a problem by means of automatically reformulating the problem is a core challenge in AI. Saul Amarel (1968) outlined the impact of different representations of the missionaries and cannibals problem on the performance of the algorithms used to solve the problem. He also proposed automating the reformulation process and, consequently, the process of selecting representations: The general problem of representation is concerned with the relationship between different ways of formulating a problem to a problem solving system and the efficiency with which the system can be expected to find a solution to the problem.
The symposium took place in July 2009 in Lake Arrowhead, California. Consequently, ARA techniques have been studied in various subfields in AI and related disciplines and have been used in various settings including automated reasoning, cognitive modeling, constraint programming, design, diagnosis, machine learning, model-based reasoning, planning, reasoning, scheduling, search, theorem proving, and intelligent tutoring. The considerable interest in ARA techniques and the great diversity of the researchers involved had led to work on ARA being presented at many different venues. Consequently, there was a need to have a single forum where researchers of different backgrounds and disciplines could discuss their work on ARA. As a result, the Symposium on Abstraction, Reformulation, and Approximation (SARA) was established in 1994 after a series of workshops in 1988, 1990, and 1992.
You wait forever for one to come along, and then two come along at once. In this case, there has been a large gap in the market for a theoretical introduction to constraint programming ever since Edward Tsang's Foundations of Constraint Satisfaction (1993) went out of print. Therefore, we are very pleased to see two books written by two of the leading researchers in this field come along to fill the gap. Constraint programming is a very active research area within AI. It is a highly successful technology for solving a wide range of combinatorial problems, including scheduling, rostering, assignment, routing, and design. A number of companies, like ILOG, Dash Optimization, and Parc Technologies, market model building and constraint programming toolkits, which are used by companies as diverse as Amazon.com, Constraint programming is a declarative style of modeling combinatorial problems in which the user identifies the decision variables, their possible domain of values, and specifies constraints over the allowed values (for example, no two of these variables can take the same value). Sophisticated but general purpose AI search techniques like constraint propagation (to prune irrelevant parts of the search tree) and dependency directed backtracking can then be used to find solutions. Given the many advances made in constraint programming over the last decade, a new text would have been needed even if Edward Tsang's book had remained in print. These two new texts are written by two of the leading researchers in this field. Principles of Constraint Programming by Krzysztof Apt contains chapters that cover topics like local consistency, constraint propagation, linear equations, interval reasoning, and search. Constraint Processing by Rina Dechter covers similar ground but also has chapters that cover topics like local search, tree decomposition methods, optimization, and probabilistic networks more extensively. Dechter's book also contains a chapter by David Cohen and Peter Jeavons on tractability and one by Francesca Rossi on constraint logic programming. There is much in common between the two books. This is perhaps not so surprising since Krzysztof Apt thanks Rina Dechter for much useful discussion that helped him enter the field and start doing research in the area.
Complex electromechanical 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?
The SAT Conference on Theory and Applications of Satisfiability Testing was held in Lisbon, Portugal, 28-31 May 2007. The conference, which attracted a record-breaking 80 participants, featured 34 papers and two invited presentations. The venue also included the SAT competition, the QBF evaluation, the PB evaluation, and the MAX-SAT evaluation. Moreover, SAT and extensions of SAT find many practical applications, including planning, software and hardware model checking, bioinformatics, equivalence check ing, test-pattern generation, software package installation, and cryptography. The annual SAT conference is now widely recognized as "the venue" for AI Magazine Volume 28 Number 4 (2007) ( AAAI) This year marked the tenth SAT meeting.
This problem is particularly pronounced for operations planners and controllers, who must be very highly knowledgeable and experienced with the business domain. This article is a case study of how one of the largest travel agencies in Hong Kong alleviated this problem by using AI to support decision making and problem solving so that its planners and controllers can work more effectively and efficiently to sustain business growth while maintaining consistent quality of service. AI is used in a mission-critical fleet management system (FMS) that supports the scheduling and management of a fleet of luxury limousines for business travelers. The AI problem was modeled as a constraint-satisfaction problem (CSP). The use of AI enabled the travel agency to sign up additional hotel partners, handle more orders, and expand its fleet with its existing team of planners and controllers.
In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are repre sented in the form of a configuration knowledge base that de - scribes the properties of allowed instances. Although the knowledge representation used is different compared to nonconfi gurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research. Similar to knowledge-based recommendation (Burke 2000) configuration is a process where users specify (and often adapt) their requirements and the configuration system provides feedback. Requirements specifications range from feature value definitions to textual queries specified on an informal level. Feedback is provided, for example, in terms of further questions that need to be answered, solutions (configurations), explanations of solutions, and proposals for relaxations of the user requirements in situations where no solution can be found. A major difference between configuration systems and recommender systems in general is the way in which product knowledge is represented. Configuration systems are operating on a configuration knowledge base (Stumptner 1997), which describes the properties of all allowed instances. In contrast to configuration systems, recommender systems are operating on the basis of an assortment of explicitly defined solution alternatives. The reason for using a configuration knowledge base is the large number of solution alternatives (possible configurations), which make an explicit representation infeasible. Although the used knowledge representations are different, the decision support goal is quite the same for both types of systems: users have to be proactively supported in finding a solution that fits their wishes and needs. Configuration systems often achieve this goal only partially since the amount and complexity of options presented by the configurator outstrip the capability of a user to identify an appropriate solution (configuration). Users are unable to find the features they would like to specify, they are unsure about their preferences regarding complex technical product properties, and they do not know how best to adapt their requirements in the case of inconsistencies (if no solution can be identified).