This paper reports on a computational model developed to study the effects of various modes of social learning on task coordination in teams through the mapping of distributed team competence, a significant aspect of efficient teamwork. The computational model emphasizes and operationalizes distinct modes of social learning, differentiated in terms of socialization opportunities. Simulation results demonstrate that computational models based on fundamental principles of social learning provide a robust approach to study task coordination in teams and can be used to explore ways to organize opportunities for social learning depending upon member retention, team structure, and the complexity of the design task.

Qualitative reasoning can generate ambiguous behaviors due to the lack of quantitative information. Despite many different research results focusing on ambiguities reduction, fundamentally it is impossible to totally remove ambiguities with only qualitative methods and to guarantee the consistency of results. This prevents the wide use of qualitative reasoning techniques in practical situations, particularly in conceptual design, where qualitative reasoning is considered intrinsically useful. To improve this situation, this paper initially investigates the origin of ambiguities in qualitative reasoning. Then it proposes a method based on intelligent interventions of the user who is able to detect ambiguities, to prioritize interventions on these ambiguities, and to reduce ambiguities based on the least commitment strategy. This interaction method breaks through the limit of qualitative reasoning in practical applications to conceptual design. The method was implemented as a new feature in a software tool called the Knowledge Intensive Engineering Framework in order to be tested and used for a printer design.

Configuration of large-scale applications in an engineering context requires a modeling environment that allows the design engineer to draft the configuration problem in a natural way and efficient methods that can process the modeled setting and scale with the number of components. Existing configuration methods in artificial intelligence typically perform quite well in certain subareas but are hard to use for general-purpose modeling without mathematical or logics background (the so-called knowledge acquisition bottleneck) and/or have scalability issues. As a remedy to this important issue both in theory and in practical applications, we use a standard modeling environment like the Unified Modeling Language that has been proposed by the configuration community as a suitable object-oriented formalism for configuration problems. We provide a translation of key concepts of class diagrams to inequalities and identify relevant configuration aspects and show how they are treated as an integer linear program. Solving an integer linear program can be done efficiently, and integer linear programming scales well to large configurations consisting of several thousands components and interactions. We conduct an empirical study in the context of package management for operating systems and for the Linux kernel configuration. We evaluate our methodology by a benchmark and obtain convincing results in support for using integer linear programming for configuration applications of realistic size and complexity.

The competitive market forces companies to offer tailored products to meet specific customer needs. To avoid wasting time, design efforts generally address the configuration of existing solutions, without producing substantial design modifications. Configuration tools are used to achieve customized products starting from a common platform. Many approaches have been successfully proposed in literature to configure products. However, in the mechanical field they need further investigation in order to be efficiently linked to computer-aided design technologies. Research is focused on tools and methods to automatically produce geometrical models and improve the flexibility of the continuous product updating process. In this context, this paper aims to combine product configuration approaches with design automation techniques in order to support design activities of products to fulfill specific requirements. The approach is based on entities called configurable virtual prototypes. Three different domains are managed and connected via configurable virtual prototypes: product specifications, geometrical data, and product knowledge. In particular, geometry recognition rules are used to identify the parameterization of parts and the assembly mating constraints. The approach is exemplified through an industrial case study where a tool has been developed on the basis of the described method. Advantages of the system are shown in terms of achieved product configuration efficiency.

Knowledge representation (KR) is an important area in artificial intelligence (AI) and is often related to specific domains. The representation of knowledge in domain-specific contexts makes it desirable to capture semantics as domain experts would. This motivates the development of semantics-preserving standards for KR within the given domain. In addition to the storage and analysis of information using such standards, the effect of globalization today necessitates the publishing of information on the Web. Thus, it is advisable to use formats that make the information easily publishable and accessible while developing KR standards. In this article, we propose such a standard called Quenching Markup Language (QuenchML). This follows the syntax of the eXtensible Markup Language and captures the semantics of the quenching domain within the heat treating of materials. We describe the development of QuenchML, a multidisciplinary effort spanning the realms of AI, database management, and materials science, considering various aspects such as ontology, data modeling, and domain-specific constraints. We also explain the usefulness of QuenchML in semantics-preserving information retrieval and in text mining guided by domain knowledge. Furthermore, we outline the significance of this work in software tools within the field of AI.