Implicit Acquisition of User Models in Cooperative Advisory Systems

User modelling systems to date have relied heavily on user models that were hand crafted for use in a particular situation. Recently, attention has focused on the feasibility of general user models, models that can be transferred from one situation to another with little or no modification. Such a general user model could be implemented as a modular component easily integrated into diverse systems. This paper addresses one class of general user models, those general with respect to the underlying domain of the application. In particular, a domain independent user modelling module for cooperative advisory systems is discussed. A major problem in building user models is the difficulty of acquiring information about the user. Traditional approaches have relied heavily on information that is pre-encoded by the system designer. For a user model to be domain independent, acquisition of knowledge will have to be done implicitly, i.e., knowledge about the user must be acquired during his interaction with the system. The research proposed in this paper focuses on domain independent implicit user model acquisition techniques for cooperative advisory systems. These techniques have been formalized as a set of model acquisition rules that will serve as the basis for the implementation of the model acquisition portion of a general user modelling module. The acquisition rules have been developed by studying a large number of conversations between advice-seekers and an expert. The rules presented are capable of supporting most of the modelling requirements of the expert in these conversations. Future work includes implementing these acquisition rules in a general user modelling module to test their effectiveness and domain independence

Linguistic and metalinguistic categories in second language learning

This paper discusses proposed characteristics of implicit linguistic and explicit metalinguistic knowledge representations as well as the properties of implicit and explicit processes believed to operate on these representations. In accordance with assumptions made in the usage-based approach to language and language acquisition, it is assumed that implicit linguistic knowledge is represented in terms of flexible and context-dependent categories which are subject to similarity-based processing. It is suggested that, by contrast, explicit metalinguistic knowledge is characterized by stable and discrete Aristotelian categories which subserve conscious, rule-based processing. The consequences of these differences in category structure and processing mechanisms for the usefulness or otherwise of metalinguistic knowledge in second language learning and performance are explored. Reference is made to existing empirical and theoretical research about the role of metalinguistic knowledge in second language acquisition, and specific empirical predictions arising out of the line of argument adopted in the current paper are put forward. © Walter de Gruyter 2008

A hybrid model for capturing implicit spatial knowledge

This paper proposes a machine learning-based approach for capturing rules embedded in users’ movement paths while navigating in Virtual Environments (VEs). It is argued that this methodology and the set of navigational rules which it provides should be regarded as a starting point for designing adaptive VEs able to provide navigation support. This is a major contribution of this work, given that the up-to-date adaptivity for navigable VEs has been primarily delivered through the manipulation of navigational cues with little reference to the user model of navigation

Tools for producing formal specifications : a view of current architectures and future directions

During the last decade, one important contribution towards requirements engineering has been the advent of formal specification languages. They offer a well-defined notation that can improve consistency and avoid ambiguity in specifications. However, the process of obtaining formal specifications that are consistent with the requirements is itself a difficult activity. Hence various researchers are developing systems that aid the transition from informal to formal specifications. The kind of problems tackled and the contributions made by these proposed systems are very diverse. This paper brings these studies together to provide a vision for future architectures that aim to aid the transition from informal to formal specifications. The new architecture, which is based on the strengths of existing studies, tackles a number of key issues in requirements engineering such as identifying ambiguities, incompleteness, and reusability. The paper concludes with a discussion of the research problems that need to be addressed in order to realise the proposed architecture

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD