Acquiring Primary and Secondary Assumptions in BGP-MS

1996

2001

The paper reviews the development of generic user modeling systems over the past twenty years. It describes their purposes, their services within user-adaptive systems, and the different design requirements for research prototypes and commercially deployed servers. It discusses the architectures that have been explored so far, namely shell systems that form part of the application, central server systems that communicate with several applications, and possible future user modeling agents that physically follow the user.

Lecture Notes in Computer Science, 2010

This paper presents an approach for modelling interactions between users and systems in the Unifying Theories of Programming. Working in the predicate calculus, we outline generic techniques for calculating a user's observations of a system and, in turn, for identifying the information that a user can deduce about the system's behaviour from those observations. To demonstrate how this approach can be applied in practical software development, we propose some alternative refinement relations that offer greater flexibility than classical refinement by utilising knowledge of the observational abilities of users.

Automated software customization is drawing increasing attention as a means to help users deal with the scope, complexity, potential intrusiveness, and everchanging nature of modern software. The ability to automatically customize functionality, interfaces, and advice to specific users is made more difficult by the uncertainty about the needs of specific individuals and their preferences for interaction. Following recent probabilistic techniques in user modeling, we model our user with a dynamic Bayesian network (DBN) and propose to explicitly infer the “user’s type” — a composite of personality and affect variables — in real time. We design the system to reason about the impact of its actions given the user’s current state. Our simulations show that user types can be inferred quickly and that a myopic policy offers considerable benefit by adapting to both different types and changing attitudes. We then develop a more realistic user model, using behavioural data from 45 users to lea...

Rough Sets and Current Trends in Computing, 2010

In a multi-agent system the sought information can often be found across various knowledge bases, which means that making early assumptions can lead to hasty conclusions. In the paper we present a formalism for distributed default reasoning to be performed by a group of agents that share knowledge in the form of a distributed default theory. The formalism is based on default transformations, which can be used to derive answers to queries in the form of defaults. Such new defaults can then be treated as intermediate results in the ...

1986

This paper describes a general architecture of a domain independent system for building and maintaining ling term models of individual users..The user modeling system is intended Io provide a well defined set of services for an application system which is interacting with various users and has a need to build and maintain models of them. As the application system interacts with a user, it can acquire knowledge of him and pass that knowledge on 1o the user model maintenance system for incorporation. We describe a prototype general user modeling system (hereafter called GUMSI) which we have implemented in Prolog. This system satisfies some of'the " desirable characteristics we discuss.

International Journal of Intelligent Systems, 1998

Problem-solving methods are means to describe the inference process of knowledge-based systems. During the last years, a number of these problemsolving methods have been identified that can be reused for building new systems. However, problem-solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in reusing problem-solving methods, in acquiring domain knowledge, and in defining the problem that can be tackled by the knowledge-based systems. In the paper, we discuss the different roles, assumptions play in the development process of knowledge-based systems and provide a survey of assumptions used by diagnostic problem solving. We show how such assumptions introduce target and bias for goal-driven machine learning and knowledge discovery techniques.

2012

While designing an architecture, architects often make assumptions about different factors like execution environment, structural properties of the artifacts, properties of input/output data etc. Implicit and invalid assumptions have been identified as a primary reason for architectural mismatches. Such mismatches cause nightmares to the people working at the system integration phase. Today's complex systems operate in dynamic and rapidly changing environments. Implicit assumptions in the reusable components often make it challenging to adopt the components in a changed operational domain. If not documented, assumptions are often forgotten and it is both difficult and expensive to find them out from previously built software. This paper aims to formally capture assumptions at the architecture level so that they can be checked automatically throughout the system development. Automated checking of assumptions would facilitate a practicable assumption management system for complex and large-scale software system development with smooth integration and evolution.

2019

The first edition of the book “Mathematical Approaches to User Modeling” is developed from the PhD dissertation “A User Modeling for Adaptive Learning”. It was accepted on 4th January 2015 by Scientific Research Publishing (SCIRP) and finished on 13rd July 2016 but it is not published yet. Following is the abstract of the book.User model is description of user's information and characteristics in abstract level. User model is very important to adaptive software which aims to support user as much as possible. The process to construct user model is called user modeling. As the title suggests, the book focuses on mathematical approaches to user modeling. The book includes seven main chapters. Chapter I is a survey of user model, user modeling, and adaptive learning. Chapter II introduces the general architecture of the proposed user modeling system Zebra and Triangular Learner Model (TLM). Chapter III, IV, V describes three sub-models of TLM such as knowledge sub-model, learning st...

1986

Abstract This paper describes a general architecture of a domain independent system for building and maintaining long term models of individual users. The user modeling system is intended to provide a well defined set of services for an application system which is interacting with various users and has a need to build and maintain models of them. As the application system interacts with a user, it can acquire knowledge of him and pass that knowledge on to the user model maintenance system for incorporation.