Intelligent User Interface in Fuzzy Environment

2012

The purpose of this chapter is to explore fuzzy logic based methodology for computing an adaptive interface in an environment of imperfect, vague, multimodal, complex nonlinear hyper information space. To this end, based on fuzzy linguistic modelling and fuzzy multi level granulation an adaptation strategy to cognitive/learning styles is presented. The granulated fuzzy if-then rules are utilized to adaptively map cognitive/learning styles of users to their information navigation and presentation preferences through natural language expressions. The important implications of this approach are that, first, uncertain and vague information is handled; second, a mechanism for approximate adaptation at a variety of granulation levels is provided; third, a qualitative linguistic model of adaptation is presented. The proposed approach is close to human reasoning and thereby lowers the cost of solution, and facilitates the design of human computer interaction systems with high level intelligence capability.

Proceedings of the 17th International Conference on Web Information Systems and Technologies, 2021

The fast growth of the internet and communication technology in recent years has resulted in rendering computers easily accessible to everyone. However, people have different knowledge and characteristics that can affect their ability to use computers and at the same time create barriers to achieve an effective user experience. The reason for this is to provide dynamic adaptability to users' individual needs. In view of this compelling need, this paper presents a user-centric system that seeks mainly to improve the interaction of users with the software they use. To achieve this, the system employs fuzzy logic to model the computer knowledge of users and based on this classification, it delivers assistive messages, which are pertinent to the interaction with the system. These messages are tailored to the user groups that have been created, as well as the degree of detail which is more adequate for each group. As a testbed for our research, the presented approach has been incorporated in a learning management system to support tutors towards having a better experience while interacting with this software. The system has been evaluated by users during the COVID-19 lockdown with promising results.

Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces

Decision making theories have been proved to be very successful for evaluating the users' interests and preferences in Intelligent User Interfaces (IUIs). However, their application and incorporation in the reasoning of an IUI requires empirical studies throughout the software life-cycle that lead to requirements analysis and specification, important design decisions and evaluation of the resulting IUI. This paper presents a life-cycle model of how a decision making theory can be applied effectively in an IUI and gives detailed information about the experiments conducted. More specifically, the Simple Additive Weighting (SAW) model has been used as a theory test bed and has been applied in an IUI that is called MBIFM. MBIFM is a file manipulation system that works in a similar way as Windows/NT Explorer. However, the system constantly reasons about every user's action and provides spontaneous advice, in case this is considered necessary.

This paper describes the interface components for a systemcalled Betty's Brain, an intelligent agent we have developed forstudying the learning by teaching paradigm. Our previous studieshave shown that students gain better understanding of domainknowledge when they prepare to teach others versus whenthey prepare to take an exam. This finding has motivated us todevelop computer agents that students teach using concept maprepresentations with a visual interface. Betty is intelligent notbecause she learns on ...

1997

Intelligent user interfaces are characterised by their capability to adapt at run-time and make several communication decisions concerning 'what', 'when', 'why' and 'how' to communicate, through a certain adaptation strategy. In this paper, we present a methodological approach to assist this decision making process, which is based on a clear separation of the important attributes that characterise the adaptation strategy, namely the adaptation determinants, constituents, goals and rules. Based on this separation, we also present a methodological approach for the formulation of adaptation rules, which utilises techniques from the domain of multiple criteria decision making. It is argued that, following the proposed approach, the adaptation strategy can be easily customised to the requirements of different application domains and user groups, and can be re-used with minor modifications in different applications. As a result, developers of intelligent user interfaces can be significantly assisted, and users can be empowered to exploit the benefits of intelligent interfaces.

Virtual Reality, 1993

In the design of a machine which adapts to the user by considering personal differences, this paper considers the interactive relationship that both user and machine employ to try to adapt to each other. This relationship is called the `interactive adaptation'. The interactive adaptive interface (IAI/F) is an intelligent interface which is designed under consideration of the interactive adaptation. This

Lecture Notes in Computer Science, 2007

A method for Information Extraction (IE) in a set of knowledge is proposed in this paper in order to answer to user consultations using natural language. The system is based on a fuzzy logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets can be built in hierarchic levels by a tree structure. A method of consultation based on a fuzzy logic application provided with an interface that one may interact with in natural language is also proposed. The eventual aim of this system is the implementation of an intelligent agent to manage the information contained in an internet portal.

TELKOMNIKA (Telecommunication Computing Electronics and Control), 2014

Every knowledge-based system has its own knowledge formalism depending on the problem that needs solving, goal to be achieved, and proposed solution. This means the knowledge contained in the system will differ from one system to another. This also means that this knowledge cannot be used by another system, which in turn means every system must start with a learning phase right at the beginning. One of the solutions to overcoming this problem is providing a unified model that can accept all types of knowledge, which guarantees automatic interaction between the knowledge-based systems. Interaction in this paper is defined as knowledge sharing, integration, and transfer from one system to another. This research provides a model and conducts a test on interaction capability. It will help to accelerate the establishment of a new knowledge-based system because it does not need knowledge initialization.

2013

Abstract: The most rational mode of communication between intelligent robot and human-operator is bilateral speech dialogue using a problem-oriented language. The dialogue mode of control raises the problem of compliance between the human and robot perception of external world, logic, behavior planning and decision making. We suppose that linguistic variables and fuzzy logic is the most suitable approach for the mentioned problems. The model of the world formed on the base of natural space-time relations allows formulating the basic robot operations in terms of linguistic variables and to solve the problem of planning of robot behavior in previously undetermined environment. Using the fuzzy neural networks it is possible to train robot to fulfill complicated operations by human-operator. Relationship between fuzzy logic approach and the procedures of speech recognition are also under consideration

Generic expert systems are reasoning systems that can be used in many application domains, thus requiring application domain independence. The user interface for a generic expert system must contain an intelligence in order to maintain this domain independence and manage the complex interactions between the user and generic expert system. This paper explores the uncertainty-based reasoning contained in an intelligent user interface called GESIA. GESIA's interface architecture and dynamically constructed Bayesian network are examined in detail to show how uncertainty-based reasoning enhances the capabilities of this generic expert system user interface.