Aysenur Bilgin

Towards a linear general type-2 fuzzy logic based approach for computing with words

Within the last two decades, the paradigm of Computing With Words (CWW) has been gaining more attention. Mainly, CWW has an exciting vision which tries to tackle the problem of human intelligence by taking the human mind as a role model. The human intelligence has been investigated by various disciplines including psychology, philosophy, neuroscience, linguistics, computer science, and cognitive sciences. Notably, it is not a straightforward task to map the human’s brain reasoning into computer processes. In this paper, we propose to facilitate such mapping by investigating a key element, which is to identify the step-by-step formation of perceptual judgments. Herein, we first introduce an approach that employs general type-2 fuzzy logic to dynamically model the human perceptions based on the human experience. This approach can be regarded as a step to enable the CWW vision. We have deployed the proposed approach in real-world settings and we will present two sets of real-world experiments which were conducted in the intelligent apartment (iSpace) in the University of Essex. The first set of experiments demonstrates the results of the proposed approach for the adaptive modeling of ambient luminance perception. In the second set of experiments, we show that our approach performs better in the rule base evaluation processing time and in output accuracy with comparison to an interval type-2 fuzzy logic system.

An experience based linear general type-2 fuzzy logic approach for Computing With Words

In this paper, we present an approach to interpret the Computing With Words (CWWs) paradigm merging the advancements from neuroscience, psychology and artificial intelligence. The presented approach will incorporate fuzzy composite concepts (FCCs), a special case of linguistic weighted average (LWA) and case-based reasoning (CBR). The focus of the paper is on the inception of the CWWs paradigm to bridge the gap between the human and machine intelligence. The investigation of FCCs processing is performed using linear general type-2 (LGT2) and interval type-2 (IT2) fuzzy sets. The results show that LGT2 fuzzy sets outperform IT2 fuzzy sets in the processing time of complete rule base evaluation, in providing better modeling of the human perceptual judgment, and in producing richer range of output intervals.

A general type-2 fuzzy logic approach for adaptive modeling of perceptions for Computing With Words

In a broad sense, the Computing With Words (CWW) vision tries to tackle the problem of human intelligence by taking the human mind as a role model. A key element to facilitate the mapping of the humans brain reasoning into computer processes is to identify the step-by-step formation of perceptual judgments. In this paper, we present an approach that employs general type-2 fuzzy logic which enables the human perceptions to be dynamically modeled and adapted depending on the human experience. This approach can be regarded as a step to enable the CWW vision. We will present real-world experiments which were conducted with real users in the intelligent apartment (iSpace) in the University of Essex. The experiments demonstrate the results of the proposed approach for the adaptive modeling of ambient luminance perception.

A Computing with Words Framework for Ambient Intelligence

One of the challenges for the fast advancing ambient intelligence vision is to maintain the perception of the future home being a safe place where the inhabitants relax, enjoy and feel comfortable. In a home environment, the role of technology is approached skeptically, hence, there is a need to provide high-level communication between the users and the intelligent space so that the users get accustomed to what technology has to offer. In this paper, we introduce a Computing with Words (CWWs) framework which provides human-like reasoning via abstraction and high-level description of thoughts, feelings, etc. This framework can be considered as an exocortex as it aids the human thinking outside the bio-brain. The CWWs paradigm aims to establish high level home-human communication, which is necessary for people to perceive the technology as a cooperative guide and an improvement on their life styles.

Novel Approaches to Artefact Adaptation in Ambient Intelligent Environments

The paper presents advanced novel approaches to develop strategies that will allow the artefacts to adapt to the uncertainties associated with the changes in the artefacts characteristics, context as well as changes in the user(s) preferences regarding these artefacts in Ambient Intelligent Environments (AIEs). This work is within the framework of an EU funded project entitled ATRACO (Adaptive and Trusted Ambient Ecologies) which aims to contribute to the realization of trusted ambient ecologies in AIEs.

Employing an Enhanced Interval Approach to encode words into Linear General Type-2 fuzzy sets for Computing With Words applications

In 1996, Zadeh coined Computing With Words (CWWs) to be a methodology in which words are used instead of numbers for computing and reasoning. One of the main challenges which faced the CWWs paradigm has been modelling words adequately. Mendel has pointed out that the CWWs paradigm should employ type-2 fuzzy logic to model words. This paper proposes employing an Enhanced Interval Approach (EIA) to create Linear General Type-2 (LGT2) fuzzy sets from Interval Type-2 (IT2) fuzzy sets to encode words for CWWs applications. We have performed experiments on 18 words belonging to 3 different linguistic variables (having 6 linguistic terms each). Interval data has been collected from 17 subjects and 18 linguistic terms have been modeled with IT2 fuzzy sets using EIA. The proposed conversion approach uses several key points within the parameters of IT2 fuzzy sets to redesign the linguistic variable using LGT2 fuzzy sets. Both IT2 and LGT2 fuzzy sets have been evaluated within a CWWs Framework, which aims to mimic the ability of humans to communicate and manipulate perceptions via words. The comparison results show that LGT2 fuzzy sets can be better than IT2 fuzzy sets in mimicking human reasoning as well as learning and adaptation since the progressive Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for LGT2 based CWWs Framework converge faster and are lower than those for IT2 based CWWs Framework.

An Ambient Intelligent and Energy Efficient Food Preparation System Using Linear General Type-2 Fuzzy Logic Based Computing with Words Framework [Application Notes]

Ambient Intelligence (AmI) is a multidisciplinary paradigm, which positively alters the relationship between humans and technology. Concerning home environments, the functions of AmI vision include home automation, communication, entertainment, working and learning. In the area of communication, AmI still needs better mechanisms for human-computer communication. A natural human-computer interaction necessitates having systems capable of modelling words and computing with them. For this purpose, the paradigm of Computing With Words (CWWs) can be employed to mimic human-like communication in Ambient Intelligent Environments (AIEs). This paper demonstrates the extendibility of Linear General Type-2 (LGT2) Fuzzy Logic based CWWs Framework to create an advanced real-world application, which integrates a semi-autonomous, safe and energy efficient electric hob. The motivation of this work is twofold: 1) there is a need to develop transparent human-computer communication rather than embedding obtrusive tablets and computing equipment throughout our surroundings, and 2) one of the most hazardous and energy consuming household devices, the electric hob, does not have competent levels of intelligence and energy efficiency. The proposed Ambient Intelligent Food Preparation System (AIFPS) can increase user comfort, facilitate food preparation, minimize energy consumption and be a useful tool for the elderly and people with major disabilities including vision impairment. The results of real-world experiments with various lay users in the intelligent flat (iSpace) show the success of AIFPS in providing up to 55.43% improved natural interaction (compared to Interval Type-2 based CWWs Framework) while achieving semi-autonomous, safe and energy efficient cooking that can save energy between 11.5% and 35.2%.