A Human-Centric Approach to Group-Based Context-Awareness

The emerging need for qualitative approaches in context-aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modeling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendability remains limited. In this paper, we present f-Context as a service-based context-awareness framework, based on language-action perspective (LAP) theory for modeling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems

Computational Modeling of Uncertainty Avoidance in Consumer Behavior

Abstract: Human purchasing behavior is affected by many influential factors. Culture at macro-level and personality at microlevel influence consumer purchasing behavior. People of different cultures tend to accept the values of their own group and consequently have different purchasing behavior. Also, people in the same culture have some differences in their purchases which can be described by their personal characteristics. Therefore, this paper studies Uncertainty Avoidance dimension of Hofstede culture model in consumer behavior as well as four personality traits. The consumer model includes three important module including perception, evaluation of the alternatives and post-purchase. Our experimental results show that people of high uncertainty avoidance tend to purchase the high quality products as well as famous brands to reduce the risk of their purchases. On the other hand, people in high uncertainty tolerant culture tend to purchase the new products. The paper discusses about the validity of the proposed model based on empirical data

Optimizing the Performance and Robustness of Type-2 Fuzzy Group Nearest-Neighbor Queries

In Group Nearest-Neighbor (GNN) queries, the goal is to find one or more points of interest with minimum sum of distance to the current location of mobile users. The classic forms of GNN use Euclidean distance measure which is not sufficient to capture other essential distance perceptions of human and the inherent uncertainty of it. To overcome this problem, an improved distance model can be used which is based on a richer, closer to real-world type-2 fuzzy logic distance model. However, large search spaces as well as the need for higher-order uncertainty management will increase the response times of such GNN queries. In this paper two fuzzy clustering methods combined with spatial tessellation are exploited to reduce the search space. Extensive evaluation of the proposed method shows improved response times compared to naïve method while maintaining a high quality of approximation. The proposed uncertainty management method also provides robustness to movement of mobile users, eliminating the need for full re-computation of candidate clusters when the locations of group members are changed

GREST : a type-2 fuzzy distance model for group nearest-neighbor queries

Collaborative and group-based queries aim to find one or more points in a search space which have the minimum aggregate distance to all members of a group, situated at a set of query points. Current approaches like Group Nearest-Neighbor (GNN) queries are based on single-measure models of distance, like Euclidean distance. In reality, human has a multi-measure perception of distance so that spatial, temporal and economical aspects are important to people with possibly different individual preferences. Current approaches to GNN are unable to handle such distance measures, since it depends on the perceptions and preferences of the users. In this study, we focus on the role of users, as members of a group, situated at GNN query points. An enriched model of distance is introduced which takes the advantage of interval type-2 fuzzy sets to cope with high-order distance uncertainties, emerged from different perceptions of distance by users, and their different preferences. The flexibility of this aggregate model in handling uncertainty enables every member of the group to use a set of group-defined words to express his/her perception of multiple distance types, and to use words instead of numeric values to set the weights for each distance type according to his/her preferences. Our experimental evaluations show that the query results are closer to group preferences by providing higher quality of consensus, while keeping the spatial dispersion of the top-k results at a small level, and improved performance with reasonable response time. The proposed distance model also provides more robustness to changes of mobile member locations, eliminating unnecessary repeated computations

Anger and Aggressive Behavior in Agent Simulation

Abstract Emotions, especially negative ones, have a significant influence on the human performance and intelligent behavior. Anger also is more likely to affect decisionmaking and behaving because anger includes most states of effective emotions, such as stress or fear. Besides, personality has a leading role in affecting the states of emotions in specific situations. The purpose of this paper is simulation of the anger emotion and personality in intelligent agents. To do this, the dimensions of personality related to anger are linked to aggression by a fuzzy expert system as a new way of implementing an intelligent emotional agent. The knowledge base of this expert system contains fuzzy rules obtained from a decision tables and facts obtained from ontology