Self‐adaptation and distributed knowledge‐based service ecosystem evolution

2021 ◽  
Author(s):  
Xianghui Wang ◽  
Zhiyong Feng ◽  
Keman Huang ◽  
Shizhan Chen
Keyword(s):  
Distributed Knowledge ◽  
Knowledge Based ◽  
Ecosystem Evolution ◽  
Self Adaptation ◽  
Service Ecosystem

INDED: a distributed knowledge-based learning system

2000 ◽  
Vol 15 (5) ◽  
pp. 38-46 ◽  
Author(s):  
J. Seitzer ◽  
J.P. Buckley ◽  
Y. Pan
Keyword(s):  
Learning System ◽  
Distributed Knowledge ◽  
Knowledge Based

A MULTILEVEL FUSION APPROACH TO OBJECT IDENTIFICATION IN OUTDOOR ROAD SCENES

1995 ◽  
Vol 09 (01) ◽  
pp. 23-65 ◽  
Author(s):  
VITTORIO MURINO ◽  
CARLO S. REGAZZONI ◽  
GIAN LUCA FORESTI ◽  
GIANNI VERNAZZA
Keyword(s):  
Data Integration ◽  
Environmental Conditions ◽  
Autonomous Vehicle ◽  
A Priori ◽  
Object Identification ◽  
Distributed Knowledge ◽  
Knowledge Based ◽  
Multilevel Fusion ◽  
Fusion Framework ◽  
Fusion Approach

The task of object identification is fundamental to the operations of an autonomous vehicle. It can be accomplished by using techniques based on a Multisensor Fusion framework, which allows the integration of data coming from different sensors. In this paper, an approach to the synergic interpretation of data provided by thermal and visual sensors is proposed. Such integration is justified by the necessity for solving the ambiguities that may arise from separate data interpretations. The architecture of a distributed Knowledge-Based system is described. It performs an Intelligent Data Fusion process by integrating, in an opportunistic way, data acquired with a thermal and a video (b/w) camera. Data integration is performed at various architecture levels in order to increase the robustness of the whole recognition process. A priori models allow the system to obtain interesting data from both sensors; to transform such data into intermediate symbolic objects; and, finally, to recognize environmental situations on which to perform further processing. Some results are reported for different environmental conditions (i.e. a road scene by day and by night, with and without the presence of obstacles).

Value Entropy: A Systematic Evaluation Model of Service Ecosystem Evolution

2020 ◽  
pp. 1-1
Author(s):  
Xiao Xue ◽  
Zhaojie Chen ◽  
Shufang Wang ◽  
Zhiyong Feng ◽  
Yucong Duan ◽  
...  
Keyword(s):  
Evaluation Model ◽  
Systematic Evaluation ◽  
Ecosystem Evolution ◽  
Service Ecosystem

Managing Identity Tensions during Mobile Ecosystem Evolution

2015 ◽  
Vol 30 (3) ◽  
pp. 229-244 ◽  
Author(s):  
Rikard Lindgren ◽  
Owen Eriksson ◽  
Kalle Lyytinen
Keyword(s):  
Service Provider ◽  
Information Service ◽  
Traffic Information ◽  
Future Research ◽  
Longitudinal Case Study ◽  
Digital Service ◽  
Effective Strategies ◽  
Ecosystem Evolution ◽  
Mobile Ecosystem ◽  
Service Ecosystem

The idea of an ecosystem suggests a holistic framing of how heterogeneous actors relate to one another and of the dynamics of their relationships. Because of the dynamics some relationships will become uncertain, posing significant challenge to the identity of participating organizations. Unfortunately, the Information Systems (IS) literature has not examined how organizations develop and negotiate their identities during ecosystem evolution. We fill this void by exploring identity challenges that Swedish Road Administration (SRA) faced while implementing the Radio Data System – Traffic Message Channel (RDS – TMC) traffic information service. Through a longitudinal case study we follow how SRA's inherited expectations, guiding norms, and standards of sense-giving about its identity prevented it from becoming a flexible service provider within an emerging mobile ecosystem. We record a constant clash – the identity tension – between the old inherited identity of a public road administrator and the aspiring new identity of a digital service provider. To enact a successful identity change, SRA had to engage in a series of change episodes whereby it deliberately implemented new routines that forged novel relationships with actors within the ecosystem. This permitted SRA to gradually align its identity to the evolving needs of the RDS-TMC service ecosystem. Our findings suggest that deliberate attempts to implement innovative mobile services – especially those involving public-private partnerships – trigger intriguing identity ambiguities and role dilemmas, and future research should therefore focus on effective strategies to identify, manage, and resolve inherent identity tensions.

KNOWLEDGE-BASED SELF-ADAPTATION IN EVOLUTIONARY SEARCH

2000 ◽  
Vol 14 (01) ◽  
pp. 19-33 ◽  
Author(s):  
CHAN-JIN CHUNG ◽  
ROBERT G. REYNOLDS
Keyword(s):  
Fuzzy Inference ◽  
Population Level ◽  
Cultural Algorithms ◽  
Performance Improvements ◽  
Minimization Problems ◽  
Knowledge Based ◽  
Cultural Framework ◽  
Self Adaptation ◽  
Acceptance Function ◽  
Generalized Knowledge

Self-adaptation has been frequently employed in evolutionary computation. Angeline1 defined three distinct adaptive levels which are: population, individual and component levels. Cultural Algorithms have been shown to provide a framework in which to model self-adaptation at each of these levels. Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the population level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems over population-only evolutionary systems.

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