Interactive granular computing

被引:112
|
作者
Skowron, Andrzej [1 ,2 ]
Jankowski, Andrzej [3 ]
Dutta, Soma [1 ,4 ]
机构
[1] Warsaw Univ, Inst Math, Banacha 2, PL-02097 Warsaw, Poland
[2] Polish Acad Sci, Syst Res Inst, Newelska 6, PL-01447 Warsaw, Poland
[3] Dziubanski Fdn Knowledge Technol, Nowogrodzka 31, PL-00511 Warsaw, Poland
[4] Vistula Univ, Stoklosy 3, PL-02787 Warsaw, Poland
关键词
Rough set; (Interactive) granular computing; Interactive computation; Adaptive judgement; Efficiency management; Risk management; Cost; benefit analysis; Big data technology; Cyber-physical system; Wisdom web of things; Ultra-large system;
D O I
10.1007/s41066-015-0002-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Decision support in solving problems related to complex systems requires relevant computation models for the agents as well as methods for reasoning on properties of computations performed by agents. Agents are performing computations on complex objects [e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, (approximate) reasoning schemes]. In Granular Computing (GrC), all such constructed and/or induced objects are called granules. To model interactive computations performed by agents, crucial for the complex systems, we extend the existing GrC approach to Interactive Granular Computing (IGrC) approach by introducing complex granules (c-granules or granules, for short). Many advanced tasks, concerning complex systems, may be classified as control tasks performed by agents aiming at achieving the high-quality computational trajectories relative to the considered quality measures defined over the trajectories. Here, new challenges are to develop strategies to control, predict, and bound the behavior of the system. We propose to investigate these challenges using the IGrC framework. The reasoning, which aims at controlling of computations, to achieve the required targets, is called an adaptive judgement. This reasoning deals with granules and computations over them. Adaptive judgement is more than a mixture of reasoning based on deduction, induction and abduction. Due to the uncertainty the agents generally cannot predict exactly the results of actions (or plans). Moreover, the approximations of the complex vague concepts initiating actions (or plans) are drifting with time. Hence, adaptive strategies for evolving approximations of concepts are needed. In particular, the adaptive judgement is very much needed in the efficiency management of granular computations, carried out by agents, for risk assessment, risk treatment, and cost/benefit analysis. In the paper, we emphasize the role of the rough set-based methods in IGrC. The discussed approach is a step towards realization of the Wisdom Technology (WisTech) program, and is developed over years, based on the work experience on different real-life projects.
引用
收藏
页码:95 / 113
页数:19
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