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Strategic analysis of implementation assets and threats

Piech Henryk, Grodzki Grzegorz

Journal of Applied Mathematics and Computational Mechanics

2019

Vol. 18, nr 2

65--74

The main goal of the work is to support the marketing strategy using the characteristics created on the base of the game theory and uncertain knowledge. We want to elaborate algorithm, which does not require game-playing investigation. The additional aim consists in adaptating the game strategy to the concrete e.g. economic situation, described by selected, specific parameters. The next aim consists in exploitation uncertain knowledge as a data also. Game theory is the part of mathematics approach extended by Nash and adopted to psychology, sociology, politics, economics and informatics (artificial intelligence) problems. Game Theory provides mathematical tools for analyzing situations in which parties, called players, make decisions that are interdependent. This causes each player to consider the other player’s possible decisions, or strategies, in formulating his own strategy. This approach based on the assumption, that a solution to a game describes the optimal decisions of the players, who may have similar, opposed, or mixed interests, and the outcomes that may result from these decisions. This will be described as an example.

Probabilistic and Fuzzy Process Classifiers for Operating Systems Scheduler

Bagchi S.

Fundamenta Informaticae

2016

Vol. 145, nr 4

405--427

The schedulers residing in kernel of Operating Systems employ patterns of resource affinities of concurrent processes in order to make scheduling decisions. The scheduling decisions affect overall resource utilization in a system. Moreover, the resource affinity patterns of a process may not be possible to profile statically in all cases. This paper proposes a novel probabilistic estimation model and a classifier algorithm to queuing processes based on respective resource affinities. The proposed model follows probabilistic estimation using execution traces, which can be either online or statically profiled. The algorithm tracks the resource affinities of processes based on periodic estimation and classifies the processes accordingly for scheduling. The effects of variations of estimation periods are investigated and fuzzy refinements are introduced. Experimental results indicate that the classifier algorithm successfully determines resource affinities of a set of processes online. However, the algorithm can determine inherent affinity pattern of a process in the presence of uniform distribution having enhanced IO frequency.