• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.167-177
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• 2021

The scientific paper studies the impact of tourism and traveling on the economic level of development of countries at the macro level and its relationship with other sectors of the economy. Tourism is one of the budget-forming factors of every economy. This work describes the main trends in the development of tourism. It is determined that about one third of tourism revenues are generated by the food sector, i.e., the culinary niche of tourism. Culinary tourism is a new direction of tourism, but it is developing quite dynamically in the EU. Culinary is an important part of rural tourism in the EU and culinary tourism is actively promoted at fairs and festivals. In recent years rural tourism has been developing both at the international level and in Ukraine, primarily due to its features, which include the implementation of the principles of sustainable community development, preservation of local traditions and cultural values, gastronomic events to promote them. The aim of the article is to study the theoretical aspects of the development of gastronomic tourism in the world, to analyze the actual condition of gastronomic tourism in the EU and Ukraine, identifying prospects and ways to develop regional gastronomic tourism. The methodological and informational basis of the work is analytical reports and researches related to the development of event tourism and statistics. Systematic and logistical approaches to the studied problems were used to achieve this goal. Various general scientific and special research methods were also used. Based on PESTLE analysis, key aspects of the external environment of gastronomic tourism in Ukraine are identified. We took into account the principles of sustainable development: political, economic, social, technological, legal and environmental. The main trends in the development of gastronomic tourism in the world are studied and it is found that the greatest development in the coming years will be the trend of combining gastronomic and event tourism on the basis of sustainable development. The main preconditions and possibilities of introduction of this holistic approach to the strategy of development of the tourist branch of Ukraine are determined. A model of sustainable value chain of gastronomic tourism in the region is formed and the main advantages of its implementation are identified: formation of a regional brand, preservation of culinary traditions, development of green farming, minimization of negative impact on the environment, sustainable development of communities.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.