Question-answering systems are widely used nowadays. They allow automating interaction between people. Common solutions include client support, recommendations, and order processing.A question-answering system is a system that answers natural language questions using a structured database or natural language document collection. It can be considered as the development of information retrieval.Three are different services for creating question-answering systems. In this work selected Dialog- flow, IBM Watson Assistant, Microsoft QnA Maker, and LUIS. Each service has its advantages and disadvantages.The main concepts of Dialogflow are agents, intents, entities, context. An agent is a virtual agent with a natural language understanding that handles conversations with users. An intent represents user intention for conversation turn. Each agent has its intents. Entities are parameters of intent that Dialogflow extracts from user expressions. A context in Dialogflow similar to natural language context and helps to control the flow of conversation.The main concepts of IBM Watson Assistant are intents and entities. Intents represent users’ goals. User inputs analyzed to select the most appropriate intent and choose dialog flow. An entity represents information in intent that is relevant to the user’s goal.Microsoft QnA Maker uses a concept of a knowledge base that is filled with information. Requests from the user are analyzed to extract the most appropriate information from the knowledge base. LUIS uses concepts of intent and entities. LUIS uses schemas to extract intent or entity from an input.Important for the choice are the tasks to be solved by the created question-answer system and the volume of system use. Each service has restrictions on the languages that are supported. Dialogflow supports 19 languages, IBM Watson Assistant only 10, Microsoft QnA Maker 52 languages, but LUIS only 17. All services support English, but only Dialogflow and Microsoft QnA Maker support Ukrainian and Russian. .Each service has standard integrations with messengers. All services support Slack and Facebook Messenger. The rest have different support from different services. All services provide options to create a custom integration for non-standard systems if the service does not provide them, through the appropriate APIs.Prices depend on the number of requests per month with a possible limit on the number per minute. Each service offers a free plan under certain conditions, but Microsoft services will require payment for Azure capacity.Manuscript received 09.06.2020

The paper focuses on solving an actual scientific and technical problem of improving the efficiency of solving multiple criteria problems of ranking and selecting alternatives. The process of ranking and selection consists of the following stages: problem identifying, problem structuring, implementation of the optimal choice (decision analysis), post-analysis, and obtaining results. As known, mistakes at the stage of structuring lead to creating an incorrect model of the problem, which is most likely to lead to inaccurate results. Thus, the quality of a solution of the problem depends, first of all, on successful structuring, which requires careful detailing of the problem area for determining the criteria, alternatives, and other information. After ranking the alternatives (objects), post-analysis of the obtained solution is carried out. At this stage, there should be a possibility for an additional research of such objects which did not become “winners”, but taking into account the specifics of the problem, these objects may be interesting for the decision maker. As a result, there is an inverse ranking problem, solution of which should determine how much it is necessary to improve the criteria values of the specified object, which in the final ranking list would occupy the given place. The desired result of solving this problem is determination of a minimum deviation of the initial criterial values which can allow to get the rating which is not below the pre-set value. This article consideres the inverse problem of ranking alternatives, which is formalized in the class of discrete programming models. The algorithm for solving this problem, based on the ideology of the method of dynamic programming, has been proposed. This allows to increase the level of analysis of the obtained results at the post-analysis stage of the process of selecting (ranking) alternatives. Solutions of the inverse problems create additional properties of research objects, which can extend the initial information model area with new properties of research objects. As a result, this increases the efficiency of the decision-making process and the efficiency of the decision-making process.