The Experts below are selected from a list of 15336 Experts worldwide ranked by ideXlab platform

Min Liu - One of the best experts on this subject based on the ideXlab platform.

  • user intention recognition and requirement Elicitation Method for conversational ai services
    arXiv: Artificial Intelligence, 2020
    Co-Authors: Junrui Tian, Zhongjie Wang, Min Liu
    Abstract:

    In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q$\&$A is the most effective way to elicit user requirements. Obviously, complex Q$\&$A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition Method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement Elicitation Method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two Methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.

S Nazneen - One of the best experts on this subject based on the ideXlab platform.

  • ahp_gore_psr applying analytic hierarchy process in goal oriented requirements Elicitation Method for the prioritization of software requirements
    Computational Intelligence, 2017
    Co-Authors: Mohd Sadiq, Tanveer Hassan, S Nazneen
    Abstract:

    Goal Oriented Requirements Engineering (GORE) is concerned with the identification of goals of the software according to the need of the stakeholders. In GORE, goals are the need of the stakeholders. These goals are refined and decomposed into sub-goals until the responsibility of the last goals are assigned to some agent or some software system. In literature different Methods have been developed based on GORE concepts for the identification of software goals or software requirements like fuzzy attributed goal oriented software requirements analysis (FAGOSRA) Method, knowledge acquisition for automated specifications (KAOS), i∗ framework, attributed goal oriented requirements analysis (AGORA) Method, etc. In AGORA, decision makers use subjective values during the selection and the prioritization of software requirements. AGORA Method can be extended by computing the objective values. These objective values can be obtained by using analytic hierarchy process (AHP). In AGORA, there is no support to check whether the values provided by the decision makers are consistent or not. Therefore, in order to address this issue we proposed a Method for the prioritization of software requirements by applying the AHP in goal oriented requirements Elicitation Method. Finally, we consider an example to explain the proposed Method.

Junrui Tian - One of the best experts on this subject based on the ideXlab platform.

  • user intention recognition and requirement Elicitation Method for conversational ai services
    arXiv: Artificial Intelligence, 2020
    Co-Authors: Junrui Tian, Zhongjie Wang, Min Liu
    Abstract:

    In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q$\&$A is the most effective way to elicit user requirements. Obviously, complex Q$\&$A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition Method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement Elicitation Method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two Methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.

Mohd Sadiq - One of the best experts on this subject based on the ideXlab platform.

  • ahp_gore_psr applying analytic hierarchy process in goal oriented requirements Elicitation Method for the prioritization of software requirements
    Computational Intelligence, 2017
    Co-Authors: Mohd Sadiq, Tanveer Hassan, S Nazneen
    Abstract:

    Goal Oriented Requirements Engineering (GORE) is concerned with the identification of goals of the software according to the need of the stakeholders. In GORE, goals are the need of the stakeholders. These goals are refined and decomposed into sub-goals until the responsibility of the last goals are assigned to some agent or some software system. In literature different Methods have been developed based on GORE concepts for the identification of software goals or software requirements like fuzzy attributed goal oriented software requirements analysis (FAGOSRA) Method, knowledge acquisition for automated specifications (KAOS), i∗ framework, attributed goal oriented requirements analysis (AGORA) Method, etc. In AGORA, decision makers use subjective values during the selection and the prioritization of software requirements. AGORA Method can be extended by computing the objective values. These objective values can be obtained by using analytic hierarchy process (AHP). In AGORA, there is no support to check whether the values provided by the decision makers are consistent or not. Therefore, in order to address this issue we proposed a Method for the prioritization of software requirements by applying the AHP in goal oriented requirements Elicitation Method. Finally, we consider an example to explain the proposed Method.

Zhongjie Wang - One of the best experts on this subject based on the ideXlab platform.

  • user intention recognition and requirement Elicitation Method for conversational ai services
    arXiv: Artificial Intelligence, 2020
    Co-Authors: Junrui Tian, Zhongjie Wang, Min Liu
    Abstract:

    In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q$\&$A is the most effective way to elicit user requirements. Obviously, complex Q$\&$A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition Method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement Elicitation Method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two Methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.

  • Crowdsourcing service requirement oriented requirement pattern Elicitation Method
    Neural Computing and Applications, 2019
    Co-Authors: Zhongjie Wang
    Abstract:

    “Pattern” can always help machine to recognize the new encounters, so does “requirement pattern.” Requirement pattern is one of the essences for the cognitive service to understand customer’s intention. Since crowdsourcing service platform holds abundant user demands in the form of text information, the Method proposed in this paper aims at eliciting valuable patterns from this “treasure.” This Method is based on a knowledge graph, which is constructed with the refined concepts of those text information of several different domains. Due to the irregularity and difference of user demand expressions, this paper will firstly explain the knowledge extraction Method for heterogeneous text and the knowledge fusion-based knowledge graph construction Method. Afterward, we will introduce the requirement pattern Elicitation Method based on this knowledge graph. The pattern could either be a frequent demand sequence or a domain-oriented rule or link. Finally, this paper will demonstrate a case study to show how those patterns can help to understand customers’ intention effectively and accurately.