• Proceedings of the KSPS conference
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• 대한음성학회 2006년도 추계학술대회 발표논문집
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• pp.14-18
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• 2006

The role of dialogue manager is to select proper actions based on observed environment and inferred user intention. This paper presents stochastic model for dialogue manager based on Markov decision process. To build a mixed initiative dialogue manager, we used accumulated user utterance, previous act of dialogue manager, and domain dependent knowledge as the input to the MDP. We also used dialogue corpus to train the automatically optimized policy of MDP with reinforcement learning algorithm. The states which have unique and intuitive actions were removed from the design of MDP by using the domain knowledge. The design of dialogue manager included the usage of natural language understanding and response generator to build short message based remote control of home networked appliances.

• Journal of KIISE:Software and Applications
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• 제34권2호
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• pp.123-130
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• 2007

In this paper, we present an example-based natural language dialogue system for Electronic Program Guide Information Access. We introduce an effective and practical dialogue management technique incorporating dialogue examples and situation-based rules. In order to generate cooperative responses to smoothly lead the dialogue with users, our natural language dialogue system consists of natural language understanding, dialogue manager, system utterance generator. and EPG database manager. Each module is designed and implemented to make an effective and practical natural language dialogue system. In particular, in order to reflect the up-to-date EPG information which is updated frequently and periodically, we applied a web-mining technology to the EPG database manager, which builds the content database based on automatically extracted information from popular EPG websites. The automatically generated content database is used by other modules in the system for building their own resources. Evaluations show that our system performs EPG access task in high performance and can be managed with low cost.

• MALSORI
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• 제56호
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• pp.185-194
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• 2005

In this paper, we present POSSDM (POSTECH Situation-Based Dialogue Manager) for a spoken dialogue system using a new example and situation-based dialogue management technique for effective generation of appropriate system responses. Spoken dialogue system should generate cooperative responses to smoothly control dialogue flow with the users. We introduce a new dialogue management technique incorporating dialogue examples and situation-based rules for EPG (Electronic Program Guide) domain. For the system response inference, we automatically construct and index a dialogue example database from dialogue corpus, and the best dialogue example is retrieved for a proper system response with the query from a dialogue situation including a current user utterance, dialogue act, and discourse history. When dialogue corpus is not enough to cover the domain, we also apply manually constructed situation-based rules mainly for meta-level dialogue management.

• Proceedings of the KSPS conference
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.113-115
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• 2005

In this paper, we present POSSDM (POSTECH Situation-Based Dialogue Manager) for a spoken dialogue system using a new example and situation-based dialogue management techniques for effective generation of appropriate system responses. Spoken dialogue system should generate cooperative responses to smoothly control dialogue flow with the users. We introduce a new dialogue management technique incorporating dialogue examples and situation-based rules for EPG (Electronic Program Guide) domain. For the system response inference, we automatically construct and index a dialogue example database from dialogue corpus, and the best dialogue example is retrieved for a proper system response with the query from a dialogue situation including a current user utterance, dialogue act, and discourse history. When dialogue corpus is not enough to cover the domain, we also apply manually constructed situation-based rules mainly for meta-level dialogue management.

• Korean Journal of Cognitive Science
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• 제19권2호
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• pp.107-123
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• 2008

The existing studies of a dialogue system can be classified into two major parts. One is a study for a practical system, and the other is a study to understand a principal of dialogue phenomena. The former focuses on robustness in real environment for dialogue systems. However, it cannot guarantee its performance in complicated dialogue environment. The latter has studied as the plan-based model typically. It has strong points that it can reflect complex dialogue phenomena and can infer user's intention in various situations. However, an initial design of this model is so complicated, and it is difficult for this model to be extended to the interaction model for response generation in a practical dialogue system. This paper proposes a new dialogue modeling using plan recognition and a discourse stark to effectively generate response in a practical dialogue system.

• Journal of the Korea Society of Computer and Information
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• 제28권1호
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• pp.27-38
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• 2023

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user's characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.

• The Journal of Korean Association of Computer Education
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• 제10권3호
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• pp.67-73
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• 2007

In this paper, we review essential constituents of a dialogue interface system and propose practical methods to implement the each constituent. The implemented system consists of a discourse manager, an intention analyzer, a named entity recognizer, a SQL query generator, and a response generator. In the progress of implementation, the intention analyzer uses a maximum entropy model based on statistics because the domain dependency of the intention analyzer is comparatively low. The others use a simple pattern matching method because they needs high domain portability. In the experiments in a schedule arrangement domain, the implemented system showed the precision of 88.1% in intention analysis and the success rate of 83,4% in SQL query generation.

• Korean Management Science Review
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• 제9권1호
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• pp.53-65
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• 1992

Decision Support System(DSS) is an advanced information system concept prevailing since 1980s. It supports managers' decision making activities by providing not only information but decision alternatives. It essentially consists of the components of database, modelbase and dialogue systems. With the development of its databases, POSCO needs a number of modelbases in establishing DSSs in various areas such as production planning and investment. This research particularly focuses on establishment of a mid-and long-range investment DSS and investigates the necessity and the problems of an investment DSS and the decision criteria for investment priority. We (1) propose a modelbase which uses the concepts of Analytic Hierarchy Process(AHP) and Zero-Base Budgeting(ZBB), along with an appropriate scoring method, database and dialogue system to support the investment manager in evaluating investment proposals ; (2) implement the system using relational database management system ; and (3) discuss the results of implementing the investment DSS.

• Journal of KIISE:Software and Applications
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• 제37권2호
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• pp.129-136
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• 2010

In this paper, we present an Example-based Dialogue System for English conversation tutoring. It aims to provide intelligent one-to-one English conversation tutoring instead of old fashioned language education with static multimedia materials. This system can understand poor expressions of students and it enables green hands to engage in a dialogue in spite of their poor linguistic ability, which gives students interesting motivation to learn a foreign language. And this system also has educational functionalities to improve the linguistic ability. To achieve these goals, we have developed a statistical natural language understanding module for understanding poor expressions and an example-based dialogue manager with high domain scalability and several effective tutoring methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• 제32B권1호
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• pp.161-170
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• 1995

In this paper, we define schematic User Interface Task(SUIT) as a scheme which is a conceptual unit to specify the dialogue between man and machine. Using a set of SUITs, the user interface can be described separately from domain applications and can be realized through SUIT Manager(SUITMAN), an execution module devised to interprete and process the descriptions of SUITs. SUIT makes it possible to describe conceptual behaviors performed on the interactions of user interface in early stages of expert system development. SUITMAN analyzes the specification described in SUITs and automatically implements the user interface by using the functions in MOTIF library of X-Window system. By an example of SUIT and SUITMAN to the user interface, we applied them into an expert system, 'Circuit Provisioning Expert System'.