• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.489-496
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• 2009

Hierarchical Task Network(HTN) planning, a typical planning method for effectively taking advantage of domain-specific control knowledge, has been widely used in complex real applications for a long time. However, it still lacks theoretical formalization and standardization, and so there are some differences among existing HTN planners in terms of principle and performance. In this paper, we present an effective way to translate a HTN planning domain specification into the corresponding standard PDDL specification. Its main advantage is to allow even many domain-independent classical planners to utilize domain-specific control knowledge contained in the HTN specifications. In this paper, we try our translation-based approach to three different domains such as Blocks World, Office Delivery, Hanoi Tower, and then conduct some experiments with a forward-chaining heuristic state-space planner, FF, to analyze the efficiency of our approach.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.350-353
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• 2007

복잡하고 다원화되어 있는 재택건강관리 계획 모델링을 계층적 작업 네트워크 계획을 기반으로 설계하여 분산 네트워크의 성능을 최대한으로 활용한 자동화 계획 설계를 제안하였다. 이를 위하여 SHOP라는 계층적 작업 도구를 이용하여 응급, 주의, 비정상, 정상과 같은 4가지 시나리오 모델에 따른 맞춤형 건강관리 계획 설계를 구현하여 재택건강관리 시스템의 상태분류에 대한 보조 의사 결정 도구로써 적용하였다.

• Journal of the Korean Institute of Gas
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• v.21 no.4
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• pp.47-61
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• 2017

The preconditions of the usefulness of an operator safety training model in large plants are the versatility and accuracy of operational procedures, obtained by detailed analysis of the various types of risks associated with the operation, and the systematic representation of knowledge. In this study, we consider the artificial intelligence planning method for the generation of operation procedures; classify them into general actions, actions and technical terms of the operator; and take into account the sharing and reuse of knowledge, defining a knowledge expression ontology. In order to expand and extend the general operations of the operation, we apply a Hierarchical Task Network (HTN). Actual boiler plant case studies are classified according to operating conditions, states and operating objectives between the units, and general emergency shutdown procedures are created to confirm the applicability of the proposed method. These results based on systematic knowledge representation can be easily applied to general plant operation procedures and operator safety training scenarios and will be used for automatic generation of safety training scenarios.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.579-582
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• 2009

With the aid of the increasing interests of Context-aware Augmented Reality Agent (AR Agent), various researches of AR Agent have been performed to explore the possibility of the agent as novel interface and the entity responding autonomously by user's input. However, in previous works, AR Agents are lack of specific method for using various contextual information. To revolve around those problems, we propose the Behavior Generation System for Context-aware AR Agent using layered architecture. Based on Belief-Desire-Intention (BDI) model and Hierarchical Task Network (HTN) searching, the sequence of agent behavior has been selected in behavior planning layer. Then, the agent evaluates appropriateness of behaviors using previous behavior and the type of input before activation. This behavior generation system can be applied for edutainment, game, and assistant agent, which need intuitive and effective behaviors to convey information. Through this research, we expect that the Context-aware AR Agent could support for not only information delivery, but also the capability of effective communication for user.